"""simple docstring"""




import argparse
from collections import defaultdict

import yaml


_a				:		Union[str, Any]	   =       'docs/source/en/_toctree.yml'





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[Any]     )	->				Tuple:
  _lowerCAmelCase						:	Optional[int]        =					defaultdict(_lowerCamelCase     )
  for doc in model_doc:
    counts[doc["local"]] += 1
  _lowerCAmelCase						:	Any        =					[key for key, value in counts.items() if value > 1]

  _lowerCAmelCase						:	List[str]        =					[]
  for duplicate_key in duplicates:
    _lowerCAmelCase						:	Any        =					list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key}     )
    if len(_lowerCamelCase     ) > 1:
      raise ValueError(
          f"{duplicate_key} is present several times in the documentation table of content at "
          """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
          """others."""     )
    # Only add this once
    new_doc.append({"""local""": duplicate_key, """title""": titles[0]}     )

  # Add none duplicate-keys
  new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1]     )

  # Sort
  return sorted(_lowerCamelCase      ,key=lambda _lowerCamelCase     : s["title"].lower()     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Tuple=False     )	->				int:
  with open(_lowerCamelCase      ,encoding="""utf-8"""     ) as f:
    _lowerCAmelCase						:	str        =					yaml.safe_load(f.read()     )

  # Get to the API doc
  _lowerCAmelCase						:	Optional[Any]        =					0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  _lowerCAmelCase						:	Dict        =					content[api_idx]["""sections"""]

  # Then to the model doc
  _lowerCAmelCase						:	List[Any]        =					0
  while api_doc[model_idx]["title"] != "Models":
    model_idx += 1

  _lowerCAmelCase						:	List[Any]        =					api_doc[model_idx]["""sections"""]

  _lowerCAmelCase						:	int        =					[(idx, section) for idx, section in enumerate(_lowerCamelCase     ) if """sections""" in section]
  _lowerCAmelCase						:	str        =					False
  for idx, modality_doc in modalities_docs:
    _lowerCAmelCase						:	int        =					modality_doc["""sections"""]
    _lowerCAmelCase						:	Optional[int]        =					clean_model_doc_toc(_lowerCamelCase     )

    if old_modality_doc != new_modality_doc:
      _lowerCAmelCase						:	List[Any]        =					True
      if overwrite:
        _lowerCAmelCase						:	Tuple        =					new_modality_doc

  if diff:
    if overwrite:
      _lowerCAmelCase						:	Tuple        =					model_doc
      _lowerCAmelCase						:	List[Any]        =					api_doc
      with open(_lowerCamelCase      ,"""w"""      ,encoding="""utf-8"""     ) as f:
        f.write(yaml.dump(_lowerCamelCase      ,allow_unicode=_lowerCamelCase     )     )
    else:
      raise ValueError(
          """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""     )


if __name__ == "__main__":
     _a				:		Dict	   =       argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     _a				:		Dict	   =       parser.parse_args()

     check_model_doc(args.fix_and_overwrite)





"""simple docstring"""




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_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor
class 						__A							(			unittest.TestCase		):






     def __init__(		self    ,					a__    ,					a__=7    ,					a__=3    ,					a__=30    ,					a__=400    ,					a__=True    ,					a__=None    ,					a__=0.9    ,					a__=None    ,					a__=True    ,					a__=[0.5, 0.5, 0.5]    ,					a__=[0.5, 0.5, 0.5]    ,					):
       _lowerCAmelCase						:	int        =					size if size is not None else {"""shortest_edge""": 30}
       _lowerCAmelCase						:	Dict        =					crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
       _lowerCAmelCase						:	List[Any]        =					parent
       _lowerCAmelCase						:	Optional[int]        =					batch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	str        =					min_resolution
       _lowerCAmelCase						:	Dict        =					max_resolution
       _lowerCAmelCase						:	str        =					do_resize_and_center_crop
       _lowerCAmelCase						:	List[str]        =					size
       _lowerCAmelCase						:	int        =					crop_pct
       _lowerCAmelCase						:	int        =					crop_size
       _lowerCAmelCase						:	Union[str, Any]        =					do_normalize
       _lowerCAmelCase						:	Tuple        =					image_mean
       _lowerCAmelCase						:	Optional[Any]        =					image_std






     def    __A  (		self  ):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[str]		 =		PoolFormerImageProcessor if is_vision_available() else None






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					PoolFormerImageProcessingTester(self  )






     @property
     def    __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__    ,					"""do_resize_and_center_crop"""  )  )
       self.assertTrue(hasattr(a__    ,					"""size"""  )  )
       self.assertTrue(hasattr(a__    ,					"""crop_pct"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_normalize"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_mean"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_std"""  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 30}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 30, """width""": 30}  )

       _lowerCAmelCase						:	List[Any]        =					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  ):
       pass






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:	Tuple        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					Image.Image  )

       # Test not batched input
       _lowerCAmelCase						:	Tuple        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values

       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:	Union[str, Any]        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					numpify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					np.ndarray  )

       # Test not batched input
       _lowerCAmelCase						:	Dict        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	int        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:	Any        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					torchify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					torch.Tensor  )

       # Test not batched input
       _lowerCAmelCase						:	str        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)




