
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     :	str    = logging.get_logger(__name__)

A     :	Tuple    = {
    '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( a			):
				snake_case_						   =    '''marian'''
				snake_case_						   =    ['''past_key_values''']
				snake_case_						   =    {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




				def __init__(     self		,     _snake_case=58_101		,     _snake_case=None		,     _snake_case=1_024		,     _snake_case=12		,     _snake_case=4_096		,     _snake_case=16		,     _snake_case=12		,     _snake_case=4_096		,     _snake_case=16		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case=True		,     _snake_case=True		,     _snake_case="gelu"		,     _snake_case=1_024		,     _snake_case=0.1		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case=0.02		,     _snake_case=58_100		,     _snake_case=False		,     _snake_case=58_100		,     _snake_case=0		,     _snake_case=0		,     _snake_case=True		,     **_snake_case		,     )    ->      List[str]:


								'''simple docstring'''




								__a										=    vocab_size
								__a										=    decoder_vocab_size or vocab_size
								__a										=    max_position_embeddings
								__a										=    d_model
								__a										=    encoder_ffn_dim
								__a										=    encoder_layers
								__a										=    encoder_attention_heads
								__a										=    decoder_ffn_dim
								__a										=    decoder_layers
								__a										=    decoder_attention_heads
								__a										=    dropout
								__a										=    attention_dropout
								__a										=    activation_dropout
								__a										=    activation_function
								__a										=    init_std
								__a										=    encoder_layerdrop
								__a										=    decoder_layerdrop
								__a										=    use_cache
								__a										=    encoder_layers
								__a										=    scale_embedding  # scale factor will be sqrt(d_model) if True
								__a										=    share_encoder_decoder_embeddings
								super().__init__(
								    pad_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 			__A( a			):




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




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

												if self.use_past:
																__a										=    {0: '''batch'''}
																__a										=    {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
												else:
																__a										=    {0: '''batch''', 1: '''decoder_sequence'''}
																__a										=    {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.
												__a										=    OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												    ]				)
												if self.use_past:
																__a							,     __a										=    self.num_layers
																for i in range(_snake_case				):
																				__a										=    {0: '''batch''', 2: '''past_sequence + sequence'''}
																				__a										=    {0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
												__a										=    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"]:
												__a										=    super().outputs
								else:
												__a										=    super(_snake_case		,     self				).outputs
												if self.use_past:
																__a							,     __a										=    self.num_layers
																for i in range(_snake_case				):
																				__a										=    {0: '''batch''', 2: '''past_sequence + sequence'''}
																				__a										=    {0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs




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


								'''simple docstring'''




								__a										=    self._generate_dummy_inputs_for_encoder_and_decoder(
								    _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case				)

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

												__a										=    torch.cat(
												    [common_inputs['''decoder_attention_mask'''], torch.ones(_snake_case		,     _snake_case				)]		,     dim=1				)

												__a										=    []
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												__a							,     __a										=    self.num_layers
												__a										=    min(_snake_case		,     _snake_case				)
												__a										=    max(_snake_case		,     _snake_case				) - min_num_layers
												__a										=    '''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.
												__a										=    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 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = -1		,     _snake_case = -1		,     _snake_case = False		,     _snake_case = None		,     )    ->      Mapping[str, Any]:


								'''simple docstring'''




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

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




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


								'''simple docstring'''




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




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


								'''simple docstring'''




								if self.task in ["default", "seq2seq-lm"]:
												__a										=    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				)

								else:
												__a										=    self._generate_dummy_inputs_for_causal_lm(
												    _snake_case		,     batch_size=_snake_case		,     seq_length=_snake_case		,     is_pair=_snake_case		,     framework=_snake_case				)

								return common_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-4

from typing import Dict

from .base import GenericTensor, Pipeline







class 			__A( a			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if tokenize_kwargs is None:
												__a										=    {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''				)
												__a										=    truncation

								__a										=    tokenize_kwargs

								__a										=    {}
								if return_tensors is not None:
												__a										=    return_tensors

								return preprocess_params, {}, postprocess_params




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     **_snake_case				)    ->      Dict[str, GenericTensor]:


								'''simple docstring'''




								__a										=    self.framework
								__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)
								return model_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.model(**_snake_case				)
								return model_outputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=False				)    ->      Optional[int]:


								'''simple docstring'''




								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(     self		,     *_snake_case		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								return super().__call__(*_snake_case		,     **_snake_case				)