

'''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 		__SCREAMING_SNAKE_CASE       :
				lowerCamelCase_							=						BlenderbotSmallConfig
				lowerCamelCase_							=						{}
				lowerCamelCase_							=						'gelu'







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

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







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       Dict								=ids_tensor([self.batch_size, self.seq_length - 1]		,				self.vocab_size      )
										lowercase   :       List[Any]								=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )		,				1      )
										lowercase   :       Optional[int]								=tf.concat([input_ids, eos_tensor]		,				axis=1      )

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

										lowercase   :       Union[str, 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		,				)
										lowercase   :       Dict								=prepare_blenderbot_small_inputs_dict(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__      )
										return config, inputs_dict







				def    lowerCamelCase_  (					self    :		Optional[Any]		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=TFBlenderbotSmallModel(config=UpperCAmelCase__      ).get_decoder()
										lowercase   :       str								=inputs_dict['''input_ids''']

										lowercase   :       Union[str, Any]								=input_ids[:1, :]
										lowercase   :       List[str]								=inputs_dict['''attention_mask'''][:1, :]
										lowercase   :       Union[str, Any]								=inputs_dict['''head_mask''']
										lowercase   :       int								=1

										# first forward pass
										lowercase   :       Any								=model(UpperCAmelCase__		,				attention_mask=UpperCAmelCase__		,				head_mask=UpperCAmelCase__		,				use_cache=UpperCAmelCase__      )

										lowercase		,  lowercase   :       Optional[int]								=outputs.to_tuple()

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

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

										lowercase   :       Optional[Any]								=model(UpperCAmelCase__		,				attention_mask=UpperCAmelCase__      )[0]
										lowercase   :       str								=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
										lowercase   :       Any								=int(ids_tensor((1,)		,				output_from_past.shape[-1]      )      )
										lowercase   :       Union[str, Any]								=output_from_no_past[:, -3:, random_slice_idx]
										lowercase   :       List[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 (      __magic_name__	:    List[str]      ,   __magic_name__	:    Optional[Any]      ,   __magic_name__	:    str      ,   __magic_name__	:    Optional[int]=None      ,   __magic_name__	:    Tuple=None      ,   __magic_name__	:    int=None      ,   __magic_name__	:    Any=None      ,   __magic_name__	:    List[Any]=None      ,   )							->     Optional[Any]:
						if attention_mask is None:
												lowercase   :       Optional[Any]								=tf.cast(tf.math.not_equal(__magic_name__      ,   config.pad_token_id	)      ,   tf.inta	)
						if decoder_attention_mask is None:
												lowercase   :       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:
												lowercase   :       Dict								=tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
						if decoder_head_mask is None:
												lowercase   :       int								=tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
						if cross_attn_head_mask is None:
												lowercase   :       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       ( lowercase__	,       lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						(
				    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
				)
				lowerCamelCase_							=						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
				lowerCamelCase_							=						(
				    {
				        'conversational': TFBlenderbotSmallForConditionalGeneration,
				        'feature-extraction': TFBlenderbotSmallModel,
				        'summarization': TFBlenderbotSmallForConditionalGeneration,
				        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
				        'translation': TFBlenderbotSmallForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				lowerCamelCase_							=						True
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Tuple								=TFBlenderbotSmallModelTester(self      )
										lowercase   :       List[Any]								=ConfigTester(self		,				config_class=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       int								=self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__      )





@require_tokenizers
@require_tf
class 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):
				lowerCamelCase_							=						[
				    '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_							=						'facebook/blenderbot_small-90M'







				@cached_property
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										# use "old" tokenizer here because of bug when downloading new tokenizer
										return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''      )







				@cached_property
				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
										return model







				@slow
				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       int								=self.tokenizer(self.src_text		,				return_tensors='''tf'''      )
										lowercase   :       Optional[int]								=self.model.generate(
										    model_inputs.input_ids		,				attention_mask=model_inputs.attention_mask		,				num_beams=2		,				use_cache=UpperCAmelCase__		,				)
										lowercase   :       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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_							=					{"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_							=					[
							    """PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """PLBartForCausalLM""",
							    """PLBartForConditionalGeneration""",
							    """PLBartForSequenceClassification""",
							    """PLBartModel""",
							    """PLBartPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_plbart import PLBartTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_plbart import (
														    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PLBartForCausalLM,
														    PLBartForConditionalGeneration,
														    PLBartForSequenceClassification,
														    PLBartModel,
														    PLBartPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure)





