



"""simple docstring"""
import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin
class 				lowerCamelCase  (			_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			Union[str, Any]						=     BlenderbotSmallTokenizer
					_A		:			List[Any]						=     False






					def    lowerCAmelCase_				(       self:							Any						)   ->	Dict:
											super().setUp()

											snake_case_ :Dict						=			["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
											snake_case_ :Dict						=			dict(zip(snake_case						,     range(len(snake_case						)						)						)						)

											snake_case_ :Union[str, Any]						=			["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
											snake_case_ :Dict						=			{"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

											snake_case_ :Any						=			os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""vocab_file"""]						)
											snake_case_ :str						=			os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""merges_file"""]						)
											with open(self.vocab_file						,     """w"""						,     encoding="""utf-8"""						) as fp:
																	fp.write(json.dumps(snake_case						) + """\n"""						)
											with open(self.merges_file						,     """w"""						,     encoding="""utf-8"""						) as fp:
																	fp.write("""\n""".join(snake_case						)						)






					def    lowerCAmelCase_				(       self:							List[Any]						,     **snake_case:							Optional[int]						)   ->	str:
											kwargs.update(self.special_tokens_map						)
											return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname						,     **snake_case						)






					def    lowerCAmelCase_				(       self:							Any						,     snake_case:							Optional[Any]						)   ->	Any:
											snake_case_ :Any						=			"""adapt act apte"""
											snake_case_ :int						=			"""adapt act apte"""
											return input_text, output_text






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											snake_case_ :Any						=			BlenderbotSmallTokenizer(self.vocab_file						,     self.merges_file						,     **self.special_tokens_map						)
											snake_case_ :Dict						=			"""adapt act apte"""
											snake_case_ :Optional[Any]						=			["""adapt""", """act""", """ap@@""", """te"""]
											snake_case_ :List[str]						=			tokenizer.tokenize(snake_case						)
											self.assertListEqual(snake_case						,     snake_case						)

											snake_case_ :int						=			[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

											snake_case_ :str						=			[0, 1, 2, 3, 4, 5]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case						)						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[Any]						)   ->	Union[str, Any]:
											snake_case_ :List[Any]						=			BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""						)
											assert tok("""sam"""						).input_ids == [1_384]
											snake_case_ :str						=			"""I am a small frog."""
											snake_case_ :Dict						=			tok([src_text]						,     padding=snake_case						,     truncation=snake_case						)["""input_ids"""]
											snake_case_ :Optional[Any]						=			tok.batch_decode(snake_case						,     skip_special_tokens=snake_case						,     clean_up_tokenization_spaces=snake_case						)[0]
											assert src_text != decoded  # I wish it did!
											assert decoded == "i am a small frog ."






					def    lowerCAmelCase_				(       self:							str						)   ->	str:
											snake_case_ :Optional[int]						=			BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""						)
											snake_case_ :str						=			"""I am a small frog ."""
											snake_case_ :Dict						=			"""."""
											snake_case_ :int						=			tok(snake_case						)["""input_ids"""]
											snake_case_ :List[Any]						=			tok(snake_case						)["""input_ids"""]

											assert encoded[-1] == encoded_dot[0]





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 SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

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

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


