





"""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 				__lowercase			:







		'''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	,			):
						__a :						Optional[Any]			       =  parent
						__a :						Union[str, Any]			       =  batch_size
						__a :						List[str]			       =  seq_length
						__a :						List[Any]			       =  is_training
						__a :						Optional[int]			       =  use_labels
						__a :						Optional[Any]			       =  vocab_size
						__a :						str			       =  hidden_size
						__a :						Optional[int]			       =  num_hidden_layers
						__a :						List[str]			       =  num_attention_heads
						__a :						Optional[Any]			       =  intermediate_size

						__a :						List[str]			       =  hidden_dropout_prob
						__a :						Optional[Any]			       =  attention_probs_dropout_prob
						__a :						Dict			       =  max_position_embeddings
						__a :						List[Any]			       =  eos_token_id
						__a :						List[Any]			       =  pad_token_id
						__a :						Any			       =  bos_token_id







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

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

						__a :						str			       =  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[str]			       =  prepare_blenderbot_inputs_dict(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     )
						return config, inputs_dict







		def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
						__a :						Any			       =  TFBlenderbotModel(config=_UpperCAmelCase     ).get_decoder()
						__a :						Any			       =  inputs_dict['''input_ids''']

						__a :						Dict			       =  input_ids[:1, :]
						__a :						Union[str, Any]			       =  inputs_dict['''attention_mask'''][:1, :]
						__a :						Union[str, Any]			       =  inputs_dict['''head_mask''']
						__a :						Optional[int]			       =  1

						# first forward pass
						__a :						int			       =  model(_UpperCAmelCase	,			attention_mask=_UpperCAmelCase	,			head_mask=_UpperCAmelCase	,			use_cache=_UpperCAmelCase     )

						__a :						List[Any]			       =  outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						__a :						int			       =  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 :						List[str]			       =  tf.concat([input_ids, next_tokens]	,			axis=-1     )
						__a :						Tuple			       =  tf.concat([attention_mask, next_attn_mask]	,			axis=-1     )

						__a :						str			       =  model(_UpperCAmelCase	,			attention_mask=_UpperCAmelCase     )[0]
						__a :						Union[str, 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 :						Tuple			       =  output_from_no_past[:, -3:, random_slice_idx]
						__a :						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     __A      (			a_						:Tuple   ,       a_						:Dict   ,       a_						:int   ,       a_						:Any=None   ,       a_						:List[Any]=None   ,       a_						:Union[str, Any]=None   ,       a_						:Any=None   ,       a_						:Any=None   ,       )		->		int:
				if attention_mask is None:
								__a :						Union[str, Any]			       =  tf.cast(tf.math.not_equal(a_   ,       config.pad_token_id)   ,       tf.inta)
				if decoder_attention_mask is None:
								__a :						Optional[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:
								__a :						Any			       =  tf.ones((config.encoder_layers, config.encoder_attention_heads))
				if decoder_head_mask is None:
								__a :						int			       =  tf.ones((config.decoder_layers, config.decoder_attention_heads))
				if cross_attn_head_mask is None:
								__a :						Optional[int]			       =  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 				__lowercase			(			_UpperCamelCase   ,      _UpperCamelCase   ,      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  _lowerCamelCase		(						self     ):
						__a :						List[Any]			       =  TFBlenderbotModelTester(self     )
						__a :						List[Any]			       =  ConfigTester(self	,			config_class=_UpperCAmelCase     )







		def  _lowerCamelCase		(						self     ):
						self.config_tester.run_common_tests()







		def  _lowerCamelCase		(						self     ):
						__a :						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 				__lowercase			(			unittest.TestCase							):







		'''simple docstring'''




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







		@cached_property
		def  _lowerCamelCase		(						self     ):
						return BlenderbotTokenizer.from_pretrained(self.model_name     )







		@cached_property
		def  _lowerCamelCase		(						self     ):
						__a :						Union[str, Any]			       =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
						return model







		@slow
		def  _lowerCamelCase		(						self     ):
						__a :						Any			       =  self.tokenizer(self.src_text	,			return_tensors='''tf'''     )
						__a :						Tuple			       =  self.model.generate(
						    model_inputs.input_ids	,			)
						__a :						List[str]			       =  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 dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=_UpperCamelCase							)
class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  field(default='''language-modeling'''   ,      metadata={'''include_in_asdict_even_if_is_default''': True}							)

					__lowerCAmelCase  =  Features({'''text''': Value('''string'''							)}							)
					__lowerCAmelCase  =  Features({}							)
					__lowerCAmelCase  =  "text"







					@property
					def  _lowerCamelCase		(						self     ):
									return {self.text_column: "text"}