





"""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 						__snake_case   (						SCREAMING_SNAKE_CASE__				,				unittest.TestCase	):





 """simple docstring"""


 _lowerCamelCase								=	BlenderbotSmallTokenizer
 _lowerCamelCase								=	False

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     super().setUp()

     __A       :		Optional[int]							=	["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
     __A       :		List[Any]							=	dict(zip(snake_case__   ,		range(len(snake_case__    )    )    )    )

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

     __A       :		Any							=	os.path.join(self.tmpdirname   ,		VOCAB_FILES_NAMES['''vocab_file''']    )
     __A       :		Any							=	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    UpperCamelCase__(       self   ,		**__lowerCamelCase    ):



     '''simple docstring'''




     kwargs.update(self.special_tokens_map    )
     return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname   ,		**snake_case__    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	"adapt act apte"
     __A       :		Optional[int]							=	"adapt act apte"
     return input_text, output_text

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Tuple							=	BlenderbotSmallTokenizer(self.vocab_file   ,		self.merges_file   ,		**self.special_tokens_map    )
     __A       :		List[Any]							=	"adapt act apte"
     __A       :		Union[str, Any]							=	["adapt", "act", "ap@@", "te"]
     __A       :		Tuple							=	tokenizer.tokenize(snake_case__    )
     self.assertListEqual(snake_case__   ,		snake_case__    )

     __A       :		Optional[Any]							=	[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

     __A       :		Optional[Any]							=	[0, 1, 2, 3, 4, 5]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__    )   ,		snake_case__    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Any							=	BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''    )
     assert tok('''sam'''    ).input_ids == [1384]
     __A       :		Optional[int]							=	"I am a small frog."
     __A       :		Optional[int]							=	tok([src_text]   ,		padding=snake_case__   ,		truncation=snake_case__    )["input_ids"]
     __A       :		Union[str, 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    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[Any]							=	BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''    )
     __A       :		List[Any]							=	"I am a small frog ."
     __A       :		Optional[int]							=	"."
     __A       :		str							=	tok(snake_case__    )["input_ids"]
     __A       :		int							=	tok(snake_case__    )["input_ids"]

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












"""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   ):
   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"							)
        UpperCAmelCase__						:    str							=							AutoTokenizer.from_pretrained("google/mt5-small"							)

        UpperCAmelCase__						:    str							=							tokenizer("Hello there"							,			return_tensors="np"							).input_ids
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer("Hi I am"							,			return_tensors="np"							).input_ids

        UpperCAmelCase__						:    Any							=							shift_tokens_right(snake_case__							,			model.config.pad_token_id							,			model.config.decoder_start_token_id							)

        UpperCAmelCase__						:    Any							=							model(snake_case__							,			decoder_input_ids=snake_case__							).logits
        UpperCAmelCase__						:    Any							=							optax.softmax_cross_entropy(snake_case__							,			onehot(snake_case__							,			logits.shape[-1]							)							).mean()

        UpperCAmelCase__						:    List[str]							=							-(labels.shape[-1] * loss.item())

        UpperCAmelCase__						:    Dict							=							-84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)


