

'''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
__lowerCamelCase									=     '''src/transformers'''
__lowerCamelCase									=     '''docs/source/en/tasks'''
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       with open(UpperCAmelCase__,							"""r""",							encoding="""utf-8""",							newline="""\n"""					) as f:
              A_						       =   f.readlines()
       # Find the start prompt.
       A_						       =   0
       while not lines[start_index].startswith(UpperCAmelCase__					):
              start_index += 1
       start_index += 1

       A_						       =   start_index
       while not lines[end_index].startswith(UpperCAmelCase__					):
              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.
__lowerCamelCase									=     direct_transformers_import(TRANSFORMERS_PATH)

__lowerCamelCase									=     {
    '''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`).
__lowerCamelCase									=     {
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      Any:
       A_						       =   TASK_GUIDE_TO_MODELS[task_guide]
       A_						       =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase__,							set()					)
       A_						       =   {
           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__     (   UpperCAmelCase__,							UpperCAmelCase__=False					)      ->      List[Any]:
       A_       ,      A_       ,      A_       ,      A_						       =   _find_text_in_file(
           filename=os.path.join(UpperCAmelCase__,							UpperCAmelCase__					),							start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->""",							end_prompt="""<!--End of the generated tip-->""",							)

       A_						       =   get_model_list_for_task(UpperCAmelCase__					)

       if current_list != new_list:
              if overwrite:
                     with open(os.path.join(UpperCAmelCase__,							UpperCAmelCase__					),							"""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__":
      __lowerCamelCase									=     argparse.ArgumentParser()
      parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
      __lowerCamelCase									=     parser.parse_args()

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



'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        A__   (   unittest.TestCase      ):



    @property
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           torch.manual_seed(0							)
           A_						       =   UNetaDModel(
               block_out_channels=(32, 64)		,					layers_per_block=2		,					sample_size=32		,					in_channels=3		,					out_channels=3		,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")		,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")		,					)
           return model







    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.dummy_uncond_unet
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""							).images

           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					num_inference_steps=20		,					output_type="""numpy"""		,					return_dict=UpperCamelCase__							)[0]

           A_						       =   image[0, -3:, -3:, -1]
           A_						       =   image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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


@slow
@require_torch
class        A__   (   unittest.TestCase      ):



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   """google/ddpm-cifar10-32"""

           A_						       =   UNetaDModel.from_pretrained(UpperCamelCase__							)
           A_						       =   PNDMScheduler()

           A_						       =   PNDMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__							)
           pndm.to(UpperCamelCase__							)
           pndm.set_progress_bar_config(disable=UpperCamelCase__							)
           A_						       =   torch.manual_seed(0							)
           A_						       =   pndm(generator=UpperCamelCase__		,					output_type="""numpy"""							).images

           A_						       =   image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           A_						       =   np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]							)

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