



"""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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  Tuple					)-> Optional[Any]:
								lowerCamelCase__		:		Any		     =tempfile.mkdtemp()

								lowerCamelCase__		:		Dict		     =BlipImageProcessor()
								lowerCamelCase__		:		Optional[int]		     =BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel'''					)

								lowerCamelCase__		:		Optional[int]		     =BlipProcessor(lowerCamelCase,  lowerCamelCase					)

								processor.save_pretrained(self.tmpdirname					)



					def 		snake_case	(	self							:  Union[str, Any],  **lowerCamelCase							:  str					)-> Any:
								return AutoProcessor.from_pretrained(self.tmpdirname,  **lowerCamelCase					).tokenizer



					def 		snake_case	(	self							:  List[str],  **lowerCamelCase							:  List[Any]					)-> Union[str, Any]:
								return AutoProcessor.from_pretrained(self.tmpdirname,  **lowerCamelCase					).image_processor



					def 		snake_case	(	self							:  Tuple					)-> Dict:
								shutil.rmtree(self.tmpdirname					)



					def 		snake_case	(	self							:  List[Any]					)-> str:
								lowerCamelCase__		:		str		     =[np.random.randint(255,  size=(3, 30, 400),  dtype=np.uinta					)]

								lowerCamelCase__		:		Union[str, Any]		     =[Image.fromarray(np.moveaxis(lowerCamelCase,  0,  -1					)					) for x in image_inputs]

								return image_inputs



					def 		snake_case	(	self							:  Tuple					)-> Union[str, Any]:
								lowerCamelCase__		:		List[Any]		     =BlipProcessor(tokenizer=self.get_tokenizer(),  image_processor=self.get_image_processor()					)
								processor.save_pretrained(self.tmpdirname					)

								lowerCamelCase__		:		Union[str, Any]		     =self.get_tokenizer(bos_token='''(BOS)''',  eos_token='''(EOS)'''					)
								lowerCamelCase__		:		Optional[Any]		     =self.get_image_processor(do_normalize=lowerCamelCase,  padding_value=1.0					)

								lowerCamelCase__		:		Dict		     =BlipProcessor.from_pretrained(
								    self.tmpdirname,  bos_token='''(BOS)''',  eos_token='''(EOS)''',  do_normalize=lowerCamelCase,  padding_value=1.0					)

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

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



					def 		snake_case	(	self							:  Tuple					)-> Any:
								lowerCamelCase__		:		List[str]		     =self.get_image_processor()
								lowerCamelCase__		:		Dict		     =self.get_tokenizer()

								lowerCamelCase__		:		Union[str, Any]		     =BlipProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)

								lowerCamelCase__		:		List[Any]		     =self.prepare_image_inputs()

								lowerCamelCase__		:		Optional[int]		     =image_processor(lowerCamelCase,  return_tensors='''np'''					)
								lowerCamelCase__		:		Dict		     =processor(images=lowerCamelCase,  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 		snake_case	(	self							:  Optional[Any]					)-> Optional[Any]:
								lowerCamelCase__		:		List[Any]		     =self.get_image_processor()
								lowerCamelCase__		:		str		     =self.get_tokenizer()

								lowerCamelCase__		:		List[Any]		     =BlipProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)

								lowerCamelCase__		:		Optional[int]		     ='''lower newer'''

								lowerCamelCase__		:		Dict		     =processor(text=lowerCamelCase					)

								lowerCamelCase__		:		Union[str, Any]		     =tokenizer(lowerCamelCase,  return_token_type_ids=lowerCamelCase					)

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



					def 		snake_case	(	self							:  Optional[int]					)-> Dict:
								lowerCamelCase__		:		Any		     =self.get_image_processor()
								lowerCamelCase__		:		List[str]		     =self.get_tokenizer()

								lowerCamelCase__		:		Dict		     =BlipProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)

								lowerCamelCase__		:		Optional[int]		     ='''lower newer'''
								lowerCamelCase__		:		Tuple		     =self.prepare_image_inputs()

								lowerCamelCase__		:		List[Any]		     =processor(text=lowerCamelCase,  images=lowerCamelCase					)

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

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



					def 		snake_case	(	self							:  Dict					)-> Optional[int]:
								lowerCamelCase__		:		Tuple		     =self.get_image_processor()
								lowerCamelCase__		:		int		     =self.get_tokenizer()

								lowerCamelCase__		:		Optional[int]		     =BlipProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)

								lowerCamelCase__		:		Optional[int]		     =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								lowerCamelCase__		:		Tuple		     =processor.batch_decode(lowerCamelCase					)
								lowerCamelCase__		:		Union[str, Any]		     =tokenizer.batch_decode(lowerCamelCase					)

								self.assertListEqual(lowerCamelCase,  lowerCamelCase					)




					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__		:		Any		     =self.get_image_processor()
								lowerCamelCase__		:		str		     =self.get_tokenizer()

								lowerCamelCase__		:		str		     =BlipProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase					)

								lowerCamelCase__		:		int		     ='''lower newer'''
								lowerCamelCase__		:		int		     =self.prepare_image_inputs()

								lowerCamelCase__		:		List[Any]		     =processor(text=lowerCamelCase,  images=lowerCamelCase					)

								# 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 os
from collections import deque

import torch
from torch.utils.data import Dataset

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					def __init__(	self							:  List[Any],  lowerCamelCase							:  Dict="",  lowerCamelCase							:  Tuple="train"					)-> Dict:
								assert os.path.isdir(lowerCamelCase					)

								lowerCamelCase__		:		Optional[Any]		     =[]
								lowerCamelCase__		:		Dict		     =os.listdir(lowerCamelCase					)
								for story_filename in story_filenames_list:
											if "summary" in story_filename:
														continue
											lowerCamelCase__		:		Optional[int]		     =os.path.join(lowerCamelCase,  lowerCamelCase					)
											if not os.path.isfile(lowerCamelCase					):
														continue
											self.documents.append(lowerCamelCase					)



					def __len__(	self							:  Optional[Any]					)-> List[str]:
								return len(self.documents					)




					def __getitem__(	self							:  List[str],  lowerCamelCase							:  Dict					)-> str:
								lowerCamelCase__		:		int		     =self.documents[idx]
								lowerCamelCase__		:		List[Any]		     =document_path.split('''/'''					)[-1]
								with open(lowerCamelCase,  encoding='''utf-8'''					) as source:
											lowerCamelCase__		:		Optional[int]		     =source.read()
											lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =process_story(lowerCamelCase					)
								return document_name, story_lines, summary_lines






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]		     =list(filter(lambda __lowerCamelCase     : len(__lowerCamelCase     ) != 0							,		[line.strip() for line in raw_story.split('''\n'''     )]     )     )

			# for some unknown reason some lines miss a period, add it
			lowerCamelCase__		:		Dict		     =[_add_missing_period(__lowerCamelCase     ) for line in nonempty_lines]

			# gather article lines
			lowerCamelCase__		:		Union[str, Any]		     =[]
			lowerCamelCase__		:		Optional[Any]		     =deque(__lowerCamelCase     )
			while True:
						try:
									lowerCamelCase__		:		Tuple		     =lines.popleft()
									if element.startswith('''@highlight'''     ):
												break
									story_lines.append(__lowerCamelCase     )
						except IndexError:
									# if "@highlight" is absent from the file we pop
									# all elements until there is None, raising an exception.
									return story_lines, []

    # gather summary lines
			lowerCamelCase__		:		Dict		     =list(filter(lambda __lowerCamelCase     : not t.startswith('''@highlight'''     )							,		__lowerCamelCase     )     )

			return story_lines, summary_lines






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
			if line.startswith('''@highlight'''     ):
						return line
			if line[-1] in END_TOKENS:
						return line
			return line + "."






def 					snake_case__       (		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   List[Any]     ):
			"""simple docstring"""
			if len(__lowerCamelCase     ) > block_size:
						return sequence[:block_size]
			else:
						sequence.extend([pad_token_id] * (block_size - len(__lowerCamelCase     ))     )
						return sequence






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			lowerCamelCase__		:		int		     =torch.ones_like(__lowerCamelCase     )
			lowerCamelCase__		:		Any		     =sequence == pad_token_id
			lowerCamelCase__		:		List[str]		     =0
			return mask






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Optional[Any]							,		__lowerCamelCase    :   Optional[Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Dict		     =[tokenizer.encode(__lowerCamelCase     ) for line in story_lines]
			lowerCamelCase__		:		List[Any]		     =[token for sentence in story_lines_token_ids for token in sentence]
			lowerCamelCase__		:		List[Any]		     =[tokenizer.encode(__lowerCamelCase     ) for line in summary_lines]
			lowerCamelCase__		:		Optional[int]		     =[token for sentence in summary_lines_token_ids for token in sentence]

			return story_token_ids, summary_token_ids






def 					snake_case__       (		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Optional[int]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     =[]
			for sequence in batch:
						lowerCamelCase__		:		Optional[int]		     =-1
						lowerCamelCase__		:		List[str]		     =[]
						for s in sequence:
									if s == separator_token_id:
												sentence_num += 1
									embeddings.append(sentence_num % 2     )
						batch_embeddings.append(__lowerCamelCase     )
			return torch.tensor(__lowerCamelCase     )




