



'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class       __magic_name__		:
							lowerCAmelCase			:       Union[str, Any]  							=   BlenderbotSmallConfig
							lowerCAmelCase			:       Dict  							=   {}
							lowerCAmelCase			:       str  							=   'gelu'







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

									_a	: Dict								=				hidden_dropout_prob
									_a	: int								=				attention_probs_dropout_prob
									_a	: Dict								=				max_position_embeddings
									_a	: int								=				eos_token_id
									_a	: str								=				pad_token_id
									_a	: str								=				bos_token_id







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

									_a	: str								=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size						)

									_a	: Tuple								=				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      ,)
									_a	: List[Any]								=				prepare_blenderbot_small_inputs_dict(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)
									return config, inputs_dict







							def 				__lowercase     (				self   :			str      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			Union[str, Any]						):
									_a	: int								=				TFBlenderbotSmallModel(config=_UpperCAmelCase						).get_decoder()
									_a	: Dict								=				inputs_dict['input_ids']

									_a	: Tuple								=				input_ids[:1, :]
									_a	: Any								=				inputs_dict['attention_mask'][:1, :]
									_a	: Optional[int]								=				inputs_dict['head_mask']
									_a	: int								=				1

									# first forward pass
									_a	: Union[str, Any]								=				model(_UpperCAmelCase      ,attention_mask=_UpperCAmelCase      ,head_mask=_UpperCAmelCase      ,use_cache=_UpperCAmelCase						)

									_a			,   _a	: List[Any]								=				outputs.to_tuple()

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

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

									_a	: List[str]								=				model(_UpperCAmelCase      ,attention_mask=_UpperCAmelCase						)[0]
									_a	: Any								=				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
									_a	: Any								=				int(ids_tensor((1,)      ,output_from_past.shape[-1]						)						)
									_a	: List[Any]								=				output_from_no_past[:, -3:, random_slice_idx]
									_a	: str								=				output_from_past[:, :, random_slice_idx]

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



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     )     -> List[Any]:
		if attention_mask is None:
				_a	: Optional[int]								=				tf.cast(tf.math.not_equal(lowerCAmelCase_		,     config.pad_token_id    )		,     tf.inta    )
		if decoder_attention_mask is None:
				_a	: Dict								=				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:
				_a	: Tuple								=				tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
		if decoder_head_mask is None:
				_a	: List[str]								=				tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
		if cross_attn_head_mask is None:
				_a	: Union[str, Any]								=				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       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Union[str, Any]  							=   (
							    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
							)
							lowerCAmelCase			:       Dict  							=   (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
							lowerCAmelCase			:       str  							=   (
							    {
							        'conversational': TFBlenderbotSmallForConditionalGeneration,
							        'feature-extraction': TFBlenderbotSmallModel,
							        'summarization': TFBlenderbotSmallForConditionalGeneration,
							        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
							        'translation': TFBlenderbotSmallForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCAmelCase			:       Any  							=   True
							lowerCAmelCase			:       Optional[Any]  							=   False
							lowerCAmelCase			:       Any  							=   False







							def 				__lowercase     (				self   :			Any						):
									_a	: Union[str, Any]								=				TFBlenderbotSmallModelTester(self						)
									_a	: List[str]								=				ConfigTester(self      ,config_class=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Any						):
									self.config_tester.run_common_tests()







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a	: 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       __magic_name__		(       unittest.TestCase  ):
							lowerCAmelCase			:       List[str]  							=   [
							    'Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '
							    '  i\'m going to throw up.\nand why is that?'
							]
							lowerCAmelCase			:       Optional[Any]  							=   'facebook/blenderbot_small-90M'







							@cached_property
							def 				__lowercase     (				self   :			Union[str, Any]						):
									# use "old" tokenizer here because of bug when downloading new tokenizer
									return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'						)







							@cached_property
							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Any								=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
									return model







							@slow
							def 				__lowercase     (				self   :			int						):
									_a	: Any								=				self.tokenizer(self.src_text      ,return_tensors='tf'						)
									_a	: int								=				self.model.generate(
									    model_inputs.input_ids      ,attention_mask=model_inputs.attention_mask      ,num_beams=2      ,use_cache=_UpperCAmelCase      ,)
									_a	: str								=				self.tokenizer.batch_decode(generated_ids.numpy()      ,skip_special_tokens=_UpperCAmelCase						)[0]
									assert generated_words in (
									    "i don't know. i just feel like i'm going to throw up. it's not fun.",
									    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
									    "i'm not sure. i just feel like i've been in a bad situation.",
									)









'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowerCAmelCase		     =  {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		     =  [
							    '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ErnieForCausalLM''',
							    '''ErnieForMaskedLM''',
							    '''ErnieForMultipleChoice''',
							    '''ErnieForNextSentencePrediction''',
							    '''ErnieForPreTraining''',
							    '''ErnieForQuestionAnswering''',
							    '''ErnieForSequenceClassification''',
							    '''ErnieForTokenClassification''',
							    '''ErnieModel''',
							    '''ErniePreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ernie import (
														    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ErnieForCausalLM,
														    ErnieForMaskedLM,
														    ErnieForMultipleChoice,
														    ErnieForNextSentencePrediction,
														    ErnieForPreTraining,
														    ErnieForQuestionAnswering,
														    ErnieForSequenceClassification,
														    ErnieForTokenClassification,
														    ErnieModel,
														    ErniePreTrainedModel,
														)

else:
							import sys

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




