






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'''



		_snake_case   :   Optional[Any]							=  BlenderbotConfig
		_snake_case   :   str							=  {}
		_snake_case   :   List[Any]							=  '''gelu'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=1_3    ,		_UpperCamelCase=7    ,		_UpperCamelCase=True    ,		_UpperCamelCase=False    ,		_UpperCamelCase=9_9    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=2    ,		_UpperCamelCase=4    ,		_UpperCamelCase=3_7    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=2_0    ,		_UpperCamelCase=2    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		)     ->							str:
							UpperCAmelCase_   :				str						=						parent
							UpperCAmelCase_   :				Optional[Any]						=						batch_size
							UpperCAmelCase_   :				str						=						seq_length
							UpperCAmelCase_   :				Dict						=						is_training
							UpperCAmelCase_   :				Any						=						use_labels
							UpperCAmelCase_   :				int						=						vocab_size
							UpperCAmelCase_   :				Optional[Any]						=						hidden_size
							UpperCAmelCase_   :				List[Any]						=						num_hidden_layers
							UpperCAmelCase_   :				Dict						=						num_attention_heads
							UpperCAmelCase_   :				str						=						intermediate_size

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



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Optional[Any]						=						ids_tensor([self.batch_size, self.seq_length - 1]    ,		self.vocab_size       )
							UpperCAmelCase_   :				Any						=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )    ,		1       )
							UpperCAmelCase_   :				Optional[Any]						=						tf.concat([input_ids, eos_tensor]    ,		axis=1       )

							UpperCAmelCase_   :				Tuple						=						ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size       )

							UpperCAmelCase_   :				Optional[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_   :				Union[str, Any]						=						prepare_blenderbot_inputs_dict(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )
							return config, inputs_dict



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				str						=						TFBlenderbotModel(config=_UpperCamelCase       ).get_decoder()
							UpperCAmelCase_   :				Optional[int]						=						inputs_dict['input_ids']

							UpperCAmelCase_   :				Any						=						input_ids[:1, :]
							UpperCAmelCase_   :				Optional[Any]						=						inputs_dict['attention_mask'][:1, :]
							UpperCAmelCase_   :				Any						=						inputs_dict['head_mask']
							UpperCAmelCase_   :				List[str]						=						1

							# first forward pass
							UpperCAmelCase_   :				Optional[int]						=						model(_UpperCamelCase    ,		attention_mask=_UpperCamelCase    ,		head_mask=_UpperCamelCase    ,		use_cache=_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						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_   :				Union[str, Any]						=						tf.concat([input_ids, next_tokens]    ,		axis=-1       )
							UpperCAmelCase_   :				Tuple						=						tf.concat([attention_mask, next_attn_mask]    ,		axis=-1       )

							UpperCAmelCase_   :				Any						=						model(_UpperCamelCase    ,		attention_mask=_UpperCamelCase       )[0]
							UpperCAmelCase_   :				Dict						=						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
							UpperCAmelCase_   :				Union[str, Any]						=						int(ids_tensor((1,)    ,		output_from_past.shape[-1]       )       )
							UpperCAmelCase_   :				Optional[int]						=						output_from_no_past[:, -3:, random_slice_idx]
							UpperCAmelCase_   :				List[Any]						=						output_from_past[:, :, random_slice_idx]

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





def 					lowercase__				(					__snake_case   :							List[str]					,	__snake_case   :							List[str]					,	__snake_case   :							List[Any]					,	__snake_case   :							Optional[int]=None					,	__snake_case   :							Dict=None					,	__snake_case   :							Union[str, Any]=None					,	__snake_case   :							int=None					,	__snake_case   :							List[str]=None					,	):


					if attention_mask is None:
										UpperCAmelCase_   :				Optional[Any]						=						tf.cast(tf.math.not_equal(__snake_case					,	config.pad_token_id  )					,	tf.inta  )
					if decoder_attention_mask is None:
										UpperCAmelCase_   :				Any						=						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[Any]						=						tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
					if decoder_head_mask is None:
										UpperCAmelCase_   :				Any						=						tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
					if cross_attn_head_mask is None:
										UpperCAmelCase_   :				Tuple						=						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'''



		_snake_case   :   str							=  (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
		_snake_case   :   Union[str, Any]							=  (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
		_snake_case   :   str							=  (
		    {
		        '''conversational''': TFBlenderbotForConditionalGeneration,
		        '''feature-extraction''': TFBlenderbotModel,
		        '''summarization''': TFBlenderbotForConditionalGeneration,
		        '''text2text-generation''': TFBlenderbotForConditionalGeneration,
		        '''translation''': TFBlenderbotForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		_snake_case   :   Optional[int]							=  True
		_snake_case   :   Tuple							=  False
		_snake_case   :   Union[str, Any]							=  False



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Optional[int]						=						TFBlenderbotModelTester(self       )
							UpperCAmelCase_   :				Optional[int]						=						ConfigTester(self    ,		config_class=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							self.config_tester.run_common_tests()



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				Union[str, Any]						=						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'''



		_snake_case   :   Dict							=  ['''My friends are cool but they eat too many carbs.''']
		_snake_case   :   Any							=  '''facebook/blenderbot-400M-distill'''



		@cached_property
		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							return BlenderbotTokenizer.from_pretrained(self.model_name       )



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



		@slow
		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				Any						=						self.tokenizer(self.src_text    ,		return_tensors='tf'       )
							UpperCAmelCase_   :				Dict						=						self.model.generate(
							    model_inputs.input_ids    ,		)
							UpperCAmelCase_   :				List[Any]						=						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?"
							)














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCAmelCase     							=						{
    '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:
		__UpperCAmelCase     							=						[
		    '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

		__UpperCAmelCase     							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






