



"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
		from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


_lowercase				:				Dict   		=		logging.get_logger(__name__)

_lowercase				:				Union[str, Any]   		=		{
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							'bloom'
					_a         =							['past_key_values']
					_a         =							{
					    'num_hidden_layers': 'n_layer',
					    'num_attention_heads': 'n_head',
					}



					def __init__( self							:						List[Any],						lowerCamelCase							:						int=25_0880,						lowerCamelCase							:						int=64,						lowerCamelCase							:						int=2,						lowerCamelCase							:						Optional[int]=8,						lowerCamelCase							:						Optional[int]=1E-5,						lowerCamelCase							:						Optional[int]=0.02,						lowerCamelCase							:						List[str]=True,						lowerCamelCase							:						Optional[int]=1,						lowerCamelCase							:						str=2,						lowerCamelCase							:						Dict=False,						lowerCamelCase							:						int=0.0,						lowerCamelCase							:						List[Any]=0.0,						lowerCamelCase							:						Optional[Any]=1,						lowerCamelCase							:						Optional[int]=False,						**lowerCamelCase							:						Optional[Any],						)->      Tuple:
								lowerCamelCase__	:	List[str]				=vocab_size
								# Backward compatibility with n_embed kwarg
								lowerCamelCase__	:	List[Any]				=kwargs.pop('''n_embed''',						lowerCamelCase  )
								lowerCamelCase__	:	List[Any]				=hidden_size if n_embed is None else n_embed
								lowerCamelCase__	:	Tuple				=n_layer
								lowerCamelCase__	:	Tuple				=n_head
								lowerCamelCase__	:	int				=layer_norm_epsilon
								lowerCamelCase__	:	Optional[Any]				=initializer_range
								lowerCamelCase__	:	Dict				=use_cache
								lowerCamelCase__	:	Any				=pretraining_tp
								lowerCamelCase__	:	List[Any]				=apply_residual_connection_post_layernorm
								lowerCamelCase__	:	int				=hidden_dropout
								lowerCamelCase__	:	str				=attention_dropout

								lowerCamelCase__	:	Optional[int]				=bos_token_id
								lowerCamelCase__	:	Optional[int]				=eos_token_id
								lowerCamelCase__	:	List[Any]				=slow_but_exact

								super().__init__(bos_token_id=lowerCamelCase,						eos_token_id=lowerCamelCase,						**lowerCamelCase  )





class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							version.parse('1.12'     )



					def __init__( self							:						int,						lowerCamelCase							:						PretrainedConfig,						lowerCamelCase							:						str = "default",						lowerCamelCase							:						List[PatchingSpec] = None,						lowerCamelCase							:						bool = False,						)->      Optional[Any]:
								super().__init__(lowerCamelCase,						task=lowerCamelCase,						patching_specs=lowerCamelCase,						use_past=lowerCamelCase  )
								if not getattr(self._config,						'''pad_token_id''',						lowerCamelCase  ):
											# TODO: how to do that better?
											lowerCamelCase__	:	Optional[Any]				=0



					@property
					def 	snake_case ( self							:						int  )->      Mapping[str, Mapping[int, str]]:
								lowerCamelCase__	:	Optional[int]				=OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}  )
								if self.use_past:
											# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
											self.fill_with_past_key_values_(lowerCamelCase,						direction='''inputs''',						inverted_values_shape=lowerCamelCase  )
											lowerCamelCase__	:	Optional[Any]				={0: '''batch''', 1: '''past_sequence + sequence'''}
								else:
											lowerCamelCase__	:	Any				={0: '''batch''', 1: '''sequence'''}

								return common_inputs



					@property
					def 	snake_case ( self							:						Optional[int]  )->      int:
								return self._config.n_layer



					@property
					def 	snake_case ( self							:						str  )->      int:
								return self._config.n_head



					@property
					def 	snake_case ( self							:						List[Any]  )->      float:
								return 1E-3



					def 	snake_case ( self							:						int,						lowerCamelCase							:						"PreTrainedTokenizer",						lowerCamelCase							:						int = -1,						lowerCamelCase							:						int = -1,						lowerCamelCase							:						bool = False,						lowerCamelCase							:						Optional["TensorType"] = None,						)->      Mapping[str, Any]:
								lowerCamelCase__	:	str				=super(lowerCamelCase,						self  ).generate_dummy_inputs(
								    lowerCamelCase,						batch_size=lowerCamelCase,						seq_length=lowerCamelCase,						is_pair=lowerCamelCase,						framework=lowerCamelCase  )

								# We need to order the input in the way they appears in the forward()
								lowerCamelCase__	:	Optional[Any]				=OrderedDict({'''input_ids''': common_inputs['''input_ids''']}  )

								# Need to add the past_keys
								if self.use_past:
											if not is_torch_available():
														raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''  )
											else:
														import torch

														lowerCamelCase__			,     lowerCamelCase__	:	int				=common_inputs['''input_ids'''].shape
														# Not using the same length for past_key_values
														lowerCamelCase__	:	List[str]				=seqlen + 2
														lowerCamelCase__	:	Union[str, Any]				=self._config.hidden_size // self.num_attention_heads
														lowerCamelCase__	:	Any				=(
														    batch * self.num_attention_heads,
														    head_dim,
														    past_key_values_length,
														)
														lowerCamelCase__	:	Any				=(
														    batch * self.num_attention_heads,
														    past_key_values_length,
														    head_dim,
														)
														lowerCamelCase__	:	Tuple				=[
														    (torch.zeros(lowerCamelCase  ), torch.zeros(lowerCamelCase  )) for _ in range(self.num_layers  )
														]

								lowerCamelCase__	:	Any				=common_inputs['''attention_mask''']
								if self.use_past:
											lowerCamelCase__	:	Dict				=ordered_inputs['''attention_mask'''].dtype
											lowerCamelCase__	:	Dict				=torch.cat(
											    [ordered_inputs['''attention_mask'''], torch.ones(lowerCamelCase,						lowerCamelCase,						dtype=lowerCamelCase  )],						dim=1  )

								return ordered_inputs




					@property
					def 	snake_case ( self							:						Any  )->      int:
								return 13









"""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			:


					'''simple docstring'''





					_a         =							BlenderbotSmallConfig
					_a         =							{}
					_a         =							'gelu'



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

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



					def 	snake_case ( self							:						Any  )->      Any:
								lowerCamelCase__	:	Any				=ids_tensor([self.batch_size, self.seq_length - 1],						self.vocab_size  )
								lowerCamelCase__	:	Tuple				=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ),						1  )
								lowerCamelCase__	:	Any				=tf.concat([input_ids, eos_tensor],						axis=1  )

								lowerCamelCase__	:	Optional[int]				=ids_tensor([self.batch_size, self.seq_length],						self.vocab_size  )

								lowerCamelCase__	:	int				=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,						)
								lowerCamelCase__	:	Optional[int]				=prepare_blenderbot_small_inputs_dict(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )
								return config, inputs_dict




					def 	snake_case ( self							:						Any,						lowerCamelCase							:						str,						lowerCamelCase							:						Any  )->      Optional[Any]:
								lowerCamelCase__	:	Union[str, Any]				=TFBlenderbotSmallModel(config=lowerCamelCase  ).get_decoder()
								lowerCamelCase__	:	List[Any]				=inputs_dict['''input_ids''']

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

								# first forward pass
								lowerCamelCase__	:	Dict				=model(lowerCamelCase,						attention_mask=lowerCamelCase,						head_mask=lowerCamelCase,						use_cache=lowerCamelCase  )

								lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=outputs.to_tuple()

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

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

								lowerCamelCase__	:	Optional[int]				=model(lowerCamelCase,						attention_mask=lowerCamelCase  )[0]
								lowerCamelCase__	:	Union[str, Any]				=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
								lowerCamelCase__	:	Tuple				=int(ids_tensor((1,),						output_from_past.shape[-1]  )  )
								lowerCamelCase__	:	int				=output_from_no_past[:, -3:, random_slice_idx]
								lowerCamelCase__	:	List[str]				=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       :					Tuple							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					Any=None							,	__lowerCamelCase       :					List[str]=None							,	__lowerCamelCase       :					Tuple=None							,	__lowerCamelCase       :					Dict=None							,	__lowerCamelCase       :					Optional[int]=None							,	):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__	:	List[str]				=tf.cast(tf.math.not_equal(__lowerCamelCase							,	config.pad_token_id			)							,	tf.inta			)
			if decoder_attention_mask is None:
						lowerCamelCase__	:	str				=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:
						lowerCamelCase__	:	int				=tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						lowerCamelCase__	:	int				=tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			if cross_attn_head_mask is None:
						lowerCamelCase__	:	List[str]				=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			(   lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							(
					    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
					)
					_a         =							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
					_a         =							(
					    {
					        'conversational': TFBlenderbotSmallForConditionalGeneration,
					        'feature-extraction': TFBlenderbotSmallModel,
					        'summarization': TFBlenderbotSmallForConditionalGeneration,
					        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
					        'translation': TFBlenderbotSmallForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					_a         =							True
					_a         =							False
					_a         =							False



					def 	snake_case ( self							:						Any  )->      str:
								lowerCamelCase__	:	Tuple				=TFBlenderbotSmallModelTester(self  )
								lowerCamelCase__	:	Union[str, Any]				=ConfigTester(self,						config_class=lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Optional[int]:
								self.config_tester.run_common_tests()




					def 	snake_case ( self							:						int  )->      str:
								lowerCamelCase__	:	List[Any]				=self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase  )





@require_tokenizers
@require_tf
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					_a         =							[
					    '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?'
					]
					_a         =							'facebook/blenderbot_small-90M'



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



					@cached_property
					def 	snake_case ( self							:						int  )->      List[Any]:
								lowerCamelCase__	:	str				=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model




					@slow
					def 	snake_case ( self							:						Tuple  )->      int:
								lowerCamelCase__	:	Dict				=self.tokenizer(self.src_text,						return_tensors='''tf'''  )
								lowerCamelCase__	:	Any				=self.model.generate(
								    model_inputs.input_ids,						attention_mask=model_inputs.attention_mask,						num_beams=2,						use_cache=lowerCamelCase,						)
								lowerCamelCase__	:	Any				=self.tokenizer.batch_decode(generated_ids.numpy(),						skip_special_tokens=lowerCamelCase  )[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.",
								)




