





'''simple docstring'''




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   ["image_processor", "tokenizer"]
     snake_case_						      =   "BridgeTowerImageProcessor"
     snake_case_						      =   ("RobertaTokenizer", "RobertaTokenizerFast")



     def __init__(   self							:			Tuple   ,       __snake_case							:			Tuple   ,       __snake_case							:			Optional[int]						)->	Union[str, Any]:
        super().__init__(__snake_case   ,       __snake_case						)



     def __call__(   self							:			Dict   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,       __snake_case							:			bool = True   ,       __snake_case							:			Union[bool, str, PaddingStrategy] = False   ,       __snake_case							:			Union[bool, str, TruncationStrategy] = None   ,       __snake_case							:			Optional[int] = None   ,       __snake_case							:			int = 0   ,       __snake_case							:			Optional[int] = None   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = False   ,       __snake_case							:			bool = True   ,       __snake_case							:			Optional[Union[str, TensorType]] = None   ,       **__snake_case							:			int   ,       )->	BatchEncoding:
        snake_case 						=   self.tokenizer(
            text=__snake_case   ,       add_special_tokens=__snake_case   ,       padding=__snake_case   ,       truncation=__snake_case   ,       max_length=__snake_case   ,       stride=__snake_case   ,       pad_to_multiple_of=__snake_case   ,       return_token_type_ids=__snake_case   ,       return_attention_mask=__snake_case   ,       return_overflowing_tokens=__snake_case   ,       return_special_tokens_mask=__snake_case   ,       return_offsets_mapping=__snake_case   ,       return_length=__snake_case   ,       verbose=__snake_case   ,       return_tensors=__snake_case   ,       **__snake_case   ,       )
        # add pixel_values + pixel_mask
        snake_case 						=   self.image_processor(
            __snake_case   ,       return_tensors=__snake_case   ,       do_normalize=__snake_case   ,       do_center_crop=__snake_case   ,       **__snake_case						)
        encoding.update(__snake_case						)

        return encoding



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       *__snake_case							:			Union[str, Any]   ,       **__snake_case							:			List[str]						)->	List[str]:
        return self.tokenizer.batch_decode(*__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Dict   ,       *__snake_case							:			str   ,       **__snake_case							:			int						)->	Optional[int]:
        return self.tokenizer.decode(*__snake_case   ,       **__snake_case						)



     @property
     def      lowerCAmelCase			(   self							:			int						)->	Tuple:
        snake_case 						=   self.tokenizer.model_input_names
        snake_case 						=   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)








'''simple docstring'''




import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     @slow
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Optional[Any]:
        snake_case 						=   FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""						)
        snake_case 						=   AutoTokenizer.from_pretrained("""google/mt5-small"""						)

        snake_case 						=   tokenizer("""Hello there"""   ,       return_tensors="""np"""						).input_ids
        snake_case 						=   tokenizer("""Hi I am"""   ,       return_tensors="""np"""						).input_ids

        snake_case 						=   shift_tokens_right(__snake_case   ,       model.config.pad_token_id   ,       model.config.decoder_start_token_id						)

        snake_case 						=   model(__snake_case   ,       decoder_input_ids=__snake_case						).logits
        snake_case 						=   optax.softmax_cross_entropy(__snake_case   ,       onehot(__snake_case   ,       logits.shape[-1]						)						).mean()

        snake_case 						=   -(labels.shape[-1] * loss.item())

        snake_case 						=   -84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1e-4						)

