






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 							__UpperCamelCase     :
	__snake_case						:List[str] =							BlenderbotConfig
	__snake_case						:int =							{}
	__snake_case						:Optional[Any] =							'gelu'







	def __init__(	self   :				List[Any]							,					_lowerCAmelCase   :				Union[str, Any]							,					_lowerCAmelCase   :				Optional[Any]=13							,					_lowerCAmelCase   :				Optional[int]=7							,					_lowerCAmelCase   :				str=True							,					_lowerCAmelCase   :				Any=False							,					_lowerCAmelCase   :				List[str]=99							,					_lowerCAmelCase   :				List[Any]=32							,					_lowerCAmelCase   :				Any=2							,					_lowerCAmelCase   :				str=4							,					_lowerCAmelCase   :				Any=37							,					_lowerCAmelCase   :				Any=0.1							,					_lowerCAmelCase   :				int=0.1							,					_lowerCAmelCase   :				Dict=20							,					_lowerCAmelCase   :				List[str]=2							,					_lowerCAmelCase   :				int=1							,					_lowerCAmelCase   :				Optional[int]=0							,					)		->			Union[str, Any]:
		"""simple docstring"""







		__lowercase   							=	parent
		__lowercase   							=	batch_size
		__lowercase   							=	seq_length
		__lowercase   							=	is_training
		__lowercase   							=	use_labels
		__lowercase   							=	vocab_size
		__lowercase   							=	hidden_size
		__lowercase   							=	num_hidden_layers
		__lowercase   							=	num_attention_heads
		__lowercase   							=	intermediate_size

		__lowercase   							=	hidden_dropout_prob
		__lowercase   							=	attention_probs_dropout_prob
		__lowercase   							=	max_position_embeddings
		__lowercase   							=	eos_token_id
		__lowercase   							=	pad_token_id
		__lowercase   							=	bos_token_id







	def 			_a (	self   :				int      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size      )
		__lowercase   							=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )							,					1      )
		__lowercase   							=	tf.concat([input_ids, eos_tensor]							,					axis=1      )

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

		__lowercase   							=	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							,					)
		__lowercase   							=	prepare_blenderbot_inputs_dict(_lowerCAmelCase							,					_lowerCAmelCase							,					_lowerCAmelCase      )
		return config, inputs_dict







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				List[Any]      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	TFBlenderbotModel(config=_lowerCAmelCase      ).get_decoder()
		__lowercase   							=	inputs_dict["""input_ids"""]

		__lowercase   							=	input_ids[:1, :]
		__lowercase   							=	inputs_dict["""attention_mask"""][:1, :]
		__lowercase   							=	inputs_dict["""head_mask"""]
		__lowercase   							=	1

		# first forward pass
		__lowercase   							=	model(_lowerCAmelCase							,					attention_mask=_lowerCAmelCase							,					head_mask=_lowerCAmelCase							,					use_cache=_lowerCAmelCase      )

		__lowercase							,    __lowercase   							=	outputs.to_tuple()

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

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

		__lowercase   							=	model(_lowerCAmelCase							,					attention_mask=_lowerCAmelCase      )[0]
		__lowercase   							=	model(_lowerCAmelCase							,					attention_mask=_lowerCAmelCase							,					past_key_values=_lowerCAmelCase      )[0]

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

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

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





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase=None					,	lowerCamelCase=None					,	lowerCamelCase=None					,	lowerCamelCase=None					,	lowerCamelCase=None					,	):




	'''simple docstring'''







	if attention_mask is None:
		__lowercase   							=	tf.cast(tf.math.not_equal(lowerCamelCase					,	config.pad_token_id       )					,	tf.inta       )
	if decoder_attention_mask is None:
		__lowercase   							=	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:
		__lowercase   							=	tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
	if decoder_head_mask is None:
		__lowercase   							=	tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
	if cross_attn_head_mask is None:
		__lowercase   							=	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 							__UpperCamelCase     (  _lowerCAmelCase ,      _lowerCAmelCase ,      unittest.TestCase ):
	__snake_case						:Union[str, Any] =							(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
	__snake_case						:List[Any] =							(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
	__snake_case						:Union[str, Any] =							(
	    {
	        'conversational': TFBlenderbotForConditionalGeneration,
	        'feature-extraction': TFBlenderbotModel,
	        'summarization': TFBlenderbotForConditionalGeneration,
	        'text2text-generation': TFBlenderbotForConditionalGeneration,
	        'translation': TFBlenderbotForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	__snake_case						:Any =							True
	__snake_case						:Dict =							False
	__snake_case						:Optional[int] =							False







	def 			_a (	self   :				Optional[Any]      )		->			Union[str, Any]:
		"""simple docstring"""







		__lowercase   							=	TFBlenderbotModelTester(self      )
		__lowercase   							=	ConfigTester(self							,					config_class=_lowerCAmelCase      )







	def 			_a (	self   :				List[str]      )		->			Optional[int]:
		"""simple docstring"""







		self.config_tester.run_common_tests()







	def 			_a (	self   :				Dict      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	self.model_tester.prepare_config_and_inputs_for_common()
		self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase      )


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







	@cached_property
	def 			_a (	self   :				Optional[int]      )		->			str:
		"""simple docstring"""







		return BlenderbotTokenizer.from_pretrained(self.model_name      )







	@cached_property
	def 			_a (	self   :				List[str]      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
		return model







	@slow
	def 			_a (	self   :				str      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	self.tokenizer(self.src_text							,					return_tensors="""tf"""      )
		__lowercase   							=	self.model.generate(
		    model_inputs.input_ids							,					)
		__lowercase   							=	self.tokenizer.batch_decode(generated_ids.numpy()							,					skip_special_tokens=_lowerCAmelCase      )[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_tokenizers_available, is_torch_available


__A   :    str											= {
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    Optional[int]											= [
      "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoCBertForCausalLM",
      "RoCBertForMaskedLM",
      "RoCBertForMultipleChoice",
      "RoCBertForPreTraining",
      "RoCBertForQuestionAnswering",
      "RoCBertForSequenceClassification",
      "RoCBertForTokenClassification",
      "RoCBertLayer",
      "RoCBertModel",
      "RoCBertPreTrainedModel",
      "load_tf_weights_in_roc_bert",
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

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



