





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


_a 						=      logging.get_logger(__name__)

_a 						=      {
    '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 					__A    (    UpperCamelCase_ ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""marian"""
			lowerCAmelCase_							     =						["""past_key_values"""]
			lowerCAmelCase_							     =						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





			def __init__(    self       ,  __lowerCAmelCase=5_8_1_0_1       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=1_0_2_4       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=4_0_9_6       ,  __lowerCAmelCase=1_6       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=4_0_9_6       ,  __lowerCAmelCase=1_6       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=1_0_2_4       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.0       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=5_8_1_0_0       ,  __lowerCAmelCase=False       ,  __lowerCAmelCase=5_8_1_0_0       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=True       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		decoder_vocab_size or vocab_size
				lowerCamelCase__								=		max_position_embeddings
				lowerCamelCase__								=		d_model
				lowerCamelCase__								=		encoder_ffn_dim
				lowerCamelCase__								=		encoder_layers
				lowerCamelCase__								=		encoder_attention_heads
				lowerCamelCase__								=		decoder_ffn_dim
				lowerCamelCase__								=		decoder_layers
				lowerCamelCase__								=		decoder_attention_heads
				lowerCamelCase__								=		dropout
				lowerCamelCase__								=		attention_dropout
				lowerCamelCase__								=		activation_dropout
				lowerCamelCase__								=		activation_function
				lowerCamelCase__								=		init_std
				lowerCamelCase__								=		encoder_layerdrop
				lowerCamelCase__								=		decoder_layerdrop
				lowerCamelCase__								=		use_cache
				lowerCamelCase__								=		encoder_layers
				lowerCamelCase__								=		scale_embedding  # scale factor will be sqrt(d_model) if True
				lowerCamelCase__								=		share_encoder_decoder_embeddings
				super().__init__(
				    pad_token_id=UpperCamelCase__       ,  eos_token_id=UpperCamelCase__       ,  is_encoder_decoder=UpperCamelCase__       ,  decoder_start_token_id=UpperCamelCase__       ,  forced_eos_token_id=UpperCamelCase__       ,  **UpperCamelCase__       ,  )






class 					__A    (    UpperCamelCase_ ):

			'''simple docstring'''







			@property
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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

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

					if self.use_past:
						self.fill_with_past_key_values_(UpperCamelCase__       ,  direction='''inputs'''							)
				elif self.task == "causal-lm":
					# TODO: figure this case out.
					lowerCamelCase__								=		OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
					    ]							)
					if self.use_past:
						lowerCamelCase__								=		self.num_layers
						for i in range(UpperCamelCase__							):
							lowerCamelCase__								=		{0: '''batch''', 2: '''past_sequence + sequence'''}
							lowerCamelCase__								=		{0: '''batch''', 2: '''past_sequence + sequence'''}
				else:
					lowerCamelCase__								=		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							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = -1       ,  __lowerCAmelCase = -1       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = None       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		self._generate_dummy_inputs_for_encoder_and_decoder(
				    UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__							)

				# Generate decoder inputs
				lowerCamelCase__								=		seq_length if not self.use_past else 1
				lowerCamelCase__								=		self._generate_dummy_inputs_for_encoder_and_decoder(
				    UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__							)
				lowerCamelCase__								=		{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
				lowerCamelCase__								=		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
					lowerCamelCase__								=		common_inputs['''input_ids'''].shape
					lowerCamelCase__								=		common_inputs['''decoder_input_ids'''].shape[1]
					lowerCamelCase__								=		self.num_attention_heads
					lowerCamelCase__								=		(
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					lowerCamelCase__								=		decoder_seq_length + 3
					lowerCamelCase__								=		(
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)

					lowerCamelCase__								=		torch.cat(
					    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase__       ,  UpperCamelCase__							)]       ,  dim=1							)

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

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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = -1       ,  __lowerCAmelCase = -1       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = None       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		self._generate_dummy_inputs_for_encoder_and_decoder(
				    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
					lowerCamelCase__								=		common_inputs['''input_ids'''].shape
					# Not using the same length for past_key_values
					lowerCamelCase__								=		seqlen + 2
					lowerCamelCase__								=		self.num_layers
					lowerCamelCase__								=		self.num_attention_heads
					lowerCamelCase__								=		(
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					lowerCamelCase__								=		common_inputs['''attention_mask'''].dtype
					lowerCamelCase__								=		torch.cat(
					    [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase__       ,  UpperCamelCase__       ,  dtype=UpperCamelCase__							)]       ,  dim=1							)
					lowerCamelCase__								=		[
					    (torch.zeros(UpperCamelCase__							), torch.zeros(UpperCamelCase__							)) for _ in range(UpperCamelCase__							)
					]
				return common_inputs





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = -1       ,  __lowerCAmelCase = -1       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = None       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		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
				lowerCamelCase__								=		tokenizer.num_special_tokens_to_add(UpperCamelCase__							)
				lowerCamelCase__								=		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
				lowerCamelCase__								=		[''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
				lowerCamelCase__								=		dict(tokenizer(UpperCamelCase__       ,  return_tensors=UpperCamelCase__							)							)
				return common_inputs





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = -1       ,  __lowerCAmelCase = -1       ,  __lowerCAmelCase = False       ,  __lowerCAmelCase = None       ,  ):



				'''simple docstring'''







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

				else:
					lowerCamelCase__								=		self._generate_dummy_inputs_for_causal_lm(
					    UpperCamelCase__       ,  batch_size=UpperCamelCase__       ,  seq_length=UpperCamelCase__       ,  is_pair=UpperCamelCase__       ,  framework=UpperCamelCase__							)

				return common_inputs





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase__								=		super()._flatten_past_key_values_(UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__							)
				else:
					lowerCamelCase__								=		super(UpperCamelCase__       ,  self							)._flatten_past_key_values_(
					    UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__       ,  UpperCamelCase__							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 1E-4




def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float(moles / volume						) * nfactor						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((moles * 0.0821 * temperature) / (volume)						)						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((moles * 0.0821 * temperature) / (pressure)						)						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						return round(float((pressure * volume) / (0.0821 * moles)						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





