



"""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      UpperCAmelCase_  (    _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Dict								=			BlenderbotSmallTokenizer
      __SCREAMING_SNAKE_CASE						:       Any								=			False



      def 						snake_case_ (					self					:     List[Any]						):
           super().setUp()

           _UpperCAmelCase      :     str        =   ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
           _UpperCAmelCase      :     int        =   dict(zip(A					,     range(len(A						)						)						)						)

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

           _UpperCAmelCase      :     Optional[Any]        =   os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["vocab_file"]						)
           _UpperCAmelCase      :     Tuple        =   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(A						) + "\n"						)
           with open(self.merges_file					,     "w"					,     encoding="utf-8"						) as fp:
                fp.write("\n".join(A						)						)



      def 						snake_case_ (					self					:     List[Any]					,     **A					:     List[Any]						):
           kwargs.update(self.special_tokens_map						)
           return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     Optional[Any]						):
           _UpperCAmelCase      :     Union[str, Any]        =   "adapt act apte"
           _UpperCAmelCase      :     int        =   "adapt act apte"
           return input_text, output_text



      def 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     List[str]        =   BlenderbotSmallTokenizer(self.vocab_file					,     self.merges_file					,     **self.special_tokens_map						)
           _UpperCAmelCase      :     Dict        =   "adapt act apte"
           _UpperCAmelCase      :     Union[str, Any]        =   ["adapt", "act", "ap@@", "te"]
           _UpperCAmelCase      :     Union[str, Any]        =   tokenizer.tokenize(A						)
           self.assertListEqual(A					,     A						)

           _UpperCAmelCase      :     Union[str, Any]        =   [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

           _UpperCAmelCase      :     Union[str, Any]        =   [0, 1, 2, 3, 4, 5]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(A						)					,     A						)



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Union[str, Any]        =   BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"						)
           assert tok("sam"						).input_ids == [1_3_8_4]
           _UpperCAmelCase      :     str        =   "I am a small frog."
           _UpperCAmelCase      :     Optional[Any]        =   tok([src_text]					,     padding=A					,     truncation=A						)["input_ids"]
           _UpperCAmelCase      :     Any        =   tok.batch_decode(A					,     skip_special_tokens=A					,     clean_up_tokenization_spaces=A						)[0]
           assert src_text != decoded  # I wish it did!
           assert decoded == "i am a small frog ."



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

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





"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowerCAmelCase    : Union[str, Any]							=							get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
 _lowerCAmelCase    : Tuple							=							json.load(f)






@require_torch
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     str					,     A					:     Union[str, Any]						):
           return FSMTTokenizer.from_pretrained(A						)



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[Any]        =   FSMTForConditionalGeneration.from_pretrained(A						).to(A						)
           if torch_device == "cuda":
                model.half()
           return model



      @parameterized.expand(
          [
              ["en-ru", 26.0],
              ["ru-en", 22.0],
              ["en-de", 22.0],
              ["de-en", 29.0],
          ]						)
      @slow
      def 						snake_case_ (					self					:     Any					,     A					:     Dict					,     A					:     List[str]						):
           # note: this test is not testing the best performance since it only evals a small batch
           # but it should be enough to detect a regression in the output quality
           _UpperCAmelCase      :     Any        =   f'facebook/wmt19-{pair}'
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer(A						)
           _UpperCAmelCase      :     Optional[int]        =   self.get_model(A						)

           _UpperCAmelCase      :     int        =   bleu_data[pair]["src"]
           _UpperCAmelCase      :     Optional[int]        =   bleu_data[pair]["tgt"]

           _UpperCAmelCase      :     List[str]        =   tokenizer(A					,     return_tensors="pt"					,     truncation=A					,     padding="longest"						).to(A						)
           _UpperCAmelCase      :     List[str]        =   model.generate(
               input_ids=batch.input_ids					,     num_beams=8					,     )
           _UpperCAmelCase      :     Any        =   tokenizer.batch_decode(
               A					,     skip_special_tokens=A					,     clean_up_tokenization_spaces=A						)
           _UpperCAmelCase      :     Any        =   calculate_bleu(A					,     A						)
           print(A						)
           self.assertGreaterEqual(scores["bleu"]					,     A						)
