
'''simple docstring'''
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      _lowerCAmelCase       :







							'''simple docstring'''

							lowerCAmelCase_   		=		BlenderbotConfig
							lowerCAmelCase_   		=		{}
							lowerCAmelCase_   		=		"""gelu"""

							def __init__(self    ,    UpperCAmelCase    ,    UpperCAmelCase=13    ,    UpperCAmelCase=7    ,    UpperCAmelCase=True    ,    UpperCAmelCase=False    ,    UpperCAmelCase=99    ,    UpperCAmelCase=32    ,    UpperCAmelCase=2    ,    UpperCAmelCase=4    ,    UpperCAmelCase=37    ,    UpperCAmelCase=0.1    ,    UpperCAmelCase=0.1    ,    UpperCAmelCase=20    ,    UpperCAmelCase=2    ,    UpperCAmelCase=1    ,    UpperCAmelCase=0    ,    )	->  Optional[int]:
														_snake_case  =    parent
														_snake_case  =    batch_size
														_snake_case  =    seq_length
														_snake_case  =    is_training
														_snake_case  =    use_labels
														_snake_case  =    vocab_size
														_snake_case  =    hidden_size
														_snake_case  =    num_hidden_layers
														_snake_case  =    num_attention_heads
														_snake_case  =    intermediate_size

														_snake_case  =    hidden_dropout_prob
														_snake_case  =    attention_probs_dropout_prob
														_snake_case  =    max_position_embeddings
														_snake_case  =    eos_token_id
														_snake_case  =    pad_token_id
														_snake_case  =    bos_token_id

							def 			lowercase				(self    )	->  List[Any]:
														_snake_case  =    ids_tensor([self.batch_size, self.seq_length - 1]    ,    self.vocab_size    )
														_snake_case  =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )    ,    1    )
														_snake_case  =    tf.concat([input_ids, eos_tensor]    ,    axis=1    )

														_snake_case  =    ids_tensor([self.batch_size, self.seq_length]    ,    self.vocab_size    )

														_snake_case  =    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    ,    )
														_snake_case  =    prepare_blenderbot_inputs_dict(__UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase    )
														return config, inputs_dict





							def 			lowercase				(self    ,    UpperCAmelCase    ,    UpperCAmelCase    )	->  Tuple:
														_snake_case  =    TFBlenderbotModel(config=__UpperCAmelCase    ).get_decoder()
														_snake_case  =    inputs_dict['input_ids']

														_snake_case  =    input_ids[:1, :]
														_snake_case  =    inputs_dict['attention_mask'][:1, :]
														_snake_case  =    inputs_dict['head_mask']
														_snake_case  =    1

														# first forward pass
														_snake_case  =    model(__UpperCAmelCase    ,    attention_mask=__UpperCAmelCase    ,    head_mask=__UpperCAmelCase    ,    use_cache=__UpperCAmelCase    )

														_snake_case  =    outputs.to_tuple()

														# create hypothetical next token and extent to next_input_ids
														_snake_case  =    ids_tensor((self.batch_size, 3)    ,    config.vocab_size    )
														_snake_case  =    tf.cast(ids_tensor((self.batch_size, 3)    ,    2    )    ,    tf.inta    )

														# append to next input_ids and
														_snake_case  =    tf.concat([input_ids, next_tokens]    ,    axis=-1    )
														_snake_case  =    tf.concat([attention_mask, next_attn_mask]    ,    axis=-1    )

														_snake_case  =    model(__UpperCAmelCase    ,    attention_mask=__UpperCAmelCase    )[0]
														_snake_case  =    model(__UpperCAmelCase    ,    attention_mask=__UpperCAmelCase    ,    past_key_values=__UpperCAmelCase    )[0]

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

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

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






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE=None				,   _SCREAMING_SNAKE_CASE=None				,   _SCREAMING_SNAKE_CASE=None				,   _SCREAMING_SNAKE_CASE=None				,   _SCREAMING_SNAKE_CASE=None				,   ):


							if attention_mask is None:
														_snake_case  =    tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE				,   config.pad_token_id )				,   tf.inta )
							if decoder_attention_mask is None:
														_snake_case  =    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:
														_snake_case  =    tf.ones((config.encoder_layers, config.encoder_attention_heads) )
							if decoder_head_mask is None:
														_snake_case  =    tf.ones((config.decoder_layers, config.decoder_attention_heads) )
							if cross_attn_head_mask is None:
														_snake_case  =    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      _lowerCAmelCase       (       __snake_case	,				__snake_case	,				unittest.TestCase ):







							'''simple docstring'''

							lowerCAmelCase_   		=		(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
							lowerCAmelCase_   		=		(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
							lowerCAmelCase_   		=		(
							    {
							        """conversational""": TFBlenderbotForConditionalGeneration,
							        """feature-extraction""": TFBlenderbotModel,
							        """summarization""": TFBlenderbotForConditionalGeneration,
							        """text2text-generation""": TFBlenderbotForConditionalGeneration,
							        """translation""": TFBlenderbotForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCAmelCase_   		=		True
							lowerCAmelCase_   		=		False
							lowerCAmelCase_   		=		False

							def 			lowercase				(self    )	->  Tuple:
														_snake_case  =    TFBlenderbotModelTester(self    )
														_snake_case  =    ConfigTester(self    ,    config_class=__UpperCAmelCase    )

							def 			lowercase				(self    )	->  str:
														self.config_tester.run_common_tests()





							def 			lowercase				(self    )	->  str:
														_snake_case  =    self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase    )


@require_tokenizers
@require_tf
class      _lowerCAmelCase       (       unittest.TestCase ):







							'''simple docstring'''

							lowerCAmelCase_   		=		["""My friends are cool but they eat too many carbs."""]
							lowerCAmelCase_   		=		"""facebook/blenderbot-400M-distill"""

							@cached_property
							def 			lowercase				(self    )	->  str:
														return BlenderbotTokenizer.from_pretrained(self.model_name    )

							@cached_property
							def 			lowercase				(self    )	->  List[Any]:
														_snake_case  =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
														return model





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






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__A											=						{}


try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						["""MLukeTokenizer"""]

if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mluke import MLukeTokenizer


else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
