


'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class        lowercase     (							unittest.TestCase ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			tempfile.mkdtemp()

							UpperCamelCase__       :Union[str, Any]							 =			BlipImageProcessor()
							UpperCamelCase__       :List[Any]							 =			GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''							)

							UpperCamelCase__       :int							 =			BlipaProcessor(UpperCamelCase_   ,       UpperCamelCase_							)

							processor.save_pretrained(self.tmpdirname							)




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							return AutoProcessor.from_pretrained(self.tmpdirname   ,       **UpperCamelCase_							).tokenizer




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							return AutoProcessor.from_pretrained(self.tmpdirname   ,       **UpperCamelCase_							).image_processor




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							shutil.rmtree(self.tmpdirname							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[int]							 =			[np.random.randint(255   ,       size=(3, 30, 400)   ,       dtype=np.uinta							)]

							UpperCamelCase__       :Any							 =			[Image.fromarray(np.moveaxis(UpperCamelCase_   ,       0   ,       -1							)							) for x in image_inputs]

							return image_inputs




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[int]							 =			BlipaProcessor(tokenizer=self.get_tokenizer()   ,       image_processor=self.get_image_processor()							)
							processor.save_pretrained(self.tmpdirname							)

							UpperCamelCase__       :List[str]							 =			self.get_tokenizer(bos_token='''(BOS)'''   ,       eos_token='''(EOS)'''							)
							UpperCamelCase__       :str							 =			self.get_image_processor(do_normalize=UpperCamelCase_   ,       padding_value=1.0							)

							UpperCamelCase__       :List[Any]							 =			BlipaProcessor.from_pretrained(
							    self.tmpdirname   ,       bos_token='''(BOS)'''   ,       eos_token='''(EOS)'''   ,       do_normalize=UpperCamelCase_   ,       padding_value=1.0							)

							self.assertEqual(processor.tokenizer.get_vocab()   ,       tokenizer_add_kwargs.get_vocab()							)
							self.assertIsInstance(processor.tokenizer   ,       UpperCamelCase_							)

							self.assertEqual(processor.image_processor.to_json_string()   ,       image_processor_add_kwargs.to_json_string()							)
							self.assertIsInstance(processor.image_processor   ,       UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.get_image_processor()
							UpperCamelCase__       :Any							 =			self.get_tokenizer()

							UpperCamelCase__       :str							 =			BlipaProcessor(tokenizer=UpperCamelCase_   ,       image_processor=UpperCamelCase_							)

							UpperCamelCase__       :Tuple							 =			self.prepare_image_inputs()

							UpperCamelCase__       :Dict							 =			image_processor(UpperCamelCase_   ,       return_tensors='''np'''							)
							UpperCamelCase__       :Optional[Any]							 =			processor(images=UpperCamelCase_   ,       return_tensors='''np'''							)

							for key in input_feat_extract.keys():
												self.assertAlmostEqual(input_feat_extract[key].sum()   ,       input_processor[key].sum()   ,       delta=1e-2							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			self.get_image_processor()
							UpperCamelCase__       :Union[str, Any]							 =			self.get_tokenizer()

							UpperCamelCase__       :Dict							 =			BlipaProcessor(tokenizer=UpperCamelCase_   ,       image_processor=UpperCamelCase_							)

							UpperCamelCase__       :str							 =			'''lower newer'''

							UpperCamelCase__       :Dict							 =			processor(text=UpperCamelCase_							)

							UpperCamelCase__       :Union[str, Any]							 =			tokenizer(UpperCamelCase_   ,       return_token_type_ids=UpperCamelCase_							)

							for key in encoded_tok.keys():
												self.assertListEqual(encoded_tok[key]   ,       encoded_processor[key]							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			self.get_image_processor()
							UpperCamelCase__       :Optional[Any]							 =			self.get_tokenizer()

							UpperCamelCase__       :Optional[int]							 =			BlipaProcessor(tokenizer=UpperCamelCase_   ,       image_processor=UpperCamelCase_							)

							UpperCamelCase__       :str							 =			'''lower newer'''
							UpperCamelCase__       :Optional[Any]							 =			self.prepare_image_inputs()

							UpperCamelCase__       :Any							 =			processor(text=UpperCamelCase_   ,       images=UpperCamelCase_							)

							self.assertListEqual(list(inputs.keys()							)   ,       ['''pixel_values''', '''input_ids''', '''attention_mask''']							)

							# test if it raises when no input is passed
							with pytest.raises(UpperCamelCase_							):
												processor()




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self.get_image_processor()
							UpperCamelCase__       :str							 =			self.get_tokenizer()

							UpperCamelCase__       :Dict							 =			BlipaProcessor(tokenizer=UpperCamelCase_   ,       image_processor=UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase__       :Tuple							 =			processor.batch_decode(UpperCamelCase_							)
							UpperCamelCase__       :List[Any]							 =			tokenizer.batch_decode(UpperCamelCase_							)

							self.assertListEqual(UpperCamelCase_   ,       UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			self.get_image_processor()
							UpperCamelCase__       :List[Any]							 =			self.get_tokenizer()

							UpperCamelCase__       :List[str]							 =			BlipaProcessor(tokenizer=UpperCamelCase_   ,       image_processor=UpperCamelCase_							)

							UpperCamelCase__       :str							 =			'''lower newer'''
							UpperCamelCase__       :Optional[Any]							 =			self.prepare_image_inputs()

							UpperCamelCase__       :List[Any]							 =			processor(text=UpperCamelCase_   ,       images=UpperCamelCase_							)

							# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
							self.assertListEqual(list(inputs.keys()							)   ,       ['''pixel_values''', '''input_ids''', '''attention_mask''']							)



'''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)