
'''simple docstring'''







import argparse
from collections import defaultdict

import yaml


_snake_case				:   Optional[int]     =				"""docs/source/en/_toctree.yml"""



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						_SCREAMING_SNAKE_CASE				= defaultdict(_SCREAMING_SNAKE_CASE  )
						for doc in model_doc:
												counts[doc["local"]] += 1
						_SCREAMING_SNAKE_CASE				= [key for key, value in counts.items() if value > 1]

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]=False  ):
						with open(_SCREAMING_SNAKE_CASE	,			encoding="utf-8"  ) as f:
												_SCREAMING_SNAKE_CASE				= yaml.safe_load(f.read()  )

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

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

						_SCREAMING_SNAKE_CASE				= api_doc[model_idx]["sections"]

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

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

						if diff:
												if overwrite:
																		_SCREAMING_SNAKE_CASE				= model_doc
																		_SCREAMING_SNAKE_CASE				= api_doc
																		with open(_SCREAMING_SNAKE_CASE	,			"w"	,			encoding="utf-8"  ) as f:
																								f.write(yaml.dump(_SCREAMING_SNAKE_CASE	,			allow_unicode=_SCREAMING_SNAKE_CASE  )  )
												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__":
						_snake_case				:   Union[str, Any]     =				argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_snake_case				:   Any     =				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 GLPNImageProcessor


class 			lowerCAmelCase (				unittest.TestCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=7					,						UpperCamelCase=3					,						UpperCamelCase=18					,						UpperCamelCase=30					,						UpperCamelCase=400					,						UpperCamelCase=True					,						UpperCamelCase=32					,						UpperCamelCase=True					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= num_channels
								_SCREAMING_SNAKE_CASE				= image_size
								_SCREAMING_SNAKE_CASE				= min_resolution
								_SCREAMING_SNAKE_CASE				= max_resolution
								_SCREAMING_SNAKE_CASE				= do_resize
								_SCREAMING_SNAKE_CASE				= size_divisor
								_SCREAMING_SNAKE_CASE				= do_rescale




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


@require_torch
@require_vision
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Dict       =					GLPNImageProcessor if is_vision_available() else None




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= GLPNImageProcessingTester(self      )




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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_resize"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"size_divisor"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"resample"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_rescale"      )      )




		def     lowercase       (				self      ):
								pass




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

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase					,						numpify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						np.ndarray      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase					,						torchify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						torch.Tensor      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= 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      )