

import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class  lowercase				(       _UpperCAmelCase	,  _UpperCAmelCase	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Dict       			=			StableDiffusionXLImgaImgPipeline
				__SCREAMING_SNAKE_CASE					: str       			=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			PipelineTesterMixin.required_optional_params - {'''latents'''}
				__SCREAMING_SNAKE_CASE					: List[str]       			=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				__SCREAMING_SNAKE_CASE					: Tuple       			=			IMAGE_TO_IMAGE_IMAGE_PARAMS
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			IMAGE_TO_IMAGE_IMAGE_PARAMS




				def     a							(					self       ):
											torch.manual_seed(0       )
											snake_case_							=    UNetaDConditionModel(
											    block_out_channels=(32, 64)      ,   layers_per_block=2      ,   sample_size=32      ,   in_channels=4      ,   out_channels=4      ,   down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,   up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,   attention_head_dim=(2, 4)      ,   use_linear_projection=lowercase_      ,   addition_embed_type='text_time'      ,   addition_time_embed_dim=8      ,   transformer_layers_per_block=(1, 2)      ,   projection_class_embeddings_input_dim=80      ,   cross_attention_dim=64      ,   )
											snake_case_							=    EulerDiscreteScheduler(
											    beta_start=0.0_00_85      ,   beta_end=0.0_12      ,   steps_offset=1      ,   beta_schedule='scaled_linear'      ,   timestep_spacing='leading'      ,   )
											torch.manual_seed(0       )
											snake_case_							=    AutoencoderKL(
											    block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,   latent_channels=4      ,   sample_size=128      ,   )
											torch.manual_seed(0       )
											snake_case_							=    CLIPTextConfig(
											    bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-0_5      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1000      ,   hidden_act='gelu'      ,   projection_dim=32      ,   )
											snake_case_							=    CLIPTextModel(lowercase_       )
											snake_case_							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      ,   local_files_only=lowercase_       )

											snake_case_							=    CLIPTextModelWithProjection(lowercase_       )
											snake_case_							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      ,   local_files_only=lowercase_       )

											snake_case_							=    {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'text_encoder_2': text_encoder_a,
											    'tokenizer_2': tokenizer_a,
											    # "safety_checker": None,
											    # "feature_extractor": None,
											}
											return components




				def     a							(					self      ,   snake_case      ,   snake_case=0       ):
											snake_case_							=    floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(lowercase_       )       ).to(lowercase_       )
											snake_case_							=    image / 2 + 0.5
											if str(lowercase_       ).startswith('mps'       ):
																		snake_case_							=    torch.manual_seed(lowercase_       )
											else:
																		snake_case_							=    torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )
											snake_case_							=    {
											    'prompt': 'A painting of a squirrel eating a burger',
											    'image': image,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 5.0,
											    'output_type': 'numpy',
											    'strength': 0.75,
											}
											return inputs




				def     a							(					self       ):
											snake_case_							=    'cpu'  # ensure determinism for the device-dependent torch.Generator
											snake_case_							=    self.get_dummy_components()
											snake_case_							=    StableDiffusionXLImgaImgPipeline(**lowercase_       )
											snake_case_							=    sd_pipe.to(lowercase_       )
											sd_pipe.set_progress_bar_config(disable=lowercase_       )

											snake_case_							=    self.get_dummy_inputs(lowercase_       )
											snake_case_							=    sd_pipe(**lowercase_       ).images
											snake_case_							=    image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)

											snake_case_							=    np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]       )

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




				def     a							(					self       ):
											super().test_attention_slicing_forward_pass(expected_max_diff=3e-3       )




				def     a							(					self       ):
											super().test_inference_batch_single_identical(expected_max_diff=3e-3       )




				def     a							(					self       ):
											pass




				def     a							(					self       ):
											snake_case_							=    self.get_dummy_components()
											snake_case_							=    StableDiffusionXLImgaImgPipeline(**lowercase_       )
											snake_case_							=    sd_pipe.to(lowercase_       )
											snake_case_							=    sd_pipe.to(lowercase_       )
											sd_pipe.set_progress_bar_config(disable=lowercase_       )

											# forward without prompt embeds
											snake_case_							=    self.get_dummy_inputs(lowercase_       )
											snake_case_							=    3 * ['this is a negative prompt']
											snake_case_							=    negative_prompt
											snake_case_							=    3 * [inputs['prompt']]

											snake_case_							=    sd_pipe(**lowercase_       )
											snake_case_							=    output.images[0, -3:, -3:, -1]

											# forward with prompt embeds
											snake_case_							=    self.get_dummy_inputs(lowercase_       )
											snake_case_							=    3 * ['this is a negative prompt']
											snake_case_							=    3 * [inputs.pop('prompt'       )]

											(
											    (
											    snake_case_						
											)				,    (
											    snake_case_						
											)				,    (
											    snake_case_						
											)				,    (
											    snake_case_						
											)				,    						
											)							=    sd_pipe.encode_prompt(lowercase_      ,   negative_prompt=lowercase_       )

											snake_case_							=    sd_pipe(
											    **lowercase_      ,   prompt_embeds=lowercase_      ,   negative_prompt_embeds=lowercase_      ,   pooled_prompt_embeds=lowercase_      ,   negative_pooled_prompt_embeds=lowercase_      ,   )
											snake_case_							=    output.images[0, -3:, -3:, -1]

											# make sure that it's equal
											assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1e-4







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




				def     a							(					self       ):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def     a							(					self      ,   snake_case      ,   snake_case="cpu"      ,   snake_case=torch.floataa      ,   snake_case=0       ):
											snake_case_							=    torch.Generator(device=lowercase_       ).manual_seed(lowercase_       )
											snake_case_							=    np.random.RandomState(lowercase_       ).standard_normal((1, 4, 64, 64)       )
											snake_case_							=    torch.from_numpy(lowercase_       ).to(device=lowercase_      ,   dtype=lowercase_       )
											snake_case_							=    {
											    'prompt': 'a photograph of an astronaut riding a horse',
											    'latents': latents,
											    'generator': generator,
											    'num_inference_steps': 3,
											    'guidance_scale': 7.5,
											    'output_type': 'numpy',
											}
											return inputs




				def     a							(					self       ):
											snake_case_							=    DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'       )
											pipe.to(lowercase_       )
											pipe.set_progress_bar_config(disable=lowercase_       )

											snake_case_							=    self.get_inputs(lowercase_       )
											snake_case_							=    pipe(**lowercase_       ).images
											snake_case_							=    image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 512, 512, 3)
											snake_case_							=    np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]       )
											assert np.abs(image_slice - expected_slice       ).max() < 7e-3







import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         GPTNeoXForCausalLM,
         GPTNeoXForQuestionAnswering,
         GPTNeoXForSequenceClassification,
         GPTNeoXForTokenClassification,
         GPTNeoXModel,
     )


class  A						:







    """simple docstring"""





    def __init__(      self :       str,lowercase_ :       Any,lowercase_ :       Tuple=1_3,lowercase_ :       str=7,lowercase_ :       Tuple=True,lowercase_ :       int=True,lowercase_ :       List[Any]=True,lowercase_ :       List[str]=True,lowercase_ :       List[str]=9_9,lowercase_ :       List[Any]=6_4,lowercase_ :       List[str]=5,lowercase_ :       Optional[Any]=4,lowercase_ :       Optional[Any]=3_7,lowercase_ :       Optional[Any]="gelu",lowercase_ :       int=0.1,lowercase_ :       str=0.1,lowercase_ :       Optional[Any]=5_1_2,lowercase_ :       int=1_6,lowercase_ :       List[Any]=2,lowercase_ :       Union[str, Any]=0.02,lowercase_ :       Tuple=3,lowercase_ :       List[Any]=4,lowercase_ :       str=None,)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   seq_length
        A__          =   is_training
        A__          =   use_input_mask
        A__          =   use_token_type_ids
        A__          =   use_labels
        A__          =   vocab_size
        A__          =   hidden_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   intermediate_size
        A__          =   hidden_act
        A__          =   hidden_dropout_prob
        A__          =   attention_probs_dropout_prob
        A__          =   max_position_embeddings
        A__          =   type_vocab_size
        A__          =   type_sequence_label_size
        A__          =   initializer_range
        A__          =   num_labels
        A__          =   num_choices
        A__          =   scope
        A__          =   vocab_size - 1


    def 						snake_case__     (      self :       str		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   ids_tensor([self.batch_size, self.seq_length],self.vocab_size		)

        A__          =   None
        if self.use_input_mask:
            A__          =   random_attention_mask([self.batch_size, self.seq_length]		)

        A__          =   None
        if self.use_labels:
            A__          =   ids_tensor([self.batch_size, self.seq_length],self.num_labels		)

        A__          =   self.get_config()

        return config, input_ids, input_mask, token_labels


    def 						snake_case__     (      self :       List[Any]		)->    Tuple:





        '''simple docstring'''







        return GPTNeoXConfig(
            vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,is_decoder=lowercase_,initializer_range=self.initializer_range,pad_token_id=self.pad_token_id,)


    def 						snake_case__     (      self :       Optional[int]		)->    Union[str, Any]:





        '''simple docstring'''







        A__      ,						A__      ,						A__      ,						A__          =   self.prepare_config_and_inputs()

        A__          =   True

        return config, input_ids, input_mask, token_labels


    def 						snake_case__     (      self :       Any,lowercase_ :       List[Any],lowercase_ :       List[Any],lowercase_ :       str		)->    Any:





        '''simple docstring'''







        A__          =   GPTNeoXModel(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_		)
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)		)


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       List[str],lowercase_ :       Dict,lowercase_ :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







        A__          =   True
        A__          =   GPTNeoXModel(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_		)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)		)


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       str,lowercase_ :       Union[str, Any],lowercase_ :       Union[str, Any],lowercase_ :       List[str]		)->    List[str]:





        '''simple docstring'''







        A__          =   GPTNeoXForCausalLM(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_,labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)		)


    def 						snake_case__     (      self :       Optional[int],lowercase_ :       Optional[int],lowercase_ :       Optional[int],lowercase_ :       Dict,lowercase_ :       Any		)->    int:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   GPTNeoXForQuestionAnswering(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_		)
        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 						snake_case__     (      self :       List[str],lowercase_ :       List[str],lowercase_ :       int,lowercase_ :       Union[str, Any],lowercase_ :       Optional[int]		)->    str:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   GPTNeoXForSequenceClassification(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   ids_tensor([self.batch_size],self.type_sequence_label_size		)
        A__          =   model(lowercase_,attention_mask=lowercase_,labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)		)


    def 						snake_case__     (      self :       Any,lowercase_ :       Union[str, Any],lowercase_ :       List[Any],lowercase_ :       Optional[Any],lowercase_ :       int		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.num_labels
        A__          =   GPTNeoXForTokenClassification(lowercase_		)
        model.to(lowercase_		)
        model.eval()
        A__          =   model(lowercase_,attention_mask=lowercase_,labels=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)		)


    def 						snake_case__     (      self :       int,lowercase_ :       str,lowercase_ :       int,lowercase_ :       Union[str, Any]		)->    List[Any]:





        '''simple docstring'''







        A__          =   True
        A__          =   GPTNeoXForCausalLM(config=lowercase_		)
        model.to(lowercase_		)
        model.eval()

        # first forward pass
        A__          =   model(lowercase_,attention_mask=lowercase_,use_cache=lowercase_		)
        A__          =   outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        A__          =   ids_tensor((self.batch_size, 3),config.vocab_size		)
        A__          =   ids_tensor((self.batch_size, 3),vocab_size=2		)

        # append to next input_ids and
        A__          =   torch.cat([input_ids, next_tokens],dim=-1		)
        A__          =   torch.cat([input_mask, next_mask],dim=-1		)

        A__          =   model(lowercase_,attention_mask=lowercase_,output_hidden_states=lowercase_		)
        A__          =   output_from_no_past['hidden_states'][0]
        A__          =   model(
            lowercase_,attention_mask=lowercase_,past_key_values=lowercase_,output_hidden_states=lowercase_,)['hidden_states'][0]

        # select random slice
        A__          =   ids_tensor((1,),output_from_past.shape[-1]		).item()
        A__          =   output_from_no_past[:, -3:, random_slice_idx].detach()
        A__          =   output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(lowercase_,lowercase_,atol=1E-3		)		)


    def 						snake_case__     (      self :       str		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.prepare_config_and_inputs()
        A__      ,						A__      ,						A__      ,						A__          =   config_and_inputs
        A__          =   {'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict




@require_torch
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(
        (
            GPTNeoXModel,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase			=							(GPTNeoXForCausalLM,) if is_torch_available() else ()
    lowerCamelCase			=							(
        {
            'feature-extraction': GPTNeoXModel,
            'question-answering': GPTNeoXForQuestionAnswering,
            'text-classification': GPTNeoXForSequenceClassification,
            'text-generation': GPTNeoXForCausalLM,
            'token-classification': GPTNeoXForTokenClassification,
            'zero-shot': GPTNeoXForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        A__          =   GPTNeoXModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,hidden_size=6_4,num_attention_heads=8		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Union[str, Any]:





        '''simple docstring'''







        self.config_tester.run_common_tests()


    def 						snake_case__     (      self :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__      ,						A__      ,						A__      ,						A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    List[Any]:





        '''simple docstring'''







        A__      ,						A__      ,						A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       List[str]		)->    Any:





        '''simple docstring'''







        A__      ,						A__      ,						A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_decoder()

        A__          =   None

        self.model_tester.create_and_check_model_as_decoder(lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    str:





        '''simple docstring'''







        A__      ,						A__      ,						A__      ,						A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase_,lowercase_,lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    Dict:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*lowercase_		)


    def 						snake_case__     (      self :       Tuple		)->    List[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase_		)


    def 						snake_case__     (      self :       Any		)->    List[str]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowercase_		)


    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase_		)


    @unittest.skip(reason='Feed forward chunking is not implemented'		)
    def 						snake_case__     (      self :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







        pass


    @parameterized.expand([('linear',), ('dynamic',)]		)
    def 						snake_case__     (      self :       List[str],lowercase_ :       Any		)->    List[str]:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()
        A__          =   ids_tensor([1, 1_0],config.vocab_size		)
        A__          =   ids_tensor([1, int(config.max_position_embeddings * 1.5		)],config.vocab_size		)

        set_seed(4_2		)  # Fixed seed at init time so the two models get the same random weights
        A__          =   GPTNeoXModel(lowercase_		)
        original_model.to(lowercase_		)
        original_model.eval()
        A__          =   original_model(lowercase_		).last_hidden_state
        A__          =   original_model(lowercase_		).last_hidden_state

        set_seed(4_2		)  # Fixed seed at init time so the two models get the same random weights
        A__          =   {'type': scaling_type, 'factor': 10.0}
        A__          =   GPTNeoXModel(lowercase_		)
        scaled_model.to(lowercase_		)
        scaled_model.eval()
        A__          =   scaled_model(lowercase_		).last_hidden_state
        A__          =   scaled_model(lowercase_		).last_hidden_state

        # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
        # maximum sequence length, so the outputs for the short input should match.
        if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(lowercase_,lowercase_,atol=1E-5		)		)
        else:
            self.assertFalse(torch.allclose(lowercase_,lowercase_,atol=1E-5		)		)

        # The output should be different for long inputs
        self.assertFalse(torch.allclose(lowercase_,lowercase_,atol=1E-5		)		)




@require_torch
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @slow
    def 						snake_case__     (      self :       Tuple		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'		)
        for checkpointing in [True, False]:
            A__          =   GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'		)

            if checkpointing:
                model.gradient_checkpointing_enable()
            else:
                model.gradient_checkpointing_disable()
            model.to(lowercase_		)

            A__          =   tokenizer('My favorite food is',return_tensors='pt'		).to(lowercase_		)
            # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
            # See: https://github.com/huggingface/transformers/pull/24193
            A__          =   'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

            A__          =   model.generate(**lowercase_,do_sample=lowercase_,max_new_tokens=2_0		)
            A__          =   tokenizer.batch_decode(lowercase_		)[0]

            self.assertEqual(lowercase_,lowercase_		)

