import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:





					class       lowercase__		:


											@staticmethod
											def  __A ( *UpperCamelCase__			:       Tuple					,	**UpperCamelCase__			:       List[str]		):





																	'''simple docstring'''





																	pass





@is_pipeline_test
@require_torch
@require_vision
class       lowercase__		(     unittest.TestCase):
						UpperCamelCase_  =	MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							pipeline('''visual-question-answering'''					,	model='''hf-internal-testing/tiny-vilt-random-vqa'''		)
												SCREAMING_SNAKE_CASE							:					Any           =							[
												    {
												        '''image''': Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		),
												        '''question''': '''How many cats are there?''',
												    },
												    {
												        '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
												        '''question''': '''How many cats are there?''',
												    },
												]
												return vqa_pipeline, examples


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							vqa_pipeline(UpperCamelCase__					,	top_k=1		)
												self.assertEqual(
												    UpperCamelCase__					,	[
												        [{'''score''': ANY(UpperCamelCase__		), '''answer''': ANY(UpperCamelCase__		)}],
												        [{'''score''': ANY(UpperCamelCase__		), '''answer''': ANY(UpperCamelCase__		)}],
												    ]					,	)


						@require_torch
						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							pipeline('''visual-question-answering'''					,	model='''hf-internal-testing/tiny-vilt-random-vqa'''		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							'''How many cats are there?'''

												SCREAMING_SNAKE_CASE							:					Tuple           =							vqa_pipeline(image=UpperCamelCase__					,	question='''How many cats are there?'''					,	top_k=2		)
												self.assertEqual(
												    UpperCamelCase__					,	[{'''score''': ANY(UpperCamelCase__		), '''answer''': ANY(UpperCamelCase__		)}, {'''score''': ANY(UpperCamelCase__		), '''answer''': ANY(UpperCamelCase__		)}]		)

												SCREAMING_SNAKE_CASE							:					Any           =							vqa_pipeline({'''image''': image, '''question''': question}					,	top_k=2		)
												self.assertEqual(
												    UpperCamelCase__					,	[{'''score''': ANY(UpperCamelCase__		), '''answer''': ANY(UpperCamelCase__		)}, {'''score''': ANY(UpperCamelCase__		), '''answer''': ANY(UpperCamelCase__		)}]		)


						@slow
						@require_torch
						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							pipeline('''visual-question-answering'''					,	model='''dandelin/vilt-b32-finetuned-vqa'''		)
												SCREAMING_SNAKE_CASE							:					str           =							'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''How many cats are there?'''

												SCREAMING_SNAKE_CASE							:					Dict           =							vqa_pipeline(image=UpperCamelCase__					,	question=UpperCamelCase__					,	top_k=2		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__					,	decimals=4		)					,	[{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}]		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							vqa_pipeline({'''image''': image, '''question''': question}					,	top_k=2		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__					,	decimals=4		)					,	[{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}]		)

												SCREAMING_SNAKE_CASE							:					Any           =							vqa_pipeline(
												    [{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}]					,	top_k=2		)
												self.assertEqual(
												    nested_simplify(UpperCamelCase__					,	decimals=4		)					,	[[{'''score''': 0.8799, '''answer''': '''2'''}, {'''score''': 0.296, '''answer''': '''1'''}]] * 2					,	)



						@require_tf
						@unittest.skip('''Visual question answering not implemented in TF'''		)
						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												pass






import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_ta import TaTokenizer
else:
					__UpperCamelCase	: Dict    		=  None


__UpperCamelCase	: Tuple    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[int]    		=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase	: Optional[int]    		=  {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__UpperCamelCase	: Union[str, Any]    		=  {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	VOCAB_FILES_NAMES
						UpperCamelCase_  =	PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_  =	["""input_ids""", """attention_mask"""]
						UpperCamelCase_  =	TaTokenizer

						UpperCamelCase_  =	[]


						def __init__( self			:       str					,	UpperCamelCase__			:       Optional[int]=None					,	UpperCamelCase__			:       Dict=None					,	UpperCamelCase__			:       str="</s>"					,	UpperCamelCase__			:       str="<unk>"					,	UpperCamelCase__			:       Optional[int]="<pad>"					,	UpperCamelCase__			:       Optional[Any]=100					,	UpperCamelCase__			:       List[Any]=None					,	**UpperCamelCase__			:       str					,	):





												'''simple docstring'''





												if extra_ids > 0 and additional_special_tokens is None:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[f"""<extra_id_{i}>""" for i in range(UpperCamelCase__		)]
												elif extra_ids > 0 and additional_special_tokens is not None:
																		# Check that we have the right number of extra special tokens
																		SCREAMING_SNAKE_CASE							:					int           =							len(set(filter(lambda UpperCamelCase__		: bool('''extra_id_''' in str(UpperCamelCase__		)		)					,	UpperCamelCase__		)		)		)
																		if extra_tokens != extra_ids:
																								raise ValueError(
																								    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																								    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																								    ''' tokens'''		)

												super().__init__(
												    UpperCamelCase__					,	tokenizer_file=UpperCamelCase__					,	eos_token=UpperCamelCase__					,	unk_token=UpperCamelCase__					,	pad_token=UpperCamelCase__					,	extra_ids=UpperCamelCase__					,	additional_special_tokens=UpperCamelCase__					,	**UpperCamelCase__					,	)

												SCREAMING_SNAKE_CASE							:					str           =							vocab_file
												SCREAMING_SNAKE_CASE							:					int           =							False if not self.vocab_file else True
												SCREAMING_SNAKE_CASE							:					str           =							extra_ids


						@staticmethod
						def  __A ( UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
																		if init_max_model_length is not None and init_max_model_length != max_model_length:
																								return init_max_model_length
																		elif init_max_model_length is None:
																								warnings.warn(
																								    '''This tokenizer was incorrectly instantiated with a model max length of'''
																								    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																								    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																								    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																								    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
																								    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																								    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																								    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																								    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''					,	UpperCamelCase__					,	)

												return max_model_length


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Optional[str] = None		):





												'''simple docstring'''





												if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''		)

												if not os.path.isdir(UpperCamelCase__		):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
																		return
												SCREAMING_SNAKE_CASE							:					Any           =							os.path.join(
												    UpperCamelCase__					,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCamelCase__		):
																		copyfile(self.vocab_file					,	UpperCamelCase__		)
																		logger.info(f"""Copy vocab file to {out_vocab_file}"""		)

												return (out_vocab_file,)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																		return self.prefix_tokens + token_ids_a
												else:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							token_ids_a + [self.eos_token_id]
																		return self.prefix_tokens + token_ids_a + token_ids_a


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[self.eos_token_id]

												if token_ids_a is None:
																		return len(token_ids_a + eos		) * [0]
												return len(token_ids_a + eos + token_ids_a + eos		) * [0]


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												return list(
												    set(filter(lambda UpperCamelCase__		: bool(re.search(r'''<extra_id_\d+>'''					,	UpperCamelCase__		)		) is not None					,	self.additional_special_tokens		)		)		)



						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												return [self.convert_tokens_to_ids(UpperCamelCase__		) for token in self.get_sentinel_tokens()]





