


"""simple docstring"""







import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
a__				:							Dict						=     '''src/transformers'''
a__				:							Union[str, Any]						=     '''docs/source/en/tasks'''




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					with open(lowerCAmelCase_  ,						"r"  ,						encoding="utf-8"  ,						newline="\n"   ) as f:
										__SCREAMING_SNAKE_CASE						   =				f.readlines()
					# Find the start prompt.
					__SCREAMING_SNAKE_CASE						   =				0
					while not lines[start_index].startswith(lowerCAmelCase_   ):
										start_index += 1
					start_index += 1

					__SCREAMING_SNAKE_CASE						   =				start_index
					while not lines[end_index].startswith(lowerCAmelCase_   ):
										end_index += 1
					end_index -= 1

					while len(lines[start_index]   ) <= 1:
										start_index += 1
					while len(lines[end_index]   ) <= 1:
										end_index -= 1
					end_index += 1
					return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
a__				:							Optional[Any]						=     direct_transformers_import(TRANSFORMERS_PATH)

a__				:							int						=     {
    '''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    '''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    '''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    '''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    '''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    '''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    '''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    '''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    '''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    '''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    '''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    '''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    '''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    '''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
a__				:							Optional[Any]						=     {
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				TASK_GUIDE_TO_MODELS[task_guide]
					__SCREAMING_SNAKE_CASE						   =				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowerCAmelCase_  ,						set()   )
					__SCREAMING_SNAKE_CASE						   =				{
					    code: name
					    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
					    if (code in model_maping_names or code in special_model_types)
					}
					return ", ".join([f"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]   ) + "\n"




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_=False   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				_find_text_in_file(
					    filename=os.path.join(lowerCAmelCase_  ,						lowerCAmelCase_   )  ,						start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"  ,						end_prompt="<!--End of the generated tip-->"  ,						)

					__SCREAMING_SNAKE_CASE						   =				get_model_list_for_task(lowerCAmelCase_   )

					if current_list != new_list:
										if overwrite:
															with open(os.path.join(lowerCAmelCase_  ,						lowerCAmelCase_   )  ,						"w"  ,						encoding="utf-8"  ,						newline="\n"   ) as f:
																				f.writelines(lines[:start_index] + [new_list] + lines[end_index:]   )
										else:
															raise ValueError(
															    f"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
															    " to fix this."   )


if __name__ == "__main__":
		a__				:							List[Any]						=     argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		a__				:							Optional[Any]						=     parser.parse_args()

		for task_guide in TASK_GUIDE_TO_MODELS.keys():
				check_model_list_for_task(task_guide, args.fix_and_overwrite)

import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




def       _snake_case	(   lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple				      =    [tensor.shape for tensor in tensor_list]
		return all(shape == shapes[0] for shape in shapes[1:]	)






class 		a__						(					A__    ,		A__    ,		A__    ,		unittest.TestCase					):
					A	       =							StableDiffusionLatentUpscalePipeline
					A	       =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
					    'height',
					    'width',
					    'cross_attention_kwargs',
					    'negative_prompt_embeds',
					    'prompt_embeds',
					}
					A	       =							PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
					A	       =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					A	       =							frozenset(
					    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					A	       =							frozenset([]					)

					A	       =							True

					@property
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    1
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    4
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    (16, 16)

							SCREAMING_SNAKE_CASE_  :		Dict				      =    floats_tensor((batch_size, num_channels) + sizes,rng=random.Random(0    )    ).to(_A    )
							return image

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		str				      =    UNetaDConditionModel(
							    act_fn="gelu",attention_head_dim=8,norm_num_groups=_A,block_out_channels=[32, 32, 64, 64],time_cond_proj_dim=160,conv_in_kernel=1,conv_out_kernel=1,cross_attention_dim=32,down_block_types=(
							        "KDownBlock2D",
							        "KCrossAttnDownBlock2D",
							        "KCrossAttnDownBlock2D",
							        "KCrossAttnDownBlock2D",
							    ),in_channels=8,mid_block_type=_A,only_cross_attention=_A,out_channels=5,resnet_time_scale_shift="scale_shift",time_embedding_type="fourier",timestep_post_act="gelu",up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D"),)
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoencoderKL(
							    block_out_channels=[32, 32, 64, 64],in_channels=3,out_channels=3,down_block_types=[
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							    ],up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],latent_channels=4,)
							SCREAMING_SNAKE_CASE_  :		int				      =    EulerDiscreteScheduler(prediction_type="sample"    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    CLIPTextConfig(
							    bos_token_id=0,eos_token_id=2,hidden_size=32,intermediate_size=37,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1000,hidden_act="quick_gelu",projection_dim=512,)
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    CLIPTextModel(_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {
							    "unet": model.eval(),
							    "vae": vae.eval(),
							    "scheduler": scheduler,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							}

							return components

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       int,_A      :       Tuple=0    ):







							"""simple docstring"""






							if str(_A    ).startswith("mps"    ):
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    torch.manual_seed(_A    )
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.Generator(device=_A    ).manual_seed(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": self.dummy_image.cpu(),
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    "cpu"

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.get_dummy_components()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.pipeline_class(**_A    )
							pipe.to(_A    )
							pipe.set_progress_bar_config(disable=_A    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.get_dummy_inputs(_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    pipe(**_A    ).images
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    image[0, -3:, -3:, -1]

							self.assertEqual(image.shape,(1, 256, 256, 3)    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    np.array(
							    [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    np.abs(image_slice.flatten() - expected_slice    ).max()
							self.assertLessEqual(_A,1E-3    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							super().test_attention_slicing_forward_pass(expected_max_diff=7E-3    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							super().test_cpu_offload_forward_pass(expected_max_diff=3E-3    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							super().test_inference_batch_single_identical(expected_max_diff=7E-3    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							super().test_save_load_local(expected_max_difference=3E-3    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							super().test_save_load_optional_components(expected_max_difference=3E-3    )






					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    [
							    "DDIMScheduler",
							    "DDPMScheduler",
							    "PNDMScheduler",
							    "HeunDiscreteScheduler",
							    "EulerAncestralDiscreteScheduler",
							    "KDPM2DiscreteScheduler",
							    "KDPM2AncestralDiscreteScheduler",
							    "DPMSolverSDEScheduler",
							]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_dummy_components()
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.pipeline_class(**_A    )

							# make sure that PNDM does not need warm-up
							pipe.scheduler.register_to_config(skip_prk_steps=_A    )

							pipe.to(_A    )
							pipe.set_progress_bar_config(disable=_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.get_dummy_inputs(_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    2

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    []
							for scheduler_enum in KarrasDiffusionSchedulers:
									if scheduler_enum.name in skip_schedulers:
											# no sigma schedulers are not supported
											# no schedulers
											continue

									SCREAMING_SNAKE_CASE_  :		Tuple				      =    getattr(_A,scheduler_enum.name    )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    scheduler_cls.from_config(pipe.scheduler.config    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    pipe(**_A    )[0]
									outputs.append(_A    )

							assert check_same_shape(_A    )







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

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    torch.manual_seed(33    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4",torch_dtype=torch.floataa    )
							pipe.to("cuda"    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler",torch_dtype=torch.floataa    )
							upscaler.to("cuda"    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    "a photo of an astronaut high resolution, unreal engine, ultra realistic"

							SCREAMING_SNAKE_CASE_  :		str				      =    pipe(_A,generator=_A,output_type="latent"    ).images

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    upscaler(
							    prompt=_A,image=_A,num_inference_steps=20,guidance_scale=0,generator=_A,output_type="np",).images[0]

							SCREAMING_SNAKE_CASE_  :		str				      =    load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"    )
							assert np.abs((expected_image - image).mean()    ) < 5E-2






					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    torch.manual_seed(33    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler",torch_dtype=torch.floataa    )
							upscaler.to("cuda"    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"    )

							SCREAMING_SNAKE_CASE_  :		str				      =    upscaler(
							    prompt=_A,image=_A,num_inference_steps=20,guidance_scale=0,generator=_A,output_type="np",).images[0]

							SCREAMING_SNAKE_CASE_  :		Any				      =    load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"    )
							assert np.abs((expected_image - image).max()    ) < 5E-2
