
"""simple docstring"""





from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		SCREAMING_SNAKE_CASE_					(		SCREAMING_SNAKE_CASE__					):




		"""simple docstring"""





		__lowercase				:    List[str]		  =						['''image_processor''', '''tokenizer''']
		__lowercase				:    Any		  =						'''BlipImageProcessor'''
		__lowercase				:    Optional[int]		  =						('''BertTokenizer''', '''BertTokenizerFast''')




		def __init__(	self				,    lowerCAmelCase__				,    lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE				=					False
				super().__init__(lowerCAmelCase__				,    lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE				=					self.image_processor




		def __call__(	self				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = True				,    lowerCAmelCase__ = False				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = 0				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = False				,    lowerCAmelCase__ = False				,    lowerCAmelCase__ = False				,    lowerCAmelCase__ = False				,    lowerCAmelCase__ = False				,    lowerCAmelCase__ = True				,    lowerCAmelCase__ = None				,    **lowerCAmelCase__				,    ):



				if images is None and text is None:
						raise ValueError("""You have to specify either images or text.""")

				# Get only text
				if images is None:
						__SCREAMING_SNAKE_CASE				=					self.tokenizer
						__SCREAMING_SNAKE_CASE				=					self.tokenizer(
						    text=lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				,    padding=lowerCAmelCase__				,    truncation=lowerCAmelCase__				,    max_length=lowerCAmelCase__				,    stride=lowerCAmelCase__				,    pad_to_multiple_of=lowerCAmelCase__				,    return_attention_mask=lowerCAmelCase__				,    return_overflowing_tokens=lowerCAmelCase__				,    return_special_tokens_mask=lowerCAmelCase__				,    return_offsets_mapping=lowerCAmelCase__				,    return_token_type_ids=lowerCAmelCase__				,    return_length=lowerCAmelCase__				,    verbose=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				,    **lowerCAmelCase__				,    )
						return text_encoding

				# add pixel_values
				__SCREAMING_SNAKE_CASE				=					self.image_processor(lowerCAmelCase__				,    return_tensors=lowerCAmelCase__)

				if text is not None:
						__SCREAMING_SNAKE_CASE				=					self.tokenizer(
						    text=lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				,    padding=lowerCAmelCase__				,    truncation=lowerCAmelCase__				,    max_length=lowerCAmelCase__				,    stride=lowerCAmelCase__				,    pad_to_multiple_of=lowerCAmelCase__				,    return_attention_mask=lowerCAmelCase__				,    return_overflowing_tokens=lowerCAmelCase__				,    return_special_tokens_mask=lowerCAmelCase__				,    return_offsets_mapping=lowerCAmelCase__				,    return_token_type_ids=lowerCAmelCase__				,    return_length=lowerCAmelCase__				,    verbose=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				,    **lowerCAmelCase__				,    )
				else:
						__SCREAMING_SNAKE_CASE				=					None

				if text_encoding is not None:
						encoding_image_processor.update(lowerCAmelCase__)

				return encoding_image_processor




		def       snake_case_	(	self				,    *lowerCAmelCase__				,    **lowerCAmelCase__):
				return self.tokenizer.batch_decode(*lowerCAmelCase__				,    **lowerCAmelCase__)




		def       snake_case_	(	self				,    *lowerCAmelCase__				,    **lowerCAmelCase__):
				return self.tokenizer.decode(*lowerCAmelCase__				,    **lowerCAmelCase__)




		@property
		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					self.tokenizer.model_input_names
				__SCREAMING_SNAKE_CASE				=					self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))










from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
				import tensorflow as tf

				from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class       a_       (     unittest.TestCase     ):





	@slow
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('google/mt5-small'      )

				SCREAMING_SNAKE_CASE_								=						tokenizer('Hello there'      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						tokenizer('Hi I am'      ,     return_tensors='tf'      ).input_ids

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      ).loss
				SCREAMING_SNAKE_CASE_								=						-tf.math.reduce_mean(SCREAMING_SNAKE_CASE      ).numpy()

				SCREAMING_SNAKE_CASE_								=						-2_1.2_2_8_1_6_8
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )
