



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

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


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   List[Any]       						="""marian"""
				UpperCamelCase__			:   Optional[int]       						=["""past_key_values"""]
				UpperCamelCase__			:   List[Any]       						={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




				def __init__(  self   :  List[str],   __lowercase   :  Dict=5_8101,   __lowercase   :  int=None,   __lowercase   :  Optional[Any]=1024,   __lowercase   :  List[Any]=12,   __lowercase   :  str=4096,   __lowercase   :  Dict=16,   __lowercase   :  Optional[int]=12,   __lowercase   :  Optional[int]=4096,   __lowercase   :  str=16,   __lowercase   :  Any=0.0,   __lowercase   :  Dict=0.0,   __lowercase   :  Optional[int]=True,   __lowercase   :  Dict=True,   __lowercase   :  str="gelu",   __lowercase   :  Optional[int]=1024,   __lowercase   :  str=0.1,   __lowercase   :  Any=0.0,   __lowercase   :  Any=0.0,   __lowercase   :  Optional[int]=0.02,   __lowercase   :  List[str]=5_8100,   __lowercase   :  Optional[Any]=False,   __lowercase   :  Optional[int]=5_8100,   __lowercase   :  Optional[int]=0,   __lowercase   :  Any=0,   __lowercase   :  List[Any]=True,   **__lowercase   :  Any,   ):
							lowercase__   					=			vocab_size
							lowercase__   					=			decoder_vocab_size or vocab_size
							lowercase__   					=			max_position_embeddings
							lowercase__   					=			d_model
							lowercase__   					=			encoder_ffn_dim
							lowercase__   					=			encoder_layers
							lowercase__   					=			encoder_attention_heads
							lowercase__   					=			decoder_ffn_dim
							lowercase__   					=			decoder_layers
							lowercase__   					=			decoder_attention_heads
							lowercase__   					=			dropout
							lowercase__   					=			attention_dropout
							lowercase__   					=			activation_dropout
							lowercase__   					=			activation_function
							lowercase__   					=			init_std
							lowercase__   					=			encoder_layerdrop
							lowercase__   					=			decoder_layerdrop
							lowercase__   					=			use_cache
							lowercase__   					=			encoder_layers
							lowercase__   					=			scale_embedding  # scale factor will be sqrt(d_model) if True
							lowercase__   					=			share_encoder_decoder_embeddings
							super().__init__(
							    pad_token_id=__lowercase,   eos_token_id=__lowercase,   is_encoder_decoder=__lowercase,   decoder_start_token_id=__lowercase,   forced_eos_token_id=__lowercase,   **__lowercase,   )
class 			_snake_case     (   lowercase__):




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def 							A__ (  self   :  Tuple  ):
							if self.task in ["default", "seq2seq-lm"]:
										lowercase__   					=			OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
										        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
										    ]  )

										if self.use_past:
													lowercase__   					=			{0: "batch"}
													lowercase__   					=			{0: "batch", 1: "past_decoder_sequence + sequence"}
										else:
													lowercase__   					=			{0: "batch", 1: "decoder_sequence"}
													lowercase__   					=			{0: "batch", 1: "decoder_sequence"}

										if self.use_past:
													self.fill_with_past_key_values_(__lowercase,   direction="inputs"  )
							elif self.task == "causal-lm":
										# TODO: figure this case out.
										lowercase__   					=			OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
										        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
										    ]  )
										if self.use_past:
													lowercase__					,    lowercase__   					=			self.num_layers
													for i in range(__lowercase  ):
																lowercase__   					=			{0: "batch", 2: "past_sequence + sequence"}
																lowercase__   					=			{0: "batch", 2: "past_sequence + sequence"}
							else:
										lowercase__   					=			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 							A__ (  self   :  Any  ):
							if self.task in ["default", "seq2seq-lm"]:
										lowercase__   					=			super().outputs
							else:
										lowercase__   					=			super(__lowercase,   self  ).outputs
										if self.use_past:
													lowercase__					,    lowercase__   					=			self.num_layers
													for i in range(__lowercase  ):
																lowercase__   					=			{0: "batch", 2: "past_sequence + sequence"}
																lowercase__   					=			{0: "batch", 2: "past_sequence + sequence"}
							return common_outputs




				def 							A__ (  self   :  str,   __lowercase   :  PreTrainedTokenizer,   __lowercase   :  int = -1,   __lowercase   :  int = -1,   __lowercase   :  bool = False,   __lowercase   :  Optional[TensorType] = None,   ):
							lowercase__   					=			self._generate_dummy_inputs_for_encoder_and_decoder(
							    __lowercase,   __lowercase,   __lowercase,   __lowercase,   __lowercase  )

							# Generate decoder inputs
							lowercase__   					=			seq_length if not self.use_past else 1
							lowercase__   					=			self._generate_dummy_inputs_for_encoder_and_decoder(
							    __lowercase,   __lowercase,   __lowercase,   __lowercase,   __lowercase  )
							lowercase__   					=			{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
							lowercase__   					=			dict(**__lowercase,   **__lowercase  )

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

										lowercase__   					=			torch.cat(
										    [common_inputs["decoder_attention_mask"], torch.ones(__lowercase,   __lowercase  )],   dim=1  )

										lowercase__   					=			[]
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										lowercase__					,    lowercase__   					=			self.num_layers
										lowercase__   					=			min(__lowercase,   __lowercase  )
										lowercase__   					=			max(__lowercase,   __lowercase  ) - min_num_layers
										lowercase__   					=			"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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




				def 							A__ (  self   :  Union[str, Any],   __lowercase   :  PreTrainedTokenizer,   __lowercase   :  int = -1,   __lowercase   :  int = -1,   __lowercase   :  bool = False,   __lowercase   :  Optional[TensorType] = None,   ):
							lowercase__   					=			self._generate_dummy_inputs_for_encoder_and_decoder(
							    __lowercase,   __lowercase,   __lowercase,   __lowercase,   __lowercase  )

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

										lowercase__   					=			common_inputs["attention_mask"].dtype
										lowercase__   					=			torch.cat(
										    [common_inputs["attention_mask"], torch.ones(__lowercase,   __lowercase,   dtype=__lowercase  )],   dim=1  )
										lowercase__   					=			[
										    (torch.zeros(__lowercase  ), torch.zeros(__lowercase  )) for _ in range(__lowercase  )
										]
							return common_inputs




				def 							A__ (  self   :  Optional[int],   __lowercase   :  PreTrainedTokenizer,   __lowercase   :  int = -1,   __lowercase   :  int = -1,   __lowercase   :  bool = False,   __lowercase   :  Optional[TensorType] = None,   ):
							# Copied from OnnxConfig.generate_dummy_inputs
							# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							lowercase__   					=			compute_effective_axis_dimension(
							    __lowercase,   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
							lowercase__   					=			tokenizer.num_special_tokens_to_add(__lowercase  )
							lowercase__   					=			compute_effective_axis_dimension(
							    __lowercase,   fixed_dimension=OnnxConfig.default_fixed_sequence,   num_token_to_add=__lowercase  )

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




				def 							A__ (  self   :  List[Any],   __lowercase   :  PreTrainedTokenizer,   __lowercase   :  int = -1,   __lowercase   :  int = -1,   __lowercase   :  bool = False,   __lowercase   :  Optional[TensorType] = None,   ):
							if self.task in ["default", "seq2seq-lm"]:
										lowercase__   					=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    __lowercase,   batch_size=__lowercase,   seq_length=__lowercase,   is_pair=__lowercase,   framework=__lowercase  )

							else:
										lowercase__   					=			self._generate_dummy_inputs_for_causal_lm(
										    __lowercase,   batch_size=__lowercase,   seq_length=__lowercase,   is_pair=__lowercase,   framework=__lowercase  )

							return common_inputs




				def 							A__ (  self   :  List[Any],   __lowercase   :  List[str],   __lowercase   :  Union[str, Any],   __lowercase   :  Union[str, Any],   __lowercase   :  Optional[Any]  ):
							if self.task in ["default", "seq2seq-lm"]:
										lowercase__   					=			super()._flatten_past_key_values_(__lowercase,   __lowercase,   __lowercase,   __lowercase  )
							else:
										lowercase__   					=			super(__lowercase,   self  )._flatten_past_key_values_(
										    __lowercase,   __lowercase,   __lowercase,   __lowercase  )




				@property
				def 							A__ (  self   :  str  ):
							return 1e-4









from __future__ import annotations



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return len(set(SCREAMING_SNAKE_CASE_					)					) == len(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




