
import argparse
from collections import defaultdict

import yaml


lowerCamelCase_						     =					'''docs/source/en/_toctree.yml'''






def 						__magic_name__			(   __a      :	Union[str, Any]						):



     '''simple docstring'''




     UpperCamelCase__				=    defaultdict(__a						)
     for doc in model_doc:
          counts[doc["local"]] += 1
     UpperCamelCase__				=    [key for key, value in counts.items() if value > 1]

     UpperCamelCase__				=    []
     for duplicate_key in duplicates:
          UpperCamelCase__				=    list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key}						)
          if len(__a						) > 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(__a	,		key=lambda __a						: s["title"].lower()						)






def 						__magic_name__			(   __a      :	int=False						):



     '''simple docstring'''




     with open(__a	,		encoding="""utf-8"""						) as f:
          UpperCamelCase__				=    yaml.safe_load(f.read()						)

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

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

     UpperCamelCase__				=    api_doc[model_idx]["""sections"""]

     UpperCamelCase__				=    [(idx, section) for idx, section in enumerate(__a						) if """sections""" in section]
     UpperCamelCase__				=    False
     for idx, modality_doc in modalities_docs:
          UpperCamelCase__				=    modality_doc["""sections"""]
          UpperCamelCase__				=    clean_model_doc_toc(__a						)

          if old_modality_doc != new_modality_doc:
               UpperCamelCase__				=    True
               if overwrite:
                    UpperCamelCase__				=    new_modality_doc

     if diff:
          if overwrite:
               UpperCamelCase__				=    model_doc
               UpperCamelCase__				=    api_doc
               with open(__a	,		"""w"""	,		encoding="""utf-8"""						) as f:
                    f.write(yaml.dump(__a	,		allow_unicode=__a						)						)
          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__":
    lowerCamelCase_						     =					argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    lowerCamelCase_						     =					parser.parse_args()

    check_model_doc(args.fix_and_overwrite)







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 GLPNImageProcessor




class      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=18	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=4_00	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=True	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    min_resolution
       UpperCamelCase__				=    max_resolution
       UpperCamelCase__				=    do_resize
       UpperCamelCase__				=    size_divisor
       UpperCamelCase__				=    do_rescale







  def 	UpperCAmelCase_ (self						):
       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }






@require_torch
@require_vision
class      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      GLPNImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    GLPNImageProcessingTester(self						)







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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_resize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """size_divisor"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """resample"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_rescale"""						)						)







  def 	UpperCAmelCase_ (self						):
       pass







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      numpify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      np.ndarray						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







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

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)





