





'''simple docstring'''


import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
    from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
    a__			:					Any       						=						get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
    from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

a__			:					Tuple       						=						128_022
a__			:					Tuple       						=						128_028






@require_sentencepiece
class     lowerCAmelCase__ (				UpperCAmelCase_      , unittest.TestCase    ):

       '''simple docstring'''
       _lowerCamelCase								=MaMaaaTokenizer
       _lowerCamelCase								=False
       _lowerCamelCase								=False
       _lowerCamelCase								=True

       def 			__snake_case      ( self					:				Any						):
              super().setUp()

              UpperCAmelCase  =						['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
              UpperCAmelCase  =						dict(zip(a__       ,     range(len(a__						)						)						)						)
              UpperCAmelCase  =						Path(self.tmpdirname						)
              save_json(a__       ,     save_dir / VOCAB_FILES_NAMES['''vocab_file''']						)
              if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                     copyfile(a__       ,     save_dir / VOCAB_FILES_NAMES['''spm_file''']						)

              UpperCAmelCase  =						MaMaaaTokenizer.from_pretrained(self.tmpdirname						)
              tokenizer.save_pretrained(self.tmpdirname						)

       def 			__snake_case      ( self					:				Optional[Any]       ,     **a__					:				Optional[Any]						):
              return MaMaaaTokenizer.from_pretrained(self.tmpdirname       ,     **a__						)

       def 			__snake_case      ( self					:				int       ,     a__					:				Optional[int]						):
              return (
                  "This is a test",
                  "This is a test",
              )

       def 			__snake_case      ( self					:				Union[str, Any]						):
              UpperCAmelCase  =						'''</s>'''
              UpperCAmelCase  =						0

              self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__						)       ,     a__						)
              self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__						)       ,     a__						)

       def 			__snake_case      ( self					:				int						):
              UpperCAmelCase  =						self.get_tokenizer()
              UpperCAmelCase  =						list(tokenizer.get_vocab().keys()						)

              self.assertEqual(vocab_keys[0]       ,     '''</s>'''						)
              self.assertEqual(vocab_keys[1]       ,     '''<unk>'''						)
              self.assertEqual(vocab_keys[-1]       ,     '''<s>'''						)
              self.assertEqual(len(a__						)       ,     tokenizer.vocab_size + len(tokenizer.get_added_vocab()						)						)

       @unittest.skip('''Skip this test while all models are still to be uploaded.'''						)
       def 			__snake_case      ( self					:				List[Any]						):
              pass

       def 			__snake_case      ( self					:				int						):
              UpperCAmelCase  =						self.get_tokenizer()

              UpperCAmelCase  =						tokenizer.tokenize('''This is a test'''						)
              self.assertListEqual(a__       ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

              self.assertListEqual(
                  tokenizer.convert_tokens_to_ids(a__						)       ,     [2, 3, 4, 5, 6]       ,     )

              UpperCAmelCase  =						tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]						)
              self.assertListEqual(a__       ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

              UpperCAmelCase  =						tokenizer.convert_tokens_to_string(a__						)
              self.assertEqual(a__       ,     '''This is a test'''						)



       @slow
       def 			__snake_case      ( self					:				Union[str, Any]						):
              # fmt: off
              UpperCAmelCase  =						{'''input_ids''': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
              # fmt: on

              self.tokenizer_integration_test_util(
                  expected_encoding=a__       ,     model_name='''facebook/m2m100_418M'''       ,     revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''       ,     )






@require_torch
@require_sentencepiece
@require_tokenizers
class     lowerCAmelCase__ (				unittest.TestCase    ):

       '''simple docstring'''
       _lowerCamelCase								="facebook/m2m100_418M"
       _lowerCamelCase								=[
           "In my opinion, there are two levels of response from the French government.",
           "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
       ]
       _lowerCamelCase								=[
           "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
           "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
       ]

       # fmt: off
       _lowerCamelCase								=[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]

       @classmethod
       def 			__snake_case      ( cls					:				Union[str, Any]						):
              UpperCAmelCase  =						MaMaaaTokenizer.from_pretrained(
                  cls.checkpoint_name       ,     src_lang='''en'''       ,     tgt_lang='''fr'''						)
              UpperCAmelCase  =						1
              return cls

       def 			__snake_case      ( self					:				List[str]						):
              self.assertEqual(self.tokenizer.get_lang_id('''ar'''						)       ,     128006						)
              self.assertEqual(self.tokenizer.get_lang_id('''en'''						)       ,     128022						)
              self.assertEqual(self.tokenizer.get_lang_id('''ro'''						)       ,     128076						)
              self.assertEqual(self.tokenizer.get_lang_id('''mr'''						)       ,     128063						)

       def 			__snake_case      ( self					:				int						):
              UpperCAmelCase  =						self.tokenizer.get_vocab()
              self.assertEqual(len(a__						)       ,     self.tokenizer.vocab_size						)
              self.assertEqual(vocab['''<unk>''']       ,     3						)
              self.assertIn(self.tokenizer.get_lang_token('''en'''						)       ,     a__						)

       def 			__snake_case      ( self					:				List[Any]						):
              UpperCAmelCase  =						'''en'''
              UpperCAmelCase  =						self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
              self.assertListEqual(self.expected_src_tokens       ,     a__						)

       def 			__snake_case      ( self					:				List[str]						):
              self.assertIn(a__       ,     self.tokenizer.all_special_ids						)
              # fmt: off
              UpperCAmelCase  =						[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
              # fmt: on
              UpperCAmelCase  =						self.tokenizer.decode(a__       ,     skip_special_tokens=a__						)
              UpperCAmelCase  =						self.tokenizer.decode(generated_ids[1:]       ,     skip_special_tokens=a__						)
              self.assertEqual(a__       ,     a__						)
              self.assertNotIn(self.tokenizer.eos_token       ,     a__						)

       def 			__snake_case      ( self					:				Any						):
              UpperCAmelCase  =						tempfile.mkdtemp()
              UpperCAmelCase  =						self.tokenizer.lang_token_to_id
              self.tokenizer.save_pretrained(a__						)
              UpperCAmelCase  =						MaMaaaTokenizer.from_pretrained(a__						)
              self.assertDictEqual(new_tok.lang_token_to_id       ,     a__						)

       @require_torch
       def 			__snake_case      ( self					:				List[Any]						):
              UpperCAmelCase  =						'''en'''
              UpperCAmelCase  =						'''fr'''

              UpperCAmelCase  =						self.tokenizer(self.src_text       ,     text_target=self.tgt_text       ,     padding=a__       ,     return_tensors='''pt'''						)

              UpperCAmelCase  =						shift_tokens_right(
                  batch['''labels''']       ,     self.tokenizer.pad_token_id       ,     self.tokenizer.eos_token_id						)

              for k in batch:
                     UpperCAmelCase  =						batch[k].tolist()
              # batch = {k: v.tolist() for k,v in batch.items()}
              # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
              # batch.decoder_inputs_ids[0][0] ==
              assert batch.input_ids[1][0] == EN_CODE
              assert batch.input_ids[1][-1] == 2
              assert batch.labels[1][0] == FR_CODE
              assert batch.labels[1][-1] == 2
              assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]

       @require_torch
       def 			__snake_case      ( self					:				List[Any]						):
              UpperCAmelCase  =						'''mr'''
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id('''mr'''						)]						)
              self.assertListEqual(self.tokenizer.suffix_tokens       ,     [self.tokenizer.eos_token_id]						)

              UpperCAmelCase  =						'''zh'''
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id('''zh'''						)]						)
              self.assertListEqual(self.tokenizer.suffix_tokens       ,     [self.tokenizer.eos_token_id]						)

       @require_torch
       def 			__snake_case      ( self					:				List[str]						):
              UpperCAmelCase  =						'''mr'''
              self.tokenizer._switch_to_target_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id('''mr'''						)]						)
              self.assertListEqual(self.tokenizer.suffix_tokens       ,     [self.tokenizer.eos_token_id]						)
              self.tokenizer._switch_to_input_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id(self.tokenizer.src_lang						)]						)

              UpperCAmelCase  =						'''zh'''
              self.tokenizer._switch_to_target_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id('''zh'''						)]						)
              self.assertListEqual(self.tokenizer.suffix_tokens       ,     [self.tokenizer.eos_token_id]						)
              self.tokenizer._switch_to_input_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens       ,     [self.tokenizer.get_lang_id(self.tokenizer.src_lang						)]						)



       @require_torch
       def 			__snake_case      ( self					:				Optional[int]						):
              UpperCAmelCase  =						self.tokenizer._build_translation_inputs('''A test'''       ,     return_tensors='''pt'''       ,     src_lang='''en'''       ,     tgt_lang='''ar'''						)

              self.assertEqual(
                  nested_simplify(a__						)       ,     {
                      # en_XX, A, test, EOS
                      '''input_ids''': [[128022, 58, 4183, 2]],
                      '''attention_mask''': [[1, 1, 1, 1]],
                      # ar_AR
                      '''forced_bos_token_id''': 128006,
                  }       ,     )






from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel


@require_tf
class 		_SCREAMING_SNAKE_CASE     :
     __SCREAMING_SNAKE_CASE     					=    BlenderbotConfig
     __SCREAMING_SNAKE_CASE     					=    {}
     __SCREAMING_SNAKE_CASE     					=    '''gelu'''


     def __init__(  self							,					A_							,					A_=13							,					A_=7							,					A_=True							,					A_=False							,					A_=99							,					A_=32							,					A_=2							,					A_=4							,					A_=37							,					A_=0.1							,					A_=0.1							,					A_=20							,					A_=2							,					A_=1							,					A_=0							,					):
       _UpperCAmelCase		:							List[str]							=     parent
       _UpperCAmelCase		:							Union[str, Any]							=     batch_size
       _UpperCAmelCase		:							Optional[Any]							=     seq_length
       _UpperCAmelCase		:							List[Any]							=     is_training
       _UpperCAmelCase		:							Union[str, Any]							=     use_labels
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							Optional[int]							=     hidden_size
       _UpperCAmelCase		:							int							=     num_hidden_layers
       _UpperCAmelCase		:							List[Any]							=     num_attention_heads
       _UpperCAmelCase		:							List[Any]							=     intermediate_size

       _UpperCAmelCase		:							Union[str, Any]							=     hidden_dropout_prob
       _UpperCAmelCase		:							str							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Optional[int]							=     max_position_embeddings
       _UpperCAmelCase		:							Optional[Any]							=     eos_token_id
       _UpperCAmelCase		:							Optional[Any]							=     pad_token_id
       _UpperCAmelCase		:							Tuple							=     bos_token_id


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size    )
       _UpperCAmelCase		:							Tuple							=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )							,					1    )
       _UpperCAmelCase		:							List[Any]							=     tf.concat([input_ids, eos_tensor]							,					axis=1    )

       _UpperCAmelCase		:							int							=     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

       _UpperCAmelCase		:							List[Any]							=     self.config_cls(
           vocab_size=self.vocab_size							,					d_model=self.hidden_size							,					encoder_layers=self.num_hidden_layers							,					decoder_layers=self.num_hidden_layers							,					encoder_attention_heads=self.num_attention_heads							,					decoder_attention_heads=self.num_attention_heads							,					encoder_ffn_dim=self.intermediate_size							,					decoder_ffn_dim=self.intermediate_size							,					dropout=self.hidden_dropout_prob							,					attention_dropout=self.attention_probs_dropout_prob							,					max_position_embeddings=self.max_position_embeddings							,					eos_token_ids=[2]							,					bos_token_id=self.bos_token_id							,					pad_token_id=self.pad_token_id							,					decoder_start_token_id=self.pad_token_id							,					**self.config_updates							,					)
       _UpperCAmelCase		:							Dict							=     prepare_blenderbot_inputs_dict(A_							,					A_							,					A_    )
       return config, inputs_dict




     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModel(config=A_    ).get_decoder()
       _UpperCAmelCase		:							List[Any]							=     inputs_dict["""input_ids"""]

       _UpperCAmelCase		:							Any							=     input_ids[:1, :]
       _UpperCAmelCase		:							List[str]							=     inputs_dict["""attention_mask"""][:1, :]
       _UpperCAmelCase		:							Tuple							=     inputs_dict["""head_mask"""]
       _UpperCAmelCase		:							Union[str, Any]							=     1

       # first forward pass
       _UpperCAmelCase		:							List[str]							=     model(A_							,					attention_mask=A_							,					head_mask=A_							,					use_cache=A_    )

       _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       _UpperCAmelCase		:							List[Any]							=     ids_tensor((self.batch_size, 3)							,					config.vocab_size    )
       _UpperCAmelCase		:							List[str]							=     tf.cast(ids_tensor((self.batch_size, 3)							,					2    )							,					tf.inta    )

       # append to next input_ids and
       _UpperCAmelCase		:							Dict							=     tf.concat([input_ids, next_tokens]							,					axis=-1    )
       _UpperCAmelCase		:							Optional[Any]							=     tf.concat([attention_mask, next_attn_mask]							,					axis=-1    )

       _UpperCAmelCase		:							Tuple							=     model(A_							,					attention_mask=A_    )[0]
       _UpperCAmelCase		:							int							=     model(A_							,					attention_mask=A_							,					past_key_values=A_    )[0]

       self.parent.assertEqual(next_tokens.shape[1]							,					output_from_past.shape[1]    )

       # select random slice
       _UpperCAmelCase		:							Tuple							=     int(ids_tensor((1,)							,					output_from_past.shape[-1]    )    )
       _UpperCAmelCase		:							str							=     output_from_no_past[:, -3:, random_slice_idx]
       _UpperCAmelCase		:							str							=     output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(A_							,					A_							,					rtol=1e-3    )




def    a__    (		snake_case__					:							Optional[Any]						,   snake_case__					:							str						,   snake_case__					:							Dict						,   snake_case__					:							str=None						,   snake_case__					:							Optional[int]=None						,   snake_case__					:							str=None						,   snake_case__					:							List[str]=None						,   snake_case__					:							List[str]=None						,   ):
  if attention_mask is None:
    _UpperCAmelCase		:							List[Any]							=     tf.cast(tf.math.not_equal(snake_case__						,   config.pad_token_id     )						,   tf.inta     )
  if decoder_attention_mask is None:
    _UpperCAmelCase		:							str							=     tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape						,   dtype=tf.inta     ),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,   config.pad_token_id     )						,   tf.inta     ),
        ]						,   axis=-1						,   )
  if head_mask is None:
    _UpperCAmelCase		:							List[str]							=     tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
  if decoder_head_mask is None:
    _UpperCAmelCase		:							Tuple							=     tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
  if cross_attn_head_mask is None:
    _UpperCAmelCase		:							Dict							=     tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }


@require_tf
class 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (
         {
             '''conversational''': TFBlenderbotForConditionalGeneration,
             '''feature-extraction''': TFBlenderbotModel,
             '''summarization''': TFBlenderbotForConditionalGeneration,
             '''text2text-generation''': TFBlenderbotForConditionalGeneration,
             '''translation''': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __SCREAMING_SNAKE_CASE     					=    True
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModelTester(self    )
       _UpperCAmelCase		:							Any							=     ConfigTester(self							,					config_class=A_    )


     def        __snake_case(  self    ):
       self.config_tester.run_common_tests()




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*A_    )


@require_tokenizers
@require_tf
class 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    ['''My friends are cool but they eat too many carbs.''']
     __SCREAMING_SNAKE_CASE     					=    '''facebook/blenderbot-400M-distill'''


     @cached_property
     def        __snake_case(  self    ):
       return BlenderbotTokenizer.from_pretrained(self.model_name    )


     @cached_property
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[Any]							=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
       return model




     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Any							=     self.tokenizer(self.src_text							,					return_tensors="""tf"""    )
       _UpperCAmelCase		:							List[Any]							=     self.model.generate(
           model_inputs.input_ids							,					)
       _UpperCAmelCase		:							List[str]							=     self.tokenizer.batch_decode(generated_ids.numpy()							,					skip_special_tokens=A_    )[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )





