



'''simple docstring'''






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 				lowercase			(    _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    UnCLIPPipeline
 UpperCAmelCase		      =    TEXT_TO_IMAGE_PARAMS - {
     """negative_prompt""",
     """height""",
     """width""",
     """negative_prompt_embeds""",
     """guidance_scale""",
     """prompt_embeds""",
     """cross_attention_kwargs""",
 }
 UpperCAmelCase		      =    TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase		      =    [
     """generator""",
     """return_dict""",
     """prior_num_inference_steps""",
     """decoder_num_inference_steps""",
     """super_res_num_inference_steps""",
 ]
 UpperCAmelCase		      =    False







 @property
 def _snake_case					(				self				)						->  List[Any]:
        return 32







 @property
 def _snake_case					(				self				)						->  List[Any]:
        return 32







 @property
 def _snake_case					(				self				)						->  Any:
        return self.time_input_dim







 @property
 def _snake_case					(				self				)						->  List[str]:
        return self.time_input_dim * 4







 @property
 def _snake_case					(				self				)						->  Optional[Any]:
        return 100







 @property
 def _snake_case					(				self				)						->  Any:
        _UpperCAmelCase    :		Dict     	=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)
        return tokenizer







 @property
 def _snake_case					(				self				)						->  Optional[Any]:
        torch.manual_seed(0				)
        _UpperCAmelCase    :		int     	=					CLIPTextConfig(
            bos_token_id=0    ,eos_token_id=2    ,hidden_size=self.text_embedder_hidden_size    ,projection_dim=self.text_embedder_hidden_size    ,intermediate_size=37    ,layer_norm_eps=1E-0_5    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1_000    ,)
        return CLIPTextModelWithProjection(a_				)







 @property
 def _snake_case					(				self				)						->  str:
        torch.manual_seed(0				)

        _UpperCAmelCase    :		Optional[int]     	=					{
            """num_attention_heads""": 2,
            """attention_head_dim""": 12,
            """embedding_dim""": self.text_embedder_hidden_size,
            """num_layers""": 1,
        }

        _UpperCAmelCase    :		Any     	=					PriorTransformer(**a_				)
        return model







 @property
 def _snake_case					(				self				)						->  Tuple:
        torch.manual_seed(0				)

        _UpperCAmelCase    :		List[str]     	=					{
            """clip_embeddings_dim""": self.text_embedder_hidden_size,
            """time_embed_dim""": self.time_embed_dim,
            """cross_attention_dim""": self.cross_attention_dim,
        }

        _UpperCAmelCase    :		Optional[Any]     	=					UnCLIPTextProjModel(**a_				)
        return model







 @property
 def _snake_case					(				self				)						->  Optional[Any]:
        torch.manual_seed(0				)

        _UpperCAmelCase    :		Optional[int]     	=					{
            """sample_size""": 32,
            # RGB in channels
            """in_channels""": 3,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 6,
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": """identity""",
        }

        _UpperCAmelCase    :		Union[str, Any]     	=					UNetaDConditionModel(**a_				)
        return model







 @property
 def _snake_case					(				self				)						->  str:
        return {
            "sample_size": 64,
            "layers_per_block": 1,
            "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
            "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "in_channels": 6,
            "out_channels": 3,
        }







 @property
 def _snake_case					(				self				)						->  List[Any]:
        torch.manual_seed(0				)

        _UpperCAmelCase    :		Any     	=					UNetaDModel(**self.dummy_super_res_kwargs				)
        return model







 @property
 def _snake_case					(				self				)						->  str:
        # seeded differently to get different unet than `self.dummy_super_res_first`
        torch.manual_seed(1				)

        _UpperCAmelCase    :		Optional[int]     	=					UNetaDModel(**self.dummy_super_res_kwargs				)
        return model







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Tuple     	=					self.dummy_prior
        _UpperCAmelCase    :		str     	=					self.dummy_decoder
        _UpperCAmelCase    :		Optional[int]     	=					self.dummy_text_proj
        _UpperCAmelCase    :		Tuple     	=					self.dummy_text_encoder
        _UpperCAmelCase    :		Any     	=					self.dummy_tokenizer
        _UpperCAmelCase    :		List[Any]     	=					self.dummy_super_res_first
        _UpperCAmelCase    :		Tuple     	=					self.dummy_super_res_last

        _UpperCAmelCase    :		Union[str, Any]     	=					UnCLIPScheduler(
            variance_type="""fixed_small_log"""    ,prediction_type="""sample"""    ,num_train_timesteps=1_000    ,clip_sample_range=5.0    ,)

        _UpperCAmelCase    :		Union[str, Any]     	=					UnCLIPScheduler(
            variance_type="""learned_range"""    ,prediction_type="""epsilon"""    ,num_train_timesteps=1_000    ,)

        _UpperCAmelCase    :		int     	=					UnCLIPScheduler(
            variance_type="""fixed_small_log"""    ,prediction_type="""epsilon"""    ,num_train_timesteps=1_000    ,)

        _UpperCAmelCase    :		List[Any]     	=					{
            """prior""": prior,
            """decoder""": decoder,
            """text_proj""": text_proj,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """super_res_first""": super_res_first,
            """super_res_last""": super_res_last,
            """prior_scheduler""": prior_scheduler,
            """decoder_scheduler""": decoder_scheduler,
            """super_res_scheduler""": super_res_scheduler,
        }

        return components







 def _snake_case					(				self    ,a_    ,a_=0				)						->  List[Any]:
        if str(a_				).startswith("""mps"""				):
               _UpperCAmelCase    :		int     	=					torch.manual_seed(a_				)
        else:
               _UpperCAmelCase    :		List[str]     	=					torch.Generator(device=a_				).manual_seed(a_				)
        _UpperCAmelCase    :		Any     	=					{
            """prompt""": """horse""",
            """generator""": generator,
            """prior_num_inference_steps""": 2,
            """decoder_num_inference_steps""": 2,
            """super_res_num_inference_steps""": 2,
            """output_type""": """numpy""",
        }
        return inputs







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Optional[int]     	=					"""cpu"""

        _UpperCAmelCase    :		List[Any]     	=					self.get_dummy_components()

        _UpperCAmelCase    :		List[Any]     	=					self.pipeline_class(**a_				)
        _UpperCAmelCase    :		Union[str, Any]     	=					pipe.to(a_				)

        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Optional[int]     	=					pipe(**self.get_dummy_inputs(a_				)				)
        _UpperCAmelCase    :		List[Any]     	=					output.images

        _UpperCAmelCase    :		int     	=					pipe(
            **self.get_dummy_inputs(a_				)    ,return_dict=a_    ,)[0]

        _UpperCAmelCase    :		List[Any]     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		Optional[int]     	=					image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        _UpperCAmelCase    :		Any     	=					np.array(
            [
                0.9997,
                0.9988,
                0.0028,
                0.9997,
                0.9984,
                0.9965,
                0.0029,
                0.9986,
                0.0025,
            ]				)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		Optional[Any]     	=					torch.device("""cpu"""				)



        class 				lowercase			:


         """simple docstring"""


         UpperCAmelCase		      =    1







        _UpperCAmelCase    :		List[Any]     	=					self.get_dummy_components()

        _UpperCAmelCase    :		Tuple     	=					self.pipeline_class(**a_				)
        _UpperCAmelCase    :		Any     	=					pipe.to(a_				)

        _UpperCAmelCase    :		int     	=					components["""prior"""]
        _UpperCAmelCase    :		Optional[Any]     	=					components["""decoder"""]
        _UpperCAmelCase    :		str     	=					components["""super_res_first"""]
        _UpperCAmelCase    :		Any     	=					components["""tokenizer"""]
        _UpperCAmelCase    :		Union[str, Any]     	=					components["""text_encoder"""]

        _UpperCAmelCase    :		Tuple     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		Optional[Any]     	=					prior.dtype
        _UpperCAmelCase    :		Tuple     	=					1

        _UpperCAmelCase    :		str     	=					(batch_size, prior.config.embedding_dim)
        _UpperCAmelCase    :		Union[str, Any]     	=					pipe.prepare_latents(
            a_    ,dtype=a_    ,device=a_    ,generator=a_    ,latents=a_    ,scheduler=DummyScheduler()				)
        _UpperCAmelCase    :		List[str]     	=					(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
        _UpperCAmelCase    :		List[str]     	=					pipe.prepare_latents(
            a_    ,dtype=a_    ,device=a_    ,generator=a_    ,latents=a_    ,scheduler=DummyScheduler()				)

        _UpperCAmelCase    :		List[Any]     	=					(
            batch_size,
            super_res_first.config.in_channels // 2,
            super_res_first.config.sample_size,
            super_res_first.config.sample_size,
        )
        _UpperCAmelCase    :		List[Any]     	=					pipe.prepare_latents(
            a_    ,dtype=a_    ,device=a_    ,generator=a_    ,latents=a_    ,scheduler=DummyScheduler()				)

        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Optional[Any]     	=					"""this is a prompt example"""

        _UpperCAmelCase    :		Optional[Any]     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		List[str]     	=					pipe(
            [prompt]    ,generator=a_    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,prior_latents=a_    ,decoder_latents=a_    ,super_res_latents=a_    ,output_type="""np"""    ,)
        _UpperCAmelCase    :		List[str]     	=					output.images

        _UpperCAmelCase    :		Dict     	=					tokenizer(
            a_    ,padding="""max_length"""    ,max_length=tokenizer.model_max_length    ,return_tensors="""pt"""    ,)
        _UpperCAmelCase    :		Optional[int]     	=					text_encoder(text_inputs.input_ids				)
        _UpperCAmelCase    :		int     	=					text_inputs.attention_mask

        _UpperCAmelCase    :		Optional[int]     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		str     	=					pipe(
            generator=a_    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,prior_latents=a_    ,decoder_latents=a_    ,super_res_latents=a_    ,text_model_output=a_    ,text_attention_mask=a_    ,output_type="""np"""    ,)[0]

        # make sure passing text embeddings manually is identical
        assert np.abs(image - image_from_text				).max() < 1E-4







 @skip_mps
 def _snake_case					(				self				)						->  str:
        _UpperCAmelCase    :		str     	=					torch_device == """cpu"""

        self._test_attention_slicing_forward_pass(test_max_difference=a_    ,expected_max_diff=0.01				)







 @skip_mps
 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase    :		Any     	=					torch_device == """cpu"""
        _UpperCAmelCase    :		Any     	=					True
        _UpperCAmelCase    :		Any     	=					[
            """prior_num_inference_steps""",
            """decoder_num_inference_steps""",
            """super_res_num_inference_steps""",
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=a_    ,relax_max_difference=a_    ,additional_params_copy_to_batched_inputs=a_    ,)







 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase    :		Optional[Any]     	=					[
            """prior_num_inference_steps""",
            """decoder_num_inference_steps""",
            """super_res_num_inference_steps""",
        ]

        if torch_device == "mps":
               # TODO: MPS errors with larger batch sizes
               _UpperCAmelCase    :		List[Any]     	=					[2, 3]
               self._test_inference_batch_consistent(
                   batch_sizes=a_    ,additional_params_copy_to_batched_inputs=a_    ,)
        else:
               self._test_inference_batch_consistent(
                   additional_params_copy_to_batched_inputs=a_				)







 @skip_mps
 def _snake_case					(				self				)						->  Any:
        return super().test_dict_tuple_outputs_equivalent()







 @skip_mps
 def _snake_case					(				self				)						->  List[Any]:
        return super().test_save_load_local()







 @skip_mps
 def _snake_case					(				self				)						->  Optional[Any]:
        return super().test_save_load_optional_components()







@nightly
class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def _snake_case					(				self				)						->  Any:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







 def _snake_case					(				self				)						->  Tuple:
        _UpperCAmelCase    :		List[str]     	=					load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/unclip/karlo_v1_alpha_horse_cpu.npy"""				)

        _UpperCAmelCase    :		Tuple     	=					UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""				)
        pipeline.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Any     	=					torch.manual_seed(0				)
        _UpperCAmelCase    :		List[str]     	=					pipeline(
            """horse"""    ,num_images_per_prompt=1    ,generator=a_    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Union[str, Any]     	=					output.images[0]

        assert image.shape == (256, 256, 3)
        assert np.abs(expected_image - image				).max() < 1E-1







@slow
@require_torch_gpu
class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def _snake_case					(				self				)						->  Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		List[Any]     	=					load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/unclip/karlo_v1_alpha_horse_fp16.npy"""				)

        _UpperCAmelCase    :		List[str]     	=					UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""    ,torch_dtype=torch.floataa				)
        _UpperCAmelCase    :		str     	=					pipeline.to(a_				)
        pipeline.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Optional[int]     	=					torch.Generator(device="""cpu"""				).manual_seed(0				)
        _UpperCAmelCase    :		int     	=					pipeline(
            """horse"""    ,generator=a_    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Optional[Any]     	=					output.images[0]

        assert image.shape == (256, 256, 3)

        assert_mean_pixel_difference(a_    ,a_				)







 def _snake_case					(				self				)						->  Optional[Any]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _UpperCAmelCase    :		Union[str, Any]     	=					UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""    ,torch_dtype=torch.floataa				)
        _UpperCAmelCase    :		str     	=					pipe.to(a_				)
        pipe.set_progress_bar_config(disable=a_				)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        _UpperCAmelCase    :		Union[str, Any]     	=					pipe(
            """horse"""    ,num_images_per_prompt=1    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Optional[int]     	=					torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9










import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          FunnelBaseModel,
          FunnelForMaskedLM,
          FunnelForMultipleChoice,
          FunnelForPreTraining,
          FunnelForQuestionAnswering,
          FunnelForSequenceClassification,
          FunnelForTokenClassification,
          FunnelModel,
      )







class        __snake_case			:





       '''simple docstring'''







       def __init__(    self					:       str				,							A					:       int				,							A					:       List[Any]=13				,							A					:       Dict=7				,							A					:       Optional[int]=True				,							A					:       Dict=True				,							A					:       str=True				,							A					:       Union[str, Any]=True				,							A					:       Union[str, Any]=99				,							A					:       Dict=[1, 1, 2]				,							A					:       List[Any]=1				,							A					:       List[str]=32				,							A					:       Dict=4				,							A					:       Tuple=8				,							A					:       List[Any]=37				,							A					:       Dict="gelu_new"				,							A					:       Dict=0.1				,							A					:       List[Any]=0.1				,							A					:       Optional[int]=0.0				,							A					:       Tuple=512				,							A					:       Tuple=3				,							A					:       List[str]=0.02				,							A					:       str=3				,							A					:       Optional[Any]=4				,							A					:       str=None				,							A					:       Union[str, Any]=False				,							):
         __snake_case:		int					    =		parent
         __snake_case:		Optional[Any]					    =		batch_size
         __snake_case:		Optional[Any]					    =		seq_length
         __snake_case:		List[Any]					    =		is_training
         __snake_case:		List[str]					    =		use_input_mask
         __snake_case:		Any					    =		use_token_type_ids
         __snake_case:		Union[str, Any]					    =		use_labels
         __snake_case:		Any					    =		vocab_size
         __snake_case:		int					    =		block_sizes
         __snake_case:		Optional[int]					    =		num_decoder_layers
         __snake_case:		Union[str, Any]					    =		d_model
         __snake_case:		List[str]					    =		n_head
         __snake_case:		Optional[Any]					    =		d_head
         __snake_case:		str					    =		d_inner
         __snake_case:		Optional[Any]					    =		hidden_act
         __snake_case:		List[Any]					    =		hidden_dropout
         __snake_case:		List[Any]					    =		attention_dropout
         __snake_case:		Tuple					    =		activation_dropout
         __snake_case:		Optional[int]					    =		max_position_embeddings
         __snake_case:		Optional[int]					    =		type_vocab_size
         __snake_case:		Any					    =		2
         __snake_case:		Dict					    =		num_labels
         __snake_case:		List[Any]					    =		num_choices
         __snake_case:		Any					    =		scope
         __snake_case:		Optional[Any]					    =		initializer_std

         # Used in the tests to check the size of the first attention layer
         __snake_case:		Union[str, Any]					    =		n_head
         # Used in the tests to check the size of the first hidden state
         __snake_case:		Union[str, Any]					    =		self.d_model
         # Used in the tests to check the number of output hidden states/attentions
         __snake_case:		Optional[int]					    =		sum(self.block_sizes						) + (0 if base else self.num_decoder_layers)
         # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
         # the last hidden state of the first block (which is the first hidden state of the decoder).
         if not base:
           __snake_case:		Any					    =		self.num_hidden_layers + 2




       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		Tuple					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size						)

         __snake_case:		Optional[int]					    =		None
         if self.use_input_mask:
           __snake_case:		Optional[Any]					    =		ids_tensor([self.batch_size, self.seq_length]				,							vocab_size=2						)

         __snake_case:		Dict					    =		None
         if self.use_token_type_ids:
           __snake_case:		List[str]					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.type_vocab_size						)

         __snake_case:		Optional[Any]					    =		None
         __snake_case:		Optional[int]					    =		None
         __snake_case:		Any					    =		None
         if self.use_labels:
           __snake_case:		List[Any]					    =		ids_tensor([self.batch_size]				,							self.type_sequence_label_size						)
           __snake_case:		Any					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.num_labels						)
           __snake_case:		Union[str, Any]					    =		ids_tensor([self.batch_size]				,							self.num_choices						)
           __snake_case:		Tuple					    =		ids_tensor([self.batch_size, self.seq_length]				,							1						)

         __snake_case:		Dict					    =		self.get_config()

         return (
             config,
             input_ids,
             token_type_ids,
             input_mask,
             sequence_labels,
             token_labels,
             choice_labels,
             fake_token_labels,
         )




       def 				UpperCAmelCase__					(    self					:       Dict						):
         return FunnelConfig(
             vocab_size=self.vocab_size				,							block_sizes=self.block_sizes				,							num_decoder_layers=self.num_decoder_layers				,							d_model=self.d_model				,							n_head=self.n_head				,							d_head=self.d_head				,							d_inner=self.d_inner				,							hidden_act=self.hidden_act				,							hidden_dropout=self.hidden_dropout				,							attention_dropout=self.attention_dropout				,							activation_dropout=self.activation_dropout				,							max_position_embeddings=self.max_position_embeddings				,							type_vocab_size=self.type_vocab_size				,							initializer_std=self.initializer_std				,							)




       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       Tuple				,							A					:       Any				,							A					:       int				,							A					:       Optional[Any]				,							A					:       List[str]				,							A					:       Optional[int]				,							A					:       Tuple				,							A					:       Optional[Any]				,							):
         __snake_case:		int					    =		FunnelModel(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Union[str, Any]					    =		model(A				,							attention_mask=A				,							token_type_ids=A						)
         __snake_case:		Dict					    =		model(A				,							token_type_ids=A						)
         __snake_case:		Optional[int]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.d_model)						)

         __snake_case:		Any					    =		False
         __snake_case:		List[Any]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.d_model)						)

         __snake_case:		List[str]					    =		False
         __snake_case:		Optional[int]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.d_model)						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]				,							A					:       Union[str, Any]				,							A					:       str				,							A					:       Any				,							A					:       str				,							A					:       Any				,							A					:       str				,							A					:       List[str]				,							A					:       Dict				,							):
         __snake_case:		Dict					    =		FunnelBaseModel(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		List[str]					    =		model(A				,							attention_mask=A				,							token_type_ids=A						)
         __snake_case:		Union[str, Any]					    =		model(A				,							token_type_ids=A						)
         __snake_case:		List[Any]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, 2, self.d_model)						)

         __snake_case:		Optional[Any]					    =		False
         __snake_case:		Tuple					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, 3, self.d_model)						)

         __snake_case:		Union[str, Any]					    =		False
         __snake_case:		List[Any]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, 2, self.d_model)						)




       def 				UpperCAmelCase__					(    self					:       Tuple				,							A					:       Union[str, Any]				,							A					:       Optional[int]				,							A					:       Optional[int]				,							A					:       Any				,							A					:       str				,							A					:       Optional[int]				,							A					:       Dict				,							A					:       str				,							):
         __snake_case:		Optional[int]					    =		self.num_labels
         __snake_case:		Optional[Any]					    =		FunnelForPreTraining(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Dict					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length)						)




       def 				UpperCAmelCase__					(    self					:       List[str]				,							A					:       Optional[int]				,							A					:       List[str]				,							A					:       Tuple				,							A					:       Any				,							A					:       str				,							A					:       Optional[Any]				,							A					:       Optional[Any]				,							A					:       Union[str, Any]				,							):
         __snake_case:		List[str]					    =		FunnelForMaskedLM(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Any					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size)						)




       def 				UpperCAmelCase__					(    self					:       Dict				,							A					:       str				,							A					:       Dict				,							A					:       List[str]				,							A					:       Any				,							A					:       Optional[Any]				,							A					:       Optional[Any]				,							A					:       Optional[Any]				,							A					:       Any				,							):
         __snake_case:		Dict					    =		self.num_labels
         __snake_case:		Dict					    =		FunnelForSequenceClassification(A						)
         model.to(A						)
         model.eval()
         __snake_case:		str					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)						)




       def 				UpperCAmelCase__					(    self					:       List[str]				,							A					:       Optional[int]				,							A					:       str				,							A					:       Optional[int]				,							A					:       str				,							A					:       Tuple				,							A					:       int				,							A					:       int				,							A					:       Dict				,							):
         __snake_case:		List[Any]					    =		self.num_choices
         __snake_case:		Optional[Any]					    =		FunnelForMultipleChoice(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		int					    =		input_ids.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		Tuple					    =		token_type_ids.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		List[str]					    =		input_mask.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		Tuple					    =		model(
             A				,							attention_mask=A				,							token_type_ids=A				,							labels=A				,							)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_choices)						)




       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       Optional[int]				,							A					:       str				,							A					:       Union[str, Any]				,							A					:       Optional[int]				,							A					:       Union[str, Any]				,							A					:       Optional[int]				,							A					:       Union[str, Any]				,							A					:       Union[str, Any]				,							):
         __snake_case:		Dict					    =		self.num_labels
         __snake_case:		List[Any]					    =		FunnelForTokenClassification(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Tuple					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.num_labels)						)




       def 				UpperCAmelCase__					(    self					:       Dict				,							A					:       Optional[Any]				,							A					:       int				,							A					:       Dict				,							A					:       Optional[int]				,							A					:       Tuple				,							A					:       str				,							A					:       List[Any]				,							A					:       Optional[Any]				,							):
         __snake_case:		List[str]					    =		FunnelForQuestionAnswering(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		str					    =		model(
             A				,							attention_mask=A				,							token_type_ids=A				,							start_positions=A				,							end_positions=A				,							)
         self.parent.assertEqual(result.start_logits.shape				,							(self.batch_size, self.seq_length)						)
         self.parent.assertEqual(result.end_logits.shape				,							(self.batch_size, self.seq_length)						)






       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		Optional[int]					    =		self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  
         ):		Dict					    =		config_and_inputs
         __snake_case:		Union[str, Any]					    =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict







@require_torch
class        __snake_case			(				__lowerCamelCase  ,     __lowerCamelCase  ,     unittest.TestCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	(
           (
               FunnelModel,
               FunnelForMaskedLM,
               FunnelForPreTraining,
               FunnelForQuestionAnswering,
               FunnelForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__ 						=	(
           {
               """feature-extraction""": (FunnelBaseModel, FunnelModel),
               """fill-mask""": FunnelForMaskedLM,
               """question-answering""": FunnelForQuestionAnswering,
               """text-classification""": FunnelForSequenceClassification,
               """token-classification""": FunnelForTokenClassification,
               """zero-shot""": FunnelForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )




       def 				UpperCAmelCase__					(    self					:       int				,							A					:       Optional[Any]				,							A					:       List[str]				,							A					:       int=False						):
         __snake_case:		str					    =		super()._prepare_for_class(A				,							A				,							return_labels=A						)

         if return_labels:
           if model_class in get_values(A						):
             __snake_case:		str					    =		torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.seq_length)				,							dtype=torch.long				,							device=A						)
         return inputs_dict




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         __snake_case:		Union[str, Any]					    =		FunnelModelTester(self						)
         __snake_case:		List[str]					    =		ConfigTester(self				,							config_class=A						)




       def 				UpperCAmelCase__					(    self					:       Dict						):
         self.config_tester.run_common_tests()




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		int					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		Optional[int]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*A						)




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		Optional[int]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		Any					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A						)




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		str					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A						)






       def 				UpperCAmelCase__					(    self					:       Union[str, Any]				,							A					:       List[Any]						):
         if hasattr(A				,							"""weight"""						) and module.weight is not None:
           module.weight.data.fill_(3						)
         if hasattr(A				,							"""bias"""						) and module.bias is not None:
           module.bias.data.fill_(3						)

         for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
           if hasattr(A				,							A						) and getattr(A				,							A						) is not None:
             __snake_case:		str					    =		getattr(A				,							A						)
             weight.data.fill_(3						)







@require_torch
class        __snake_case			(				__lowerCamelCase  ,     unittest.TestCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	(
           (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
       )




       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		Optional[int]					    =		FunnelModelTester(self				,							base=A						)
         __snake_case:		int					    =		ConfigTester(self				,							config_class=A						)




       def 				UpperCAmelCase__					(    self					:       Dict						):
         self.config_tester.run_common_tests()




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		List[str]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_base_model(*A						)




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		Dict					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		List[str]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case    ,  __snake_case:		Optional[Any]					    =		self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case:		List[str]					    =		True

         for model_class in self.all_model_classes:
           if model_class.__name__ == "FunnelBaseModel":
             continue
           __snake_case:		List[Any]					    =		model_class(A						)
           model.to(A						)
           model.train()
           __snake_case:		Optional[int]					    =		self._prepare_for_class(A				,							A				,							return_labels=A						)
           __snake_case:		Optional[int]					    =		model(**A						).loss
           loss.backward()






       def 				UpperCAmelCase__					(    self					:       Union[str, Any]				,							A					:       Optional[Any]						):
         if hasattr(A				,							"""weight"""						) and module.weight is not None:
           module.weight.data.fill_(3						)
         if hasattr(A				,							"""bias"""						) and module.bias is not None:
           module.bias.data.fill_(3						)

         for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
           if hasattr(A				,							A						) and getattr(A				,							A						) is not None:
             __snake_case:		int					    =		getattr(A				,							A						)
             weight.data.fill_(3						)







@require_torch
@require_sentencepiece
@require_tokenizers
class        __snake_case			(				unittest.TestCase     ):





       '''simple docstring'''







       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		List[str]					    =		13
         __snake_case:		List[Any]					    =		7
         __snake_case:		List[Any]					    =		torch.arange(0				,							batch_size * sequence_length						).long().reshape(A				,							A						)
         __snake_case:		Tuple					    =		[0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
         __snake_case:		List[str]					    =		torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]						)

         __snake_case:		Optional[int]					    =		FunnelModel.from_pretrained("""sgugger/funnel-random-tiny"""						)
         __snake_case:		List[Any]					    =		model(A				,							token_type_ids=A						)[0].abs()

         __snake_case:		str					    =		torch.tensor(2344.8352						)
         __snake_case:		Union[str, Any]					    =		torch.tensor(0.8052						)
         self.assertTrue(torch.allclose(output.sum()				,							A				,							atol=1E-4						)						)
         self.assertTrue(torch.allclose(output.mean()				,							A				,							atol=1E-4						)						)

         __snake_case:		str					    =		torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]						)
         __snake_case:		Tuple					    =		model(A				,							attention_mask=A				,							token_type_ids=A						)[0].abs()

         __snake_case:		List[str]					    =		torch.tensor(2343.8425						)
         __snake_case:		List[Any]					    =		torch.tensor(0.8049						)
         self.assertTrue(torch.allclose(output.sum()				,							A				,							atol=1E-4						)						)
         self.assertTrue(torch.allclose(output.mean()				,							A				,							atol=1E-4						)						)






       @slow
       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		Union[str, Any]					    =		FunnelTokenizer.from_pretrained("""huggingface/funnel-small"""						)
         __snake_case:		Tuple					    =		FunnelModel.from_pretrained("""huggingface/funnel-small"""						)
         __snake_case:		Tuple					    =		tokenizer("""Hello! I am the Funnel Transformer model."""				,							return_tensors="""pt"""						)
         __snake_case:		Union[str, Any]					    =		model(**A						)[0]

         __snake_case:		int					    =		torch.tensor(235.7246						)
         __snake_case:		Dict					    =		torch.tensor(0.0256						)
         self.assertTrue(torch.allclose(output.sum()				,							A				,							atol=1E-4						)						)
         self.assertTrue(torch.allclose(output.mean()				,							A				,							atol=1E-4						)						)






