




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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					Any          =  {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """blenderbot-small"""
						UpperCamelCase      =       ["""past_key_values"""]
						UpperCamelCase      =       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

						def __init__(		self :Any     ,			__snake_case :Optional[int]=5_02_65     ,			__snake_case :Any=5_12     ,			__snake_case :Tuple=8     ,			__snake_case :Optional[Any]=20_48     ,			__snake_case :List[Any]=16     ,			__snake_case :Any=8     ,			__snake_case :Union[str, Any]=20_48     ,			__snake_case :Any=16     ,			__snake_case :List[str]=0.0     ,			__snake_case :Dict=0.0     ,			__snake_case :str=True     ,			__snake_case :Optional[int]=True     ,			__snake_case :Optional[int]="gelu"     ,			__snake_case :Dict=5_12     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Tuple=0.0     ,			__snake_case :Optional[Any]=0.0     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :Optional[int]=1     ,			__snake_case :str=False     ,			__snake_case :List[Any]=0     ,			__snake_case :int=1     ,			__snake_case :List[Any]=2     ,			__snake_case :Optional[Any]=2     ,			**__snake_case :str     ,			):
										'''simple docstring'''

										__magic_name__						:						int										=vocab_size
										__magic_name__						:						Optional[Any]										=max_position_embeddings
										__magic_name__						:						Optional[Any]										=d_model
										__magic_name__						:						str										=encoder_ffn_dim
										__magic_name__						:						Tuple										=encoder_layers
										__magic_name__						:						List[str]										=encoder_attention_heads
										__magic_name__						:						Union[str, Any]										=decoder_ffn_dim
										__magic_name__						:						int										=decoder_layers
										__magic_name__						:						Tuple										=decoder_attention_heads
										__magic_name__						:						Tuple										=dropout
										__magic_name__						:						List[str]										=attention_dropout
										__magic_name__						:						int										=activation_dropout
										__magic_name__						:						Union[str, Any]										=activation_function
										__magic_name__						:						Any										=init_std
										__magic_name__						:						Any										=encoder_layerdrop
										__magic_name__						:						Optional[int]										=decoder_layerdrop
										__magic_name__						:						int										=use_cache
										__magic_name__						:						Dict										=encoder_layers
										__magic_name__						:						Tuple										=scale_embedding  # scale factor will be sqrt(d_model) if True

										super().__init__(
										    pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			is_encoder_decoder=__snake_case     ,			decoder_start_token_id=__snake_case     ,			forced_eos_token_id=__snake_case     ,			**__snake_case     ,			)




class 		__A   (      UpperCamelCase__      ):

						@property
						def      A__    (		self :Any			):
										'''simple docstring'''

										if self.task in ["default", "seq2seq-lm"]:
														__magic_name__						:						List[Any]										=OrderedDict(
														    [
														        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
														        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
														    ]			)

														if self.use_past:
																		__magic_name__						:						Tuple										={0: """batch"""}
																		__magic_name__						:						Tuple										={0: """batch""", 1: """past_decoder_sequence + sequence"""}
														else:
																		__magic_name__						:						Optional[Any]										={0: """batch""", 1: """decoder_sequence"""}
																		__magic_name__						:						Union[str, Any]										={0: """batch""", 1: """decoder_sequence"""}

														if self.use_past:
																		self.fill_with_past_key_values_(__snake_case     ,			direction="""inputs"""			)
										elif self.task == "causal-lm":
														# TODO: figure this case out.
														__magic_name__						:						Optional[Any]										=OrderedDict(
														    [
														        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
														        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
														    ]			)
														if self.use_past:
																		__magic_name__					,			__magic_name__						:						List[Any]										=self.num_layers
																		for i in range(__snake_case			):
																						__magic_name__						:						Optional[int]										={0: """batch""", 2: """past_sequence + sequence"""}
																						__magic_name__						:						str										={0: """batch""", 2: """past_sequence + sequence"""}
										else:
														__magic_name__						:						Dict										=OrderedDict(
														    [
														        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
														        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
														        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
														        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
														    ]			)

										return common_inputs

						@property
						def      A__    (		self :Any			):
										'''simple docstring'''

										if self.task in ["default", "seq2seq-lm"]:
														__magic_name__						:						List[Any]										=super().outputs
										else:
														__magic_name__						:						Optional[Any]										=super(__snake_case     ,			self			).outputs
														if self.use_past:
																		__magic_name__					,			__magic_name__						:						int										=self.num_layers
																		for i in range(__snake_case			):
																						__magic_name__						:						Optional[int]										={0: """batch""", 2: """past_sequence + sequence"""}
																						__magic_name__						:						str										={0: """batch""", 2: """past_sequence + sequence"""}
										return common_outputs

						def      A__    (		self :List[Any]     ,			__snake_case :PreTrainedTokenizer     ,			__snake_case :int = -1     ,			__snake_case :int = -1     ,			__snake_case :bool = False     ,			__snake_case :Optional[TensorType] = None     ,			):
										'''simple docstring'''

										__magic_name__						:						str										=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case			)

										# Generate decoder inputs
										__magic_name__						:						List[str]										=seq_length if not self.use_past else 1
										__magic_name__						:						Any										=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case			)
										__magic_name__						:						Any										={f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
										__magic_name__						:						List[str]										=dict(**__snake_case     ,			**__snake_case			)

										if self.use_past:
														if not is_torch_available():
																		raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""			)
														else:
																		import torch
														__magic_name__					,			__magic_name__						:						int										=common_inputs["""input_ids"""].shape
														__magic_name__						:						Any										=common_inputs["""decoder_input_ids"""].shape[1]
														__magic_name__					,			__magic_name__						:						Optional[int]										=self.num_attention_heads
														__magic_name__						:						int										=(
														    batch,
														    num_encoder_attention_heads,
														    encoder_seq_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)
														__magic_name__						:						List[Any]										=decoder_seq_length + 3
														__magic_name__						:						List[Any]										=(
														    batch,
														    num_decoder_attention_heads,
														    decoder_past_length,
														    self._config.hidden_size // num_decoder_attention_heads,
														)

														__magic_name__						:						Any										=torch.cat(
														    [common_inputs["""decoder_attention_mask"""], torch.ones(__snake_case     ,			__snake_case			)]     ,			dim=1			)

														__magic_name__						:						Optional[Any]										=[]
														# If the number of encoder and decoder layers are present in the model configuration, both are considered
														__magic_name__					,			__magic_name__						:						Dict										=self.num_layers
														__magic_name__						:						List[Any]										=min(__snake_case     ,			__snake_case			)
														__magic_name__						:						List[Any]										=max(__snake_case     ,			__snake_case			) - min_num_layers
														__magic_name__						:						Optional[Any]										="""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

														for _ in range(__snake_case			):
																		common_inputs["past_key_values"].append(
																		    (
																		        torch.zeros(__snake_case			),
																		        torch.zeros(__snake_case			),
																		        torch.zeros(__snake_case			),
																		        torch.zeros(__snake_case			),
																		    )			)
														# TODO: test this.
														__magic_name__						:						int										=encoder_shape if remaining_side_name == """encoder""" else decoder_shape
														for _ in range(__snake_case     ,			__snake_case			):
																		common_inputs["past_key_values"].append((torch.zeros(__snake_case			), torch.zeros(__snake_case			))			)
										return common_inputs

						def      A__    (		self :List[str]     ,			__snake_case :PreTrainedTokenizer     ,			__snake_case :int = -1     ,			__snake_case :int = -1     ,			__snake_case :bool = False     ,			__snake_case :Optional[TensorType] = None     ,			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    __snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case			)

										if self.use_past:
														if not is_torch_available():
																		raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""			)
														else:
																		import torch
														__magic_name__					,			__magic_name__						:						int										=common_inputs["""input_ids"""].shape
														# Not using the same length for past_key_values
														__magic_name__						:						int										=seqlen + 2
														__magic_name__					,			__magic_name__						:						Tuple										=self.num_layers
														__magic_name__					,			__magic_name__						:						List[str]										=self.num_attention_heads
														__magic_name__						:						List[Any]										=(
														    batch,
														    num_encoder_attention_heads,
														    past_key_values_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)

														__magic_name__						:						Union[str, Any]										=common_inputs["""attention_mask"""].dtype
														__magic_name__						:						int										=torch.cat(
														    [common_inputs["""attention_mask"""], torch.ones(__snake_case     ,			__snake_case     ,			dtype=__snake_case			)]     ,			dim=1			)
														__magic_name__						:						Dict										=[
														    (torch.zeros(__snake_case			), torch.zeros(__snake_case			)) for _ in range(__snake_case			)
														]
										return common_inputs

						def      A__    (		self :Any     ,			__snake_case :PreTrainedTokenizer     ,			__snake_case :int = -1     ,			__snake_case :int = -1     ,			__snake_case :bool = False     ,			__snake_case :Optional[TensorType] = None     ,			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=compute_effective_axis_dimension(
										    __snake_case     ,			fixed_dimension=OnnxConfig.default_fixed_batch     ,			num_token_to_add=0			)

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										__magic_name__						:						Any										=tokenizer.num_special_tokens_to_add(__snake_case			)
										__magic_name__						:						Optional[Any]										=compute_effective_axis_dimension(
										    __snake_case     ,			fixed_dimension=OnnxConfig.default_fixed_sequence     ,			num_token_to_add=__snake_case			)

										# Generate dummy inputs according to compute batch and sequence
										__magic_name__						:						List[str]										=[""" """.join([tokenizer.unk_token]			) * seq_length] * batch_size
										__magic_name__						:						int										=dict(tokenizer(__snake_case     ,			return_tensors=__snake_case			)			)
										return common_inputs

						def      A__    (		self :Optional[Any]     ,			__snake_case :PreTrainedTokenizer     ,			__snake_case :int = -1     ,			__snake_case :int = -1     ,			__snake_case :bool = False     ,			__snake_case :Optional[TensorType] = None     ,			):
										'''simple docstring'''

										if self.task in ["default", "seq2seq-lm"]:
														__magic_name__						:						Any										=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
														    __snake_case     ,			batch_size=__snake_case     ,			seq_length=__snake_case     ,			is_pair=__snake_case     ,			framework=__snake_case			)

										elif self.task == "causal-lm":
														__magic_name__						:						List[Any]										=self._generate_dummy_inputs_for_causal_lm(
														    __snake_case     ,			batch_size=__snake_case     ,			seq_length=__snake_case     ,			is_pair=__snake_case     ,			framework=__snake_case			)
										else:
														__magic_name__						:						int										=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    __snake_case     ,			batch_size=__snake_case     ,			seq_length=__snake_case     ,			is_pair=__snake_case     ,			framework=__snake_case			)

										return common_inputs


						def      A__    (		self :List[str]     ,			__snake_case :Any     ,			__snake_case :Dict     ,			__snake_case :Any     ,			__snake_case :Any			):
										'''simple docstring'''

										if self.task in ["default", "seq2seq-lm"]:
														__magic_name__						:						Union[str, Any]										=super()._flatten_past_key_values_(__snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case			)
										else:
														__magic_name__						:						Optional[Any]										=super(__snake_case     ,			self			)._flatten_past_key_values_(
														    __snake_case     ,			__snake_case     ,			__snake_case     ,			__snake_case			)










import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										debug_launcher(test_script.main			)


						def      A__    (		self :Dict			):
										'''simple docstring'''

										debug_launcher(test_ops.main			)




