


import argparse
from collections import defaultdict

import yaml


_lowerCAmelCase:						int   				=       'docs/source/en/_toctree.yml'






def   _lowercase(					__a				:						Union[str, Any]     ):
							a__      =defaultdict(__a     )
							for doc in model_doc:
														counts[doc["local"]] += 1
							a__      =[key for key, value in counts.items() if value > 1]

							a__      =[]
							for duplicate_key in duplicates:
														a__      =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   _lowercase(					__a				:						List[Any]=False     ):
							with open(__a    ,     encoding='utf-8'     ) as f:
														a__      =yaml.safe_load(f.read()     )

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

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

							a__      =api_doc[model_idx]['sections']

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

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

							if diff:
														if overwrite:
																					a__      =model_doc
																					a__      =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:						List[str]   				=       argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	_lowerCAmelCase:						List[Any]   				=       parser.parse_args()

	check_model_doc(args.fix_and_overwrite)





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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class       lowercase_					(unittest.TestCase   ):





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =tempfile.mkdtemp()

											a__      =BlipImageProcessor()
											a__      =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel')

											a__      =BlipProcessor(lowercase_       ,  lowercase_)

											processor.save_pretrained(self.tmpdirname)





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> str:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).tokenizer





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> List[str]:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).image_processor





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											shutil.rmtree(self.tmpdirname)





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =[np.random.randint(255       ,  size=(3, 30, 400)       ,  dtype=np.uinta)]

											a__      =[Image.fromarray(np.moveaxis(lowercase_       ,  0       ,  -1)) for x in image_inputs]

											return image_inputs





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =BlipProcessor(tokenizer=self.get_tokenizer()       ,  image_processor=self.get_image_processor())
											processor.save_pretrained(self.tmpdirname)

											a__      =self.get_tokenizer(bos_token='(BOS)'       ,  eos_token='(EOS)')
											a__      =self.get_image_processor(do_normalize=lowercase_       ,  padding_value=1.0)

											a__      =BlipProcessor.from_pretrained(
											    self.tmpdirname       ,  bos_token='(BOS)'       ,  eos_token='(EOS)'       ,  do_normalize=lowercase_       ,  padding_value=1.0)

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

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





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      =self.prepare_image_inputs()

											a__      =image_processor(lowercase_       ,  return_tensors='np')
											a__      =processor(images=lowercase_       ,  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 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'

											a__      =processor(text=lowercase_)

											a__      =tokenizer(lowercase_       ,  return_token_type_ids=lowercase_)

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





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'
											a__      =self.prepare_image_inputs()

											a__      =processor(text=lowercase_       ,  images=lowercase_)

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

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





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											a__      =processor.batch_decode(lowercase_)
											a__      =tokenizer.batch_decode(lowercase_)

											self.assertListEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'
											a__      =self.prepare_image_inputs()

											a__      =processor(text=lowercase_       ,  images=lowercase_)

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

