


'''simple docstring'''
import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:
		class        lowercase     :

				"""simple docstring"""

				@staticmethod
				def    lowerCAmelCase__    (		*UpperCamelCase_   ,       **UpperCamelCase_							):

									'''simple docstring'''


									pass


def       a				(      __a       )  ->     str:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			hashlib.mda(image.tobytes()       )
					return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        lowercase     (							unittest.TestCase ):

		"""simple docstring"""

		_a   			=    MODEL_FOR_DEPTH_ESTIMATION_MAPPING




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			DepthEstimationPipeline(model=UpperCamelCase_   ,       image_processor=UpperCamelCase_							)
							return depth_estimator, [
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							    "./tests/fixtures/tests_samples/COCO/000000039769.png",
							]




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							self.assertEqual({'''predicted_depth''': ANY(torch.Tensor							), '''depth''': ANY(Image.Image							)}   ,       UpperCamelCase_							)
							import datasets

							UpperCamelCase__       :Optional[int]							 =			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''   ,       '''image'''   ,       split='''test'''							)
							UpperCamelCase__       :Any							 =			depth_estimator(
							    [
							        Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							),
							        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
							        # RGBA
							        dataset[0]['''file'''],
							        # LA
							        dataset[1]['''file'''],
							        # L
							        dataset[2]['''file'''],
							    ]							)
							self.assertEqual(
							    [
							        {'''predicted_depth''': ANY(torch.Tensor							), '''depth''': ANY(Image.Image							)},
							        {'''predicted_depth''': ANY(torch.Tensor							), '''depth''': ANY(Image.Image							)},
							        {'''predicted_depth''': ANY(torch.Tensor							), '''depth''': ANY(Image.Image							)},
							        {'''predicted_depth''': ANY(torch.Tensor							), '''depth''': ANY(Image.Image							)},
							        {'''predicted_depth''': ANY(torch.Tensor							), '''depth''': ANY(Image.Image							)},
							    ]   ,       UpperCamelCase_   ,       )




		@require_tf
		@unittest.skip('''Depth estimation is not implemented in TF'''							)
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		@slow
		@require_torch
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			'''Intel/dpt-large'''
							UpperCamelCase__       :Optional[int]							 =			pipeline('''depth-estimation'''   ,       model=UpperCamelCase_							)
							UpperCamelCase__       :List[Any]							 =			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
							UpperCamelCase__       :str							 =			hashimage(outputs['''depth''']							)

							# This seems flaky.
							# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
							self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()							)   ,       29.304							)
							self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()							)   ,       2.662							)




		@require_torch
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''							)



'''simple docstring'''
import argparse
from collections import defaultdict

import yaml


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


def       a				(      __a       )  ->     int:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			defaultdict(__a       )
					UpperCamelCase__       :int							 =			[]
					UpperCamelCase__       :int							 =			[]
					for doc in doc_list:
										if "local" in doc:
															counts[doc["local"]] += 1

										if doc["title"].lower() == "overview":
															overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']}       )
										else:
															new_doc_list.append(__a       )

					UpperCamelCase__       :Union[str, Any]							 =			new_doc_list
					UpperCamelCase__       :Tuple							 =			[key for key, value in counts.items() if value > 1]

					UpperCamelCase__       :Union[str, Any]							 =			[]
					for duplicate_key in duplicates:
										UpperCamelCase__       :Dict							 =			list({doc['''title'''] for doc in doc_list 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 doc_list if '''local''' not in counts or counts[doc['''local''']] == 1]       )
					UpperCamelCase__       :Union[str, Any]							 =			sorted(__a			, key=lambda __a       : s["title"].lower()       )

					# "overview" gets special treatment and is always first
					if len(__a       ) > 1:
										raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.'''       )

					overview_doc.extend(__a       )

					# Sort
					return overview_doc


def       a				(      __a=False       )  ->     Any:



					'''simple docstring'''






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

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

					# Then to the model doc
					UpperCamelCase__       :Optional[Any]							 =			0
					while api_doc[scheduler_idx]["title"] != "Schedulers":
										scheduler_idx += 1

					UpperCamelCase__       :List[Any]							 =			api_doc[scheduler_idx]['''sections''']
					UpperCamelCase__       :Union[str, Any]							 =			clean_doc_toc(__a       )

					UpperCamelCase__       :List[Any]							 =			False
					if new_scheduler_doc != scheduler_doc:
										UpperCamelCase__       :Optional[int]							 =			True
										if overwrite:
															UpperCamelCase__       :Dict							 =			new_scheduler_doc

					if diff:
										if overwrite:
															UpperCamelCase__       :Any							 =			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.'''       )


def       a				(      __a=False       )  ->     Optional[Any]:



					'''simple docstring'''






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

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

					# Then to the model doc
					UpperCamelCase__       :str							 =			0
					while api_doc[pipeline_idx]["title"] != "Pipelines":
										pipeline_idx += 1

					UpperCamelCase__       :Any							 =			False
					UpperCamelCase__       :Union[str, Any]							 =			api_doc[pipeline_idx]['''sections''']
					UpperCamelCase__       :Tuple							 =			[]

					# sort sub pipeline docs
					for pipeline_doc in pipeline_docs:
										if "section" in pipeline_doc:
															UpperCamelCase__       :Dict							 =			pipeline_doc['''section''']
															UpperCamelCase__       :Optional[Any]							 =			clean_doc_toc(__a       )
															if overwrite:
																				UpperCamelCase__       :Optional[int]							 =			new_sub_pipeline_doc
										new_pipeline_docs.append(__a       )

					# sort overall pipeline doc
					UpperCamelCase__       :Optional[Any]							 =			clean_doc_toc(__a       )

					if new_pipeline_docs != pipeline_docs:
										UpperCamelCase__       :int							 =			True
										if overwrite:
															UpperCamelCase__       :Union[str, Any]							 =			new_pipeline_docs

					if diff:
										if overwrite:
															UpperCamelCase__       :Dict							 =			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__":
		__snake_case											=					argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		__snake_case											=					parser.parse_args()

		check_scheduler_doc(args.fix_and_overwrite)
		check_pipeline_doc(args.fix_and_overwrite)