





'''simple docstring'''






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

A   : Optional[int]            =			logging.get_logger(__name__)







@add_end_docstrings(__UpperCAmelCase  )
class 			lowerCamelCase					(						__UpperCAmelCase  ):


					def __init__(					self	:  Optional[Any]       ,     *__snake_case	:  Optional[int]       ,     **__snake_case	:  Any       ):





											'''simple docstring'''





											super().__init__(*__snake_case       ,     **__snake_case       )
											requires_backends(self       ,     'vision'       )
											self.check_model_type(
											    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ,     __snake_case	:  Optional[int]=None       ,     __snake_case	:  Optional[int]=None       ,     __snake_case	:  Any=None       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		{}
											_snake_case:   Optional[int]  					=		{}

											if prompt is not None:
																	_snake_case:   Any  					=		prompt

											if generate_kwargs is not None:
																	_snake_case:   Optional[int]  					=		generate_kwargs
											if max_new_tokens is not None:
																	if "generate_kwargs" not in forward_kwargs:
																							_snake_case:   str  					=		{}
																	if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																							raise ValueError(
																							    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
																							    ' please use only one'       )
																	_snake_case:   List[Any]  					=		max_new_tokens
											return preprocess_params, forward_kwargs, {}


					def __call__(					self	:  List[str]       ,     __snake_case	:  Union[str, List[str], "Image.Image", List["Image.Image"]]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											return super().__call__(__snake_case       ,     **__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  List[Any]       ,     __snake_case	:  Any=None       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		load_image(__snake_case       )

											if prompt is not None:
																	if not isinstance(__snake_case       ,     __snake_case       ):
																							raise ValueError(
																							    f'''Received an invalid text input, got - {type(__snake_case       )} - but expected a single string. '''
																							    'Note also that one single text can be provided for conditional image to text generation.'       )

																	_snake_case:   List[Any]  					=		self.model.config.model_type

																	if model_type == "git":
																							_snake_case:   Any  					=		self.image_processor(images=__snake_case       ,     return_tensors=self.framework       )
																							_snake_case:   Tuple  					=		self.tokenizer(text=__snake_case       ,     add_special_tokens=__snake_case       ).input_ids
																							_snake_case:   Optional[int]  					=		[self.tokenizer.cls_token_id] + input_ids
																							_snake_case:   Optional[int]  					=		torch.tensor(__snake_case       ).unsqueeze(0       )
																							model_inputs.update({'input_ids': input_ids}       )

																	elif model_type == "pix2struct":
																							_snake_case:   Dict  					=		self.image_processor(images=__snake_case       ,     header_text=__snake_case       ,     return_tensors=self.framework       )

																	elif model_type != "vision-encoder-decoder":
																							# vision-encoder-decoder does not support conditional generation
																							_snake_case:   List[str]  					=		self.image_processor(images=__snake_case       ,     return_tensors=self.framework       )
																							_snake_case:   List[Any]  					=		self.tokenizer(__snake_case       ,     return_tensors=self.framework       )
																							model_inputs.update(__snake_case       )

																	else:
																							raise ValueError(f'''Model type {model_type} does not support conditional text generation'''       )

											else:
																	_snake_case:   Optional[int]  					=		self.image_processor(images=__snake_case       ,     return_tensors=self.framework       )

											if self.model.config.model_type == "git" and prompt is None:
																	_snake_case:   Tuple  					=		None

											return model_inputs


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Any=None       ):





											'''simple docstring'''





											if (
											    "input_ids" in model_inputs
											    and isinstance(model_inputs['input_ids']       ,     __snake_case       )
											    and all(x is None for x in model_inputs['input_ids']       )
											):
																	_snake_case:   Union[str, Any]  					=		None

											if generate_kwargs is None:
																	_snake_case:   Optional[int]  					=		{}
											# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
											#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
											#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
											#  in the `_prepare_model_inputs` method.
											_snake_case:   List[Any]  					=		model_inputs.pop(self.model.main_input_name       )
											_snake_case:   Optional[Any]  					=		self.model.generate(__snake_case       ,     **__snake_case       ,     **__snake_case       )
											return model_outputs




					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		[]
											for output_ids in model_outputs:
																	_snake_case:   List[str]  					=		{
																	    'generated_text': self.tokenizer.decode(
																	        __snake_case       ,     skip_special_tokens=__snake_case       ,     )
																	}
																	records.append(__snake_case       )
											return records












'''simple docstring'''






import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A   : Tuple             =			'▁'

A   : Optional[Any]             =			get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
@require_tokenizers
class 			lowerCamelCase					(						__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    BigBirdTokenizer
					_SCREAMING_SNAKE_CASE							   =    BigBirdTokenizerFast
					_SCREAMING_SNAKE_CASE							   =    True
					_SCREAMING_SNAKE_CASE							   =    True


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											super().setUp()

											_snake_case:   Dict  					=		self.tokenizer_class(__snake_case       ,     keep_accents=__snake_case       )
											tokenizer.save_pretrained(self.tmpdirname       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		'<s>'
											_snake_case:   Tuple  					=		1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case       )       ,     __snake_case       )
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case       )       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		list(self.get_tokenizer().get_vocab().keys()       )

											self.assertEqual(vocab_keys[0]       ,     '<unk>'       )
											self.assertEqual(vocab_keys[1]       ,     '<s>'       )
											self.assertEqual(vocab_keys[-1]       ,     '[MASK]'       )
											self.assertEqual(len(__snake_case       )       ,     10_04       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											self.assertEqual(self.get_tokenizer().vocab_size       ,     10_00       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											if not self.test_rust_tokenizer:
																	return

											_snake_case:   Optional[int]  					=		self.get_tokenizer()
											_snake_case:   Union[str, Any]  					=		self.get_rust_tokenizer()

											_snake_case:   List[str]  					=		'I was born in 92000, and this is falsé.'

											_snake_case:   str  					=		tokenizer.tokenize(__snake_case       )
											_snake_case:   Dict  					=		rust_tokenizer.tokenize(__snake_case       )
											self.assertListEqual(__snake_case       ,     __snake_case       )

											_snake_case:   Union[str, Any]  					=		tokenizer.encode(__snake_case       ,     add_special_tokens=__snake_case       )
											_snake_case:   List[Any]  					=		rust_tokenizer.encode(__snake_case       ,     add_special_tokens=__snake_case       )
											self.assertListEqual(__snake_case       ,     __snake_case       )

											_snake_case:   Optional[int]  					=		self.get_rust_tokenizer()
											_snake_case:   int  					=		tokenizer.encode(__snake_case       )
											_snake_case:   Tuple  					=		rust_tokenizer.encode(__snake_case       )
											self.assertListEqual(__snake_case       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Tuple  					=		BigBirdTokenizer(__snake_case       ,     keep_accents=__snake_case       )

											_snake_case:   Optional[int]  					=		tokenizer.tokenize('This is a test'       )
											self.assertListEqual(__snake_case       ,     ['▁This', '▁is', '▁a', '▁t', 'est']       )

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(__snake_case       )       ,     [2_85, 46, 10, 1_70, 3_82]       ,     )

											_snake_case:   Any  					=		tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
											self.assertListEqual(
											    __snake_case       ,     [
											        SPIECE_UNDERLINE + 'I',
											        SPIECE_UNDERLINE + 'was',
											        SPIECE_UNDERLINE + 'b',
											        'or',
											        'n',
											        SPIECE_UNDERLINE + 'in',
											        SPIECE_UNDERLINE + '',
											        '9',
											        '2',
											        '0',
											        '0',
											        '0',
											        ',',
											        SPIECE_UNDERLINE + 'and',
											        SPIECE_UNDERLINE + 'this',
											        SPIECE_UNDERLINE + 'is',
											        SPIECE_UNDERLINE + 'f',
											        'al',
											        's',
											        'é',
											        '.',
											    ]       ,     )
											_snake_case:   List[Any]  					=		tokenizer.convert_tokens_to_ids(__snake_case       )
											self.assertListEqual(
											    __snake_case       ,     [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]       ,     )

											_snake_case:   Optional[Any]  					=		tokenizer.convert_ids_to_tokens(__snake_case       )
											self.assertListEqual(
											    __snake_case       ,     [
											        SPIECE_UNDERLINE + 'I',
											        SPIECE_UNDERLINE + 'was',
											        SPIECE_UNDERLINE + 'b',
											        'or',
											        'n',
											        SPIECE_UNDERLINE + 'in',
											        SPIECE_UNDERLINE + '',
											        '<unk>',
											        '2',
											        '0',
											        '0',
											        '0',
											        ',',
											        SPIECE_UNDERLINE + 'and',
											        SPIECE_UNDERLINE + 'this',
											        SPIECE_UNDERLINE + 'is',
											        SPIECE_UNDERLINE + 'f',
											        'al',
											        's',
											        '<unk>',
											        '.',
											    ]       ,     )


					@cached_property
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		'Hello World!'
											_snake_case:   Optional[int]  					=		[65, 1_85_36, 22_60, 1_01, 66]

											self.assertListEqual(__snake_case       ,     self.big_tokenizer.encode(__snake_case       )       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		(
											    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
											    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
											)
											# fmt: off
											_snake_case:   str  					=		[65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66]  # noqa: E231
											# fmt: on
											self.assertListEqual(__snake_case       ,     self.big_tokenizer.encode(__snake_case       )       )


					@require_torch
					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[int]       ):





											'''simple docstring'''





											import torch

											from transformers import BigBirdConfig, BigBirdModel

											# Build sequence
											_snake_case:   Tuple  					=		list(self.big_tokenizer.get_vocab().keys()       )[:10]
											_snake_case:   Union[str, Any]  					=		' '.join(__snake_case       )
											_snake_case:   Optional[Any]  					=		self.big_tokenizer.encode_plus(__snake_case       ,     return_tensors='pt'       ,     return_token_type_ids=__snake_case       )
											_snake_case:   int  					=		self.big_tokenizer.batch_encode_plus(
											    [sequence + ' ' + sequence]       ,     return_tensors='pt'       ,     return_token_type_ids=__snake_case       )

											_snake_case:   int  					=		BigBirdConfig(attention_type='original_full'       )
											_snake_case:   int  					=		BigBirdModel(__snake_case       )

											assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

											with torch.no_grad():
																	model(**__snake_case       )
																	model(**__snake_case       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   Tuple  					=		BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'       )
											_snake_case:   Optional[Any]  					=		tokenizer.decode(tokenizer('Paris is the [MASK].'       ).input_ids       )

											self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]'       )




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		{'input_ids': [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=__snake_case       ,     model_name='google/bigbird-roberta-base'       ,     revision='215c99f1600e06f83acce68422f2035b2b5c3510'       ,     )





