import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import VivitImageProcessor


class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def __init__(							self       :				Optional[int]     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				int=7     ,   __magic_name__       :				List[Any]=3     ,   __magic_name__       :				Dict=10     ,   __magic_name__       :				int=18     ,   __magic_name__       :				List[str]=30     ,   __magic_name__       :				Union[str, Any]=400     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Optional[int]=None     ,   __magic_name__       :				int=True     ,   __magic_name__       :				Union[str, Any]=[0.5, 0.5, 0.5]     ,   __magic_name__       :				List[Any]=[0.5, 0.5, 0.5]     ,   __magic_name__       :				List[Any]=None     ,   )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     size if size is not None else {"shortest_edge": 18}
        SCREAMING_SNAKE_CASE_      =     crop_size if crop_size is not None else {"height": 18, "width": 18}

        SCREAMING_SNAKE_CASE_      =     parent
        SCREAMING_SNAKE_CASE_      =     batch_size
        SCREAMING_SNAKE_CASE_      =     num_channels
        SCREAMING_SNAKE_CASE_      =     num_frames
        SCREAMING_SNAKE_CASE_      =     image_size
        SCREAMING_SNAKE_CASE_      =     min_resolution
        SCREAMING_SNAKE_CASE_      =     max_resolution
        SCREAMING_SNAKE_CASE_      =     do_resize
        SCREAMING_SNAKE_CASE_      =     size
        SCREAMING_SNAKE_CASE_      =     do_normalize
        SCREAMING_SNAKE_CASE_      =     image_mean
        SCREAMING_SNAKE_CASE_      =     image_std
        SCREAMING_SNAKE_CASE_      =     crop_size







  def       __A   (							self       :				Optional[Any]      )     ->		int:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  VivitImageProcessor if is_vision_available() else None







  def       __A   (							self       :				Optional[int]      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     VivitImageProcessingTester(self      )







  @property
  def       __A   (							self       :				Optional[int]      )     ->		str:
        return self.image_processor_tester.prepare_image_processor_dict()







  def       __A   (							self       :				Optional[Any]      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class(**self.image_processor_dict      )
        self.assertTrue(hasattr(__magic_name__     ,   "image_mean"      )      )
        self.assertTrue(hasattr(__magic_name__     ,   "image_std"      )      )
        self.assertTrue(hasattr(__magic_name__     ,   "do_normalize"      )      )
        self.assertTrue(hasattr(__magic_name__     ,   "do_resize"      )      )
        self.assertTrue(hasattr(__magic_name__     ,   "do_center_crop"      )      )
        self.assertTrue(hasattr(__magic_name__     ,   "size"      )      )







  def       __A   (							self       :				Union[str, Any]      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class.from_dict(self.image_processor_dict      )
        self.assertEqual(image_processor.size     ,   {"shortest_edge": 18}      )
        self.assertEqual(image_processor.crop_size     ,   {"height": 18, "width": 18}      )

        SCREAMING_SNAKE_CASE_      =     self.image_processing_class.from_dict(self.image_processor_dict     ,   size=42     ,   crop_size=84      )
        self.assertEqual(image_processor.size     ,   {"shortest_edge": 42}      )
        self.assertEqual(image_processor.crop_size     ,   {"height": 84, "width": 84}      )







  def       __A   (							self       :				Tuple      )     ->		List[str]:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class(**self.image_processor_dict      )
        # create random PIL videos
        SCREAMING_SNAKE_CASE_      =     prepare_video_inputs(self.image_processor_tester     ,   equal_resolution=__magic_name__      )
        for video in video_inputs:
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )
              self.assertIsInstance(video[0]     ,   Image.Image      )

        # Test not batched input
        SCREAMING_SNAKE_CASE_      =     image_processing(video_inputs[0]     ,   return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_videos.shape     ,   (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )     ,   )

        # Test batched
        SCREAMING_SNAKE_CASE_      =     image_processing(__magic_name__     ,   return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_videos.shape     ,   (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )     ,   )







  def       __A   (							self       :				Any      )     ->		Any:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class(**self.image_processor_dict      )
        # create random numpy tensors
        SCREAMING_SNAKE_CASE_      =     prepare_video_inputs(self.image_processor_tester     ,   equal_resolution=__magic_name__     ,   numpify=__magic_name__      )
        for video in video_inputs:
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )
              self.assertIsInstance(video[0]     ,   np.ndarray      )

        # Test not batched input
        SCREAMING_SNAKE_CASE_      =     image_processing(video_inputs[0]     ,   return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_videos.shape     ,   (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )     ,   )

        # Test batched
        SCREAMING_SNAKE_CASE_      =     image_processing(__magic_name__     ,   return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_videos.shape     ,   (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )     ,   )







  def       __A   (							self       :				Any      )     ->		Dict:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_      =     self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE_      =     prepare_video_inputs(self.image_processor_tester     ,   equal_resolution=__magic_name__     ,   torchify=__magic_name__      )
        for video in video_inputs:
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )
              self.assertIsInstance(video[0]     ,   torch.Tensor      )

        # Test not batched input
        SCREAMING_SNAKE_CASE_      =     image_processing(video_inputs[0]     ,   return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_videos.shape     ,   (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )     ,   )

        # Test batched
        SCREAMING_SNAKE_CASE_      =     image_processing(__magic_name__     ,   return_tensors="pt"      ).pixel_values
        self.assertEqual(
            encoded_videos.shape     ,   (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            )     ,   )



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   :     Tuple       =				"src/transformers"
A   :     Optional[Any]       =				"docs/source/en/tasks"


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      with open(__UpperCamelCase   ,					"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(__UpperCamelCase ):
            start_index += 1
      start_index += 1

      SCREAMING_SNAKE_CASE_      =     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.
A   :     List[str]       =				direct_transformers_import(TRANSFORMERS_PATH)

A   :     List[Any]       =				{
    "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   :     Any       =				{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     TASK_GUIDE_TO_MODELS[task_guide]
      SCREAMING_SNAKE_CASE_      =     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__UpperCamelCase   ,					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    a__       (						__UpperCamelCase   ,					__UpperCamelCase=False ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     _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-->"   ,					)

      SCREAMING_SNAKE_CASE_      =     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__":
      A   :     Tuple       =				argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      A   :     Dict       =				parser.parse_args()

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


