


"""simple docstring"""


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


A     :   List[Any]			    =			False
@skip_mps
class   _UpperCamelCase  (   __lowercase       ,__lowercase       ,__lowercase       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    List[Any]				      =StableDiffusionAttendAndExcitePipeline
							__UpperCAmelCase						:    Any				      =False
							__UpperCAmelCase						:    List[str]				      =TEXT_TO_IMAGE_PARAMS
							__UpperCAmelCase						:    Tuple				      =TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""}		)
							__UpperCAmelCase						:    Dict				      =TEXT_TO_IMAGE_IMAGE_PARAMS
							__UpperCAmelCase						:    Optional[Any]				      =TEXT_TO_IMAGE_IMAGE_PARAMS

							@classmethod
							def 			snake_case				(     cls	):
										super().setUpClass()
										torch.use_deterministic_algorithms(UpperCAmelCase__	)

							@classmethod
							def 			snake_case				(     cls	):
										super().tearDownClass()
										torch.use_deterministic_algorithms(UpperCAmelCase__	)

							def 			snake_case				(     self	):
										torch.manual_seed(0	)
										__lowerCAmelCase				    =			UNetaDConditionModel(
										    block_out_channels=(32, 64)    ,   layers_per_block=1    ,   sample_size=32    ,   in_channels=4    ,   out_channels=4    ,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")    ,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")    ,   cross_attention_dim=32    ,   attention_head_dim=(2, 4)    ,   use_linear_projection=UpperCAmelCase__    ,   )
										__lowerCAmelCase				    =			DDIMScheduler(
										    beta_start=0.0_0_0_8_5    ,   beta_end=0.0_1_2    ,   beta_schedule="scaled_linear"    ,   clip_sample=UpperCAmelCase__    ,   set_alpha_to_one=UpperCAmelCase__    ,   )
										torch.manual_seed(0	)
										__lowerCAmelCase				    =			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=1_28    ,   )
										torch.manual_seed(0	)
										__lowerCAmelCase				    =			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=10_00    ,   hidden_act="gelu"    ,   projection_dim=5_12    ,   )
										__lowerCAmelCase				    =			CLIPTextModel(UpperCAmelCase__	)
										__lowerCAmelCase				    =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										__lowerCAmelCase				    =			{
										    "unet": unet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}

										return components

							def 			snake_case				(     self    ,   __a    ,   __a=0	):
										if str(UpperCAmelCase__	).startswith("mps"	):
													__lowerCAmelCase				    =			torch.manual_seed(UpperCAmelCase__	)
										else:
													__lowerCAmelCase				    =			torch.Generator(device=UpperCAmelCase__	).manual_seed(UpperCAmelCase__	)
										__lowerCAmelCase				    =			__lowerCAmelCase				    =			{
										    "prompt": "a cat and a frog",
										    "token_indices": [2, 5],
										    "generator": generator,
										    "num_inference_steps": 1,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										    "max_iter_to_alter": 2,
										    "thresholds": {0: 0.7},
										}
										return inputs

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			"cpu"

										__lowerCAmelCase				    =			self.get_dummy_components()
										__lowerCAmelCase				    =			self.pipeline_class(**UpperCAmelCase__	)
										pipe.to(UpperCAmelCase__	)
										pipe.set_progress_bar_config(disable=UpperCAmelCase__	)

										__lowerCAmelCase				    =			self.get_dummy_inputs(UpperCAmelCase__	)
										__lowerCAmelCase				    =			pipe(**UpperCAmelCase__	).images
										__lowerCAmelCase				    =			image[0, -3:, -3:, -1]

										self.assertEqual(image.shape    ,   (1, 64, 64, 3)	)
										__lowerCAmelCase				    =			np.array(
										    [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]	)
										__lowerCAmelCase				    =			np.abs(image_slice.flatten() - expected_slice	).max()
										self.assertLessEqual(UpperCAmelCase__    ,   1e-3	)

							def 			snake_case				(     self	):
										super().test_cpu_offload_forward_pass(expected_max_diff=5e-4	)

							def 			snake_case				(     self	):
										# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
										self._test_inference_batch_consistent(batch_sizes=[1, 2]	)

							def 			snake_case				(     self	):
										self._test_inference_batch_single_identical(batch_size=2    ,   expected_max_diff=7e-4	)

							def 			snake_case				(     self	):
										super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3	)

							def 			snake_case				(     self	):
										super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4	)

							def 			snake_case				(     self	):
										super().test_save_load_local(expected_max_difference=5e-4	)



							def 			snake_case				(     self	):
										super().test_save_load_optional_components(expected_max_difference=4e-4	)
@require_torch_gpu
@slow
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							@classmethod
							def 			snake_case				(     cls	):
										super().setUpClass()
										torch.use_deterministic_algorithms(UpperCAmelCase__	)

							@classmethod
							def 			snake_case				(     cls	):
										super().tearDownClass()
										torch.use_deterministic_algorithms(UpperCAmelCase__	)

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



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			torch.manual_seed(51	)

										__lowerCAmelCase				    =			StableDiffusionAttendAndExcitePipeline.from_pretrained(
										    "CompVis/stable-diffusion-v1-4"    ,   safety_checker=UpperCAmelCase__    ,   torch_dtype=torch.floataa	)
										pipe.to("cuda"	)

										__lowerCAmelCase				    =			"a painting of an elephant with glasses"
										__lowerCAmelCase				    =			[5, 7]

										__lowerCAmelCase				    =			pipe(
										    prompt=UpperCAmelCase__    ,   token_indices=UpperCAmelCase__    ,   guidance_scale=7.5    ,   generator=UpperCAmelCase__    ,   num_inference_steps=5    ,   max_iter_to_alter=5    ,   output_type="numpy"    ,   ).images[0]

										__lowerCAmelCase				    =			load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"	)
										assert np.abs((expected_image - image).max()	) < 5e-1








'''simple docstring'''




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    						={
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__snake_case    						={
    """facebook/blenderbot_small-90M""": 512,
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	Union[str, Any]		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     BlenderbotSmallTokenizer





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Dict=None    ,      UpperCAmelCase__       :      int="<|endoftext|>"    ,      UpperCAmelCase__       :      Dict="<|endoftext|>"    ,      UpperCAmelCase__       :      str="<|endoftext|>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Tuple=True    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Any:
        super().__init__(
            ByteLevelBPETokenizer(
                vocab=UpperCAmelCase__    ,      merges=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      )    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     =			add_prefix_space





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict=None    )				->  Any:
        lowerCAmelCase     =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





