

import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer






class      lowerCamelCase_      (	tf.keras.layers.Layer	):




	def __init__(     self						:     List[str]					,      __A						:     Dict[str, int]					,      __A						:     List[str]					,      __A						:     int = None					,      __A						:     int = None							):
		super().__init__()
		__A :			Optional[Any]		    =			pad_token_id
		__A :			Any		    =			max_length
		__A :			Any		    =			vocab
		__A :			int		    =			merges
		__A :			int		    =			BytePairTokenizer(__A					,      __A					,      sequence_length=__A							)




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Dict					,      __A						:     GPTaTokenizer					,      *__A						:     int					,      **__A						:     str							):
		__A :			Optional[int]		    =			[""" """.join(__A							) for m in tokenizer.bpe_ranks.keys()]
		__A :			Tuple		    =			tokenizer.get_vocab()
		return cls(__A					,      __A					,      *__A					,      **__A							)




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Tuple					,      __A						:     Union[str, os.PathLike]					,      *__A						:     Any					,      **__A						:     Optional[Any]							):
		__A :			Any		    =			GPTaTokenizer.from_pretrained(__A					,      *__A					,      **__A							)
		return cls.from_tokenizer(__A					,      *__A					,      **__A							)




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Optional[int]					,      __A						:     Union[str, Any]							):
		return cls(**__A							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		return {
		    "vocab": self.vocab,
		    "merges": self.merges,
		    "max_length": self.max_length,
		    "pad_token_id": self.pad_token_id,
		}




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     Optional[Any]					,      __A						:     int = None							):
		__A :			Tuple		    =			self.tf_tokenizer(__A							)
		__A :			Tuple		    =			tf.ones_like(__A							)

		if self.pad_token_id is not None:
			# pad the tokens up to max length
			__A :			Any		    =			max_length if max_length is not None else self.max_length

			if max_length is not None:
				__A    ,				__A :			Optional[int]		    =			pad_model_inputs(
				    __A					,      max_seq_length=__A					,      pad_value=self.pad_token_id							)

		return {"attention_mask": attention_mask, "input_ids": input_ids}












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   lowerCAmelCase				(   unittest.TestCase     ):
 @slow
 def 	A_      ( self    : int      )     ->      Optional[Any]:
     lowerCamelCase__       : Tuple   					=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
     lowerCamelCase__       : Any   					=						AutoTokenizer.from_pretrained('google/mt5-small'      )

     lowerCamelCase__       : int   					=						tokenizer('Hello there'     ,	return_tensors='tf'      ).input_ids
     lowerCamelCase__       : int   					=						tokenizer('Hi I am'     ,	return_tensors='tf'      ).input_ids

     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase     ,	labels=UpperCAmelCase      ).loss
     lowerCamelCase__       : List[Any]   					=						-tf.math.reduce_mean(UpperCAmelCase      ).numpy()

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





