

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


lowercase     :       Dict        =       logging.get_logger(__name__)

lowercase     :       List[Any]        =       {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				Optional[Any]											=							'blenderbot'
						lowercase  :				Optional[Any]											=							['past_key_values']
						lowercase  :				Optional[Any]											=							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

						def __init__(				self , __UpperCamelCase=80_08 , __UpperCamelCase=1_28 , __UpperCamelCase=2 , __UpperCamelCase=1_02_40 , __UpperCamelCase=32 , __UpperCamelCase=24 , __UpperCamelCase=1_02_40 , __UpperCamelCase=32 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=True , __UpperCamelCase=True , __UpperCamelCase="gelu" , __UpperCamelCase=25_60 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.02 , __UpperCamelCase=1 , __UpperCamelCase=False , __UpperCamelCase=0 , __UpperCamelCase=1 , __UpperCamelCase=2 , __UpperCamelCase=3 , __UpperCamelCase=2 , **__UpperCamelCase , )							->	Optional[Any]:




									'''simple docstring'''


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

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







class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						@property
						def __lowerCamelCase					(				self    )							->	Mapping[str, Mapping[int, str]]:




									'''simple docstring'''


									if self.task in ["default", "seq2seq-lm"]:
												__UpperCamelCase    :    List[Any]								=      OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]    )
												if self.use_past:
															__UpperCamelCase    :    List[str]								=      {0: "batch"}
															__UpperCamelCase    :    str								=      {0: "batch", 1: "past_decoder_sequence + sequence"}
												else:
															__UpperCamelCase    :    Tuple								=      {0: "batch", 1: "decoder_sequence"}
															__UpperCamelCase    :    List[str]								=      {0: "batch", 1: "decoder_sequence"}
												if self.use_past:
															self.fill_with_past_key_values_(__UpperCamelCase , direction="inputs"    )
									elif self.task == "causal-lm":
												__UpperCamelCase    :    List[Any]								=      OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]    )
												if self.use_past:
															__UpperCamelCase							,				__UpperCamelCase    :    List[Any]								=      self.num_layers
															for i in range(__UpperCamelCase    ):
																		__UpperCamelCase    :    Optional[Any]								=      {0: "batch", 2: "past_sequence + sequence"}
																		__UpperCamelCase    :    Union[str, Any]								=      {0: "batch", 2: "past_sequence + sequence"}
									else:
												__UpperCamelCase    :    Tuple								=      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
						# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
						def __lowerCamelCase					(				self    )							->	Mapping[str, Mapping[int, str]]:




									'''simple docstring'''


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

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = -1 , __UpperCamelCase = -1 , __UpperCamelCase = False , __UpperCamelCase = None , )							->	Mapping[str, Any]:




									'''simple docstring'''


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

									if self.use_past:
												if not is_torch_available():
															raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."    )
												else:
															import torch
												__UpperCamelCase							,				__UpperCamelCase    :    Optional[int]								=      common_inputs["input_ids"].shape
												__UpperCamelCase    :    Optional[Any]								=      common_inputs["decoder_input_ids"].shape[1]
												__UpperCamelCase							,				__UpperCamelCase    :    int								=      self.num_attention_heads
												__UpperCamelCase    :    List[Any]								=      (
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												__UpperCamelCase    :    Optional[Any]								=      decoder_seq_length
												__UpperCamelCase    :    Union[str, Any]								=      (
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)
												__UpperCamelCase    :    str								=      torch.cat(
												    [common_inputs["decoder_attention_mask"], torch.ones(__UpperCamelCase , __UpperCamelCase    )] , dim=1    )
												__UpperCamelCase    :    Dict								=      []
												__UpperCamelCase							,				__UpperCamelCase    :    Union[str, Any]								=      self.num_layers

												for _ in range(__UpperCamelCase    ):
															common_inputs["past_key_values"].append(
															    (
															        torch.zeros(__UpperCamelCase    ),
															        torch.zeros(__UpperCamelCase    ),
															        torch.zeros(__UpperCamelCase    ),
															        torch.zeros(__UpperCamelCase    ),
															    )    )
									return common_inputs

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = -1 , __UpperCamelCase = -1 , __UpperCamelCase = False , __UpperCamelCase = None , )							->	Mapping[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[int]								=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase    )

									if self.use_past:
												if not is_torch_available():
															raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."    )
												else:
															import torch
												__UpperCamelCase							,				__UpperCamelCase    :    List[Any]								=      common_inputs["input_ids"].shape
												__UpperCamelCase    :    List[str]								=      seqlen
												__UpperCamelCase							,				__UpperCamelCase    :    Dict								=      self.num_layers
												__UpperCamelCase							,				__UpperCamelCase    :    Tuple								=      self.num_attention_heads
												__UpperCamelCase    :    int								=      (
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												__UpperCamelCase    :    Optional[int]								=      common_inputs["attention_mask"].dtype
												__UpperCamelCase    :    Union[str, Any]								=      torch.cat(
												    [common_inputs["attention_mask"], torch.ones(__UpperCamelCase , __UpperCamelCase , dtype=__UpperCamelCase    )] , dim=1    )
												__UpperCamelCase    :    str								=      [
												    (torch.zeros(__UpperCamelCase    ), torch.zeros(__UpperCamelCase    )) for _ in range(__UpperCamelCase    )
												]
									return common_inputs

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = -1 , __UpperCamelCase = -1 , __UpperCamelCase = False , __UpperCamelCase = None , )							->	Mapping[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      compute_effective_axis_dimension(
									    __UpperCamelCase , 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
									__UpperCamelCase    :    Tuple								=      tokenizer.num_special_tokens_to_add(__UpperCamelCase    )
									__UpperCamelCase    :    Optional[int]								=      compute_effective_axis_dimension(
									    __UpperCamelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=__UpperCamelCase    )

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

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = -1 , __UpperCamelCase = -1 , __UpperCamelCase = False , __UpperCamelCase = None , )							->	Mapping[str, Any]:




									'''simple docstring'''


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

									elif self.task == "causal-lm":
												__UpperCamelCase    :    Tuple								=      self._generate_dummy_inputs_for_causal_lm(
												    __UpperCamelCase , batch_size=__UpperCamelCase , seq_length=__UpperCamelCase , is_pair=__UpperCamelCase , framework=__UpperCamelCase    )
									else:
												__UpperCamelCase    :    Optional[int]								=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    __UpperCamelCase , batch_size=__UpperCamelCase , seq_length=__UpperCamelCase , is_pair=__UpperCamelCase , framework=__UpperCamelCase    )

									return common_inputs

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase    )							->	Union[str, Any]:




									'''simple docstring'''


									if self.task in ["default", "seq2seq-lm"]:
												__UpperCamelCase    :    Dict								=      super()._flatten_past_key_values_(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase    )
									else:
												__UpperCamelCase    :    Optional[Any]								=      super(__UpperCamelCase , self    )._flatten_past_key_values_(
												    __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase    )




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase    )							->	List[str]:




									'''simple docstring'''


									if direction not in ["inputs", "outputs"]:
												raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''    )

									__UpperCamelCase    :    List[Any]								=      "past_key_values" if direction == "inputs" else "present"
									__UpperCamelCase							,				__UpperCamelCase    :    Tuple								=      self.num_layers

									__UpperCamelCase    :    Optional[int]								=      "past_encoder_sequence"
									__UpperCamelCase    :    Optional[Any]								=      "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

									for i in range(__UpperCamelCase    ):
												__UpperCamelCase    :    str								=      {0: "batch", 2: decoder_sequence}
												__UpperCamelCase    :    Union[str, Any]								=      {0: "batch", 2: decoder_sequence}
												__UpperCamelCase    :    Any								=      {0: "batch", 2: encoder_sequence}
												__UpperCamelCase    :    Union[str, Any]								=      {0: "batch", 2: encoder_sequence}


lowercase     :       dict[str, float]        =       {
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0009842073,
    "short-ton": 0.0011023122,
    "pound": 2.2046244202,
    "stone": 0.1574731728,
    "ounce": 35.273990723,
    "carrat": 5000,
    "atomic-mass-unit": 6.0_22_13_66_52E26,
}

lowercase     :       dict[str, float]        =       {
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.04608,
    "short-ton": 907.184,
    "pound": 0.453592,
    "stone": 6.35029,
    "ounce": 0.0283495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.6_60_54_01_99E-27,
}







def  UpperCAmelCase_   (_lowerCAmelCase :		str     ,			_lowerCAmelCase :		str     ,			_lowerCAmelCase :		float		):
			if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
						__UpperCamelCase    :    Dict								=      (
						    F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
						    F'''Supported values are: {", ".join(_lowerCAmelCase		)}'''
						)
						raise ValueError(_lowerCAmelCase		)
			return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
					import doctest

					doctest.testmod()