

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


a_      =     logging.get_logger(__name__)

a_      =     {
    '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 _UpperCamelCase				(				A__ ):
			'''simple docstring'''







			lowerCamelCase__							     ='blenderbot-small'
			lowerCamelCase__							     =['past_key_values']
			lowerCamelCase__							     ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
			def __init__(      self				:			List[str]   ,   a				:			str=5_0265   ,   a				:			Optional[int]=512   ,   a				:			Any=8   ,   a				:			str=2048   ,   a				:			str=16   ,   a				:			Tuple=8   ,   a				:			Any=2048   ,   a				:			Optional[Any]=16   ,   a				:			Optional[int]=0.0   ,   a				:			Any=0.0   ,   a				:			Dict=True   ,   a				:			List[Any]=True   ,   a				:			int="gelu"   ,   a				:			List[Any]=512   ,   a				:			Optional[int]=0.1   ,   a				:			List[Any]=0.0   ,   a				:			Union[str, Any]=0.0   ,   a				:			List[str]=0.02   ,   a				:			Optional[Any]=1   ,   a				:			Dict=False   ,   a				:			Dict=0   ,   a				:			str=1   ,   a				:			Optional[Any]=2   ,   a				:			Tuple=2   ,   **a				:			Any   ,   )				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							vocab_size
				SCREAMING_SNAKE_CASE :   Any					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							d_model
				SCREAMING_SNAKE_CASE :   List[Any]					  =							encoder_ffn_dim
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							encoder_layers
				SCREAMING_SNAKE_CASE :   Any					  =							encoder_attention_heads
				SCREAMING_SNAKE_CASE :   List[str]					  =							decoder_ffn_dim
				SCREAMING_SNAKE_CASE :   Any					  =							decoder_layers
				SCREAMING_SNAKE_CASE :   str					  =							decoder_attention_heads
				SCREAMING_SNAKE_CASE :   List[str]					  =							dropout
				SCREAMING_SNAKE_CASE :   Dict					  =							attention_dropout
				SCREAMING_SNAKE_CASE :   List[str]					  =							activation_dropout
				SCREAMING_SNAKE_CASE :   str					  =							activation_function
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							init_std
				SCREAMING_SNAKE_CASE :   str					  =							encoder_layerdrop
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							decoder_layerdrop
				SCREAMING_SNAKE_CASE :   List[Any]					  =							use_cache
				SCREAMING_SNAKE_CASE :   List[str]					  =							encoder_layers
				SCREAMING_SNAKE_CASE :   List[Any]					  =							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 _UpperCamelCase				(				A__ ):
			'''simple docstring'''







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

				"""simple docstring"""

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

					if self.use_past:
						SCREAMING_SNAKE_CASE :   List[Any]					  =							{0: "batch"}
						SCREAMING_SNAKE_CASE :   Any					  =							{0: "batch", 1: "past_decoder_sequence + sequence"}
					else:
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							{0: "batch", 1: "decoder_sequence"}
						SCREAMING_SNAKE_CASE :   List[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.
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							OrderedDict(
					    [
					        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
					        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
					    ]						)
					if self.use_past:
						SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.num_layers
						for i in range(__snake_case						):
							SCREAMING_SNAKE_CASE :   str					  =							{0: "batch", 2: "past_sequence + sequence"}
							SCREAMING_SNAKE_CASE :   Any					  =							{0: "batch", 2: "past_sequence + sequence"}
				else:
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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 				__UpperCamelCase			(      self				:			Optional[int]						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE :   List[str]					  =							super().outputs
				else:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							super(__snake_case   ,   self						).outputs
					if self.use_past:
						SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							self.num_layers
						for i in range(__snake_case						):
							SCREAMING_SNAKE_CASE :   List[str]					  =							{0: "batch", 2: "past_sequence + sequence"}
							SCREAMING_SNAKE_CASE :   Any					  =							{0: "batch", 2: "past_sequence + sequence"}
				return common_outputs
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			PreTrainedTokenizer   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			bool = False   ,   a				:			Optional[TensorType] = None   ,   )				->							Mapping[str, Any]:

				"""simple docstring"""

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

				# Generate decoder inputs
				SCREAMING_SNAKE_CASE :   str					  =							seq_length if not self.use_past else 1
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    __snake_case   ,   __snake_case   ,   __snake_case   ,   __snake_case   ,   __snake_case						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
				SCREAMING_SNAKE_CASE :   Tuple					  =							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
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							common_inputs["input_ids"].shape
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							common_inputs["decoder_input_ids"].shape[1]
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							self.num_attention_heads
					SCREAMING_SNAKE_CASE :   Tuple					  =							(
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							decoder_seq_length + 3
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							(
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

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

					SCREAMING_SNAKE_CASE :   List[str]					  =							[]
					# If the number of encoder and decoder layers are present in the model configuration, both are considered
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.num_layers
					SCREAMING_SNAKE_CASE :   str					  =							min(__snake_case   ,   __snake_case						)
					SCREAMING_SNAKE_CASE :   List[Any]					  =							max(__snake_case   ,   __snake_case						) - min_num_layers
					SCREAMING_SNAKE_CASE :   List[str]					  =							"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.
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			PreTrainedTokenizer   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			bool = False   ,   a				:			Optional[TensorType] = None   ,   )				->							Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, 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
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   int					  =							common_inputs["input_ids"].shape
					# Not using the same length for past_key_values
					SCREAMING_SNAKE_CASE :   List[Any]					  =							seqlen + 2
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							self.num_layers
					SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							self.num_attention_heads
					SCREAMING_SNAKE_CASE :   str					  =							(
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE :   List[str]					  =							common_inputs["attention_mask"].dtype
					SCREAMING_SNAKE_CASE :   Dict					  =							torch.cat(
					    [common_inputs["attention_mask"], torch.ones(__snake_case   ,   __snake_case   ,   dtype=__snake_case						)]   ,   dim=1						)
					SCREAMING_SNAKE_CASE :   List[Any]					  =							[
					    (torch.zeros(__snake_case						), torch.zeros(__snake_case						)) for _ in range(__snake_case						)
					]
				return common_inputs
			def 				__UpperCamelCase			(      self				:			Union[str, Any]   ,   a				:			PreTrainedTokenizer   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			bool = False   ,   a				:			Optional[TensorType] = None   ,   )				->							Mapping[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							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
				SCREAMING_SNAKE_CASE :   List[Any]					  =							tokenizer.num_special_tokens_to_add(__snake_case						)
				SCREAMING_SNAKE_CASE :   str					  =							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
				SCREAMING_SNAKE_CASE :   int					  =							[" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							dict(tokenizer(__snake_case   ,   return_tensors=__snake_case						)						)
				return common_inputs
			def 				__UpperCamelCase			(      self				:			Any   ,   a				:			PreTrainedTokenizer   ,   a				:			int = -1   ,   a				:			int = -1   ,   a				:			bool = False   ,   a				:			Optional[TensorType] = None   ,   )				->							Mapping[str, Any]:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE :   List[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":
					SCREAMING_SNAKE_CASE :   Optional[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:
					SCREAMING_SNAKE_CASE :   List[str]					  =							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 				__UpperCamelCase			(      self				:			str   ,   a				:			int   ,   a				:			List[str]   ,   a				:			Optional[Any]   ,   a				:			Dict						)				->							Any:

				"""simple docstring"""

				if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE :   Tuple					  =							super()._flatten_past_key_values_(__snake_case   ,   __snake_case   ,   __snake_case   ,   __snake_case						)
				else:
					SCREAMING_SNAKE_CASE :   Dict					  =							super(__snake_case   ,   self						)._flatten_past_key_values_(
					    __snake_case   ,   __snake_case   ,   __snake_case   ,   __snake_case						)






'''simple docstring'''




from __future__ import annotations

_SCREAMING_SNAKE_CASE							     =			1.6021E-19  # units = C





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  tuple[str, float]:
   if (conductivity, electron_conc, mobility).count(0		) != 1:
      raise ValueError("""You cannot supply more or less than 2 values"""		)
   elif conductivity < 0:
      raise ValueError("""Conductivity cannot be negative"""		)
   elif electron_conc < 0:
      raise ValueError("""Electron concentration cannot be negative"""		)
   elif mobility < 0:
      raise ValueError("""mobility cannot be negative"""		)
   elif conductivity == 0:
      return (
          "conductivity",
          mobility * electron_conc * ELECTRON_CHARGE,
      )
   elif electron_conc == 0:
      return (
          "electron_conc",
          conductivity / (mobility * ELECTRON_CHARGE),
      )
   else:
      return (
          "mobility",
          conductivity / (electron_conc * ELECTRON_CHARGE),
      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

