






"""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__			(    __magic_name__							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =BlenderbotSmallTokenizer
   SCREAMING_SNAKE_CASE_    =False
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        super().setUp()

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

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

        UpperCAmelCase__						:    Any							=							os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES["vocab_file"]							)
        UpperCAmelCase__						:    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       __a							(		self      :	Optional[Any]							,			**snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        kwargs.update(self.special_tokens_map							)
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname							,			**snake_case__							)
   def       __a							(		self      :	Optional[int]							,			snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[Any]							=							"adapt act apte"
        UpperCAmelCase__						:    Optional[int]							=							"adapt act apte"
        return input_text, output_text
   def       __a							(		self      :	Union[str, Any]							):


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

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

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


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"							)
        assert tok("sam"							).input_ids == [1_3_8_4]
        UpperCAmelCase__						:    Optional[int]							=							"I am a small frog."
        UpperCAmelCase__						:    Optional[int]							=							tok([src_text]							,			padding=snake_case__							,			truncation=snake_case__							)["input_ids"]
        UpperCAmelCase__						:    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       __a							(		self      :	Optional[Any]							):


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


