


'''simple docstring'''




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	:   Optional[Any]   		=					logging.get_logger(__name__)

__lowercase	:   int   		=					{
    """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 _snake_case (			a__ ):
							"""simple docstring"""





							__a				       =  '''blenderbot'''
							__a				       =  ['''past_key_values''']
							__a				       =  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







							def __init__( self      ,					__A=8008      ,					__A=128      ,					__A=2      ,					__A=10240      ,					__A=32      ,					__A=24      ,					__A=10240      ,					__A=32      ,					__A=0.0      ,					__A=0.0      ,					__A=True      ,					__A=True      ,					__A="gelu"      ,					__A=2560      ,					__A=0.1      ,					__A=0.0      ,					__A=0.0      ,					__A=0.02      ,					__A=1      ,					__A=False      ,					__A=0      ,					__A=1      ,					__A=2      ,					__A=3      ,					__A=2      ,					**__A      ,					)  ->Optional[Any]:



											"""simple docstring"""







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

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





class _snake_case (			a__ ):
							"""simple docstring"""





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



											"""simple docstring"""







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



											"""simple docstring"""







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







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



											"""simple docstring"""







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

											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		:   List[str]           =       common_inputs["input_ids"].shape
															__lowerCamelCase		:   Any           =       common_inputs["decoder_input_ids"].shape[1]
															__lowerCamelCase    ,       __lowerCamelCase		:   str           =       self.num_attention_heads
															__lowerCamelCase		:   Optional[int]           =       (
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															__lowerCamelCase		:   Dict           =       decoder_seq_length
															__lowerCamelCase		:   Any           =       (
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)
															__lowerCamelCase		:   Union[str, Any]           =       torch.cat(
															    [common_inputs["decoder_attention_mask"], torch.ones(__A      ,					__A						)]      ,					dim=1						)
															__lowerCamelCase		:   Optional[Any]           =       []
															__lowerCamelCase    ,       __lowerCamelCase		:   str           =       self.num_layers

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







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



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __A      ,					__A      ,					__A      ,					__A      ,					__A						)

											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		:   List[Any]           =       common_inputs["input_ids"].shape
															__lowerCamelCase		:   Optional[int]           =       seqlen
															__lowerCamelCase    ,       __lowerCamelCase		:   Any           =       self.num_layers
															__lowerCamelCase    ,       __lowerCamelCase		:   Tuple           =       self.num_attention_heads
															__lowerCamelCase		:   str           =       (
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															__lowerCamelCase		:   List[Any]           =       common_inputs["attention_mask"].dtype
															__lowerCamelCase		:   Optional[int]           =       torch.cat(
															    [common_inputs["attention_mask"], torch.ones(__A      ,					__A      ,					dtype=__A						)]      ,					dim=1						)
															__lowerCamelCase		:   List[Any]           =       [
															    (torch.zeros(__A						), torch.zeros(__A						)) for _ in range(__A						)
															]
											return common_inputs







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



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       compute_effective_axis_dimension(
											    __A      ,					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
											__lowerCamelCase		:   int           =       tokenizer.num_special_tokens_to_add(__A						)
											__lowerCamelCase		:   str           =       compute_effective_axis_dimension(
											    __A      ,					fixed_dimension=OnnxConfig.default_fixed_sequence      ,					num_token_to_add=__A						)

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







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



											"""simple docstring"""







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

											elif self.task == "causal-lm":
															__lowerCamelCase		:   Union[str, Any]           =       self._generate_dummy_inputs_for_causal_lm(
															    __A      ,					batch_size=__A      ,					seq_length=__A      ,					is_pair=__A      ,					framework=__A						)
											else:
															__lowerCamelCase		:   Any           =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
															    __A      ,					batch_size=__A      ,					seq_length=__A      ,					is_pair=__A      ,					framework=__A						)

											return common_inputs







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A      ,					__A						)  ->Union[str, Any]:



											"""simple docstring"""







											if self.task in ["default", "seq2seq-lm"]:
															__lowerCamelCase		:   str           =       super()._flatten_past_key_values_(__A      ,					__A      ,					__A      ,					__A						)
											else:
															__lowerCamelCase		:   Any           =       super(__A      ,					self						)._flatten_past_key_values_(
															    __A      ,					__A      ,					__A      ,					__A						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Optional[int]:



											"""simple docstring"""







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

											__lowerCamelCase		:   Dict           =       "past_key_values" if direction == "inputs" else "present"
											__lowerCamelCase    ,       __lowerCamelCase		:   str           =       self.num_layers

											__lowerCamelCase		:   List[Any]           =       "past_encoder_sequence"
											__lowerCamelCase		:   List[Any]           =       "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

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









'''simple docstring'''




def 				a_							(    lowercase				,    lowercase     )      ->list:






				'''simple docstring'''

				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       len(lowercase     )
				__lowerCamelCase		:   int           =       (
				    first_input_list_length
				    if first_input_list_length > second_input_list_length
				    else second_input_list_length
				)
				__lowerCamelCase		:   list           =       []
				for char_count in range(lowercase     ):
								if char_count < first_input_list_length:
												output_result_list.append(first_input_list[char_count]     )
								if char_count < second_input_list_length:
												output_result_list.append(second_input_list[char_count]     )

				return output_result_list


if __name__ == "__main__":
				print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)





