




'''simple docstring'''







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  (	__a ):
       __a				:     List[Any]   				=					"""marian"""
       __a				:     Union[str, Any]   				=					["""past_key_values"""]
       __a				:     List[str]   				=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
       def __init__(	self						:	List[Any]   ,				lowercase						:	Union[str, Any]=58_101   ,				lowercase						:	Tuple=None   ,				lowercase						:	str=1_024   ,				lowercase						:	Optional[int]=12   ,				lowercase						:	Optional[int]=4_096   ,				lowercase						:	int=16   ,				lowercase						:	List[Any]=12   ,				lowercase						:	int=4_096   ,				lowercase						:	Optional[int]=16   ,				lowercase						:	int=0.0   ,				lowercase						:	Tuple=0.0   ,				lowercase						:	Tuple=True   ,				lowercase						:	Union[str, Any]=True   ,				lowercase						:	List[Any]="gelu"   ,				lowercase						:	Tuple=1_024   ,				lowercase						:	str=0.1   ,				lowercase						:	str=0.0   ,				lowercase						:	Optional[int]=0.0   ,				lowercase						:	Dict=0.02   ,				lowercase						:	Union[str, Any]=58_100   ,				lowercase						:	List[str]=False   ,				lowercase						:	str=58_100   ,				lowercase						:	Any=0   ,				lowercase						:	Optional[Any]=0   ,				lowercase						:	Tuple=True   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		vocab_size
           UpperCAmelCase			       =		decoder_vocab_size or vocab_size
           UpperCAmelCase			       =		max_position_embeddings
           UpperCAmelCase			       =		d_model
           UpperCAmelCase			       =		encoder_ffn_dim
           UpperCAmelCase			       =		encoder_layers
           UpperCAmelCase			       =		encoder_attention_heads
           UpperCAmelCase			       =		decoder_ffn_dim
           UpperCAmelCase			       =		decoder_layers
           UpperCAmelCase			       =		decoder_attention_heads
           UpperCAmelCase			       =		dropout
           UpperCAmelCase			       =		attention_dropout
           UpperCAmelCase			       =		activation_dropout
           UpperCAmelCase			       =		activation_function
           UpperCAmelCase			       =		init_std
           UpperCAmelCase			       =		encoder_layerdrop
           UpperCAmelCase			       =		decoder_layerdrop
           UpperCAmelCase			       =		use_cache
           UpperCAmelCase			       =		encoder_layers
           UpperCAmelCase			       =		scale_embedding  # scale factor will be sqrt(d_model) if True
           UpperCAmelCase			       =		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 			_a  (	__a ):
       @property
       # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
       def     A      (	self						:	int				):



           '''simple docstring'''

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

               if self.use_past:
                   UpperCAmelCase			       =		{0: '''batch'''}
                   UpperCAmelCase			       =		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
               else:
                   UpperCAmelCase			       =		{0: '''batch''', 1: '''decoder_sequence'''}
                   UpperCAmelCase			       =		{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.
               UpperCAmelCase			       =		OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ]				)
               if self.use_past:
                   UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_layers
                   for i in range(lowercase				):
                       UpperCAmelCase			       =		{0: '''batch''', 2: '''past_sequence + sequence'''}
                       UpperCAmelCase			       =		{0: '''batch''', 2: '''past_sequence + sequence'''}
           else:
               UpperCAmelCase			       =		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				):



           '''simple docstring'''

           if self.task in ["default", "seq2seq-lm"]:
               UpperCAmelCase			       =		super().outputs
           else:
               UpperCAmelCase			       =		super(lowercase   ,				self				).outputs
               if self.use_past:
                   UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_layers
                   for i in range(lowercase				):
                       UpperCAmelCase			       =		{0: '''batch''', 2: '''past_sequence + sequence'''}
                       UpperCAmelCase			       =		{0: '''batch''', 2: '''past_sequence + sequence'''}
           return common_outputs
       def     A      (	self						:	Dict   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		self._generate_dummy_inputs_for_encoder_and_decoder(
               lowercase   ,				lowercase   ,				lowercase   ,				lowercase   ,				lowercase				)

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

               UpperCAmelCase			       =		torch.cat(
                   [common_inputs['''decoder_attention_mask'''], torch.ones(lowercase   ,				lowercase				)]   ,				dim=1				)

               UpperCAmelCase			       =		[]
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               UpperCAmelCase       ,     UpperCAmelCase			       =		self.num_layers
               UpperCAmelCase			       =		min(lowercase   ,				lowercase				)
               UpperCAmelCase			       =		max(lowercase   ,				lowercase				) - min_num_layers
               UpperCAmelCase			       =		'''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.
               UpperCAmelCase			       =		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						:	int   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

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

               UpperCAmelCase			       =		common_inputs['''attention_mask'''].dtype
               UpperCAmelCase			       =		torch.cat(
                   [common_inputs['''attention_mask'''], torch.ones(lowercase   ,				lowercase   ,				dtype=lowercase				)]   ,				dim=1				)
               UpperCAmelCase			       =		[
                   (torch.zeros(lowercase				), torch.zeros(lowercase				)) for _ in range(lowercase				)
               ]
           return common_inputs
       def     A      (	self						:	str   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		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
           UpperCAmelCase			       =		tokenizer.num_special_tokens_to_add(lowercase				)
           UpperCAmelCase			       =		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
           UpperCAmelCase			       =		[''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
           UpperCAmelCase			       =		dict(tokenizer(lowercase   ,				return_tensors=lowercase				)				)
           return common_inputs
       def     A      (	self						:	List[str]   ,				lowercase						:	PreTrainedTokenizer   ,				lowercase						:	int = -1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional[TensorType] = None   ,				):



           '''simple docstring'''

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

           else:
               UpperCAmelCase			       =		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						:	Any   ,				lowercase						:	Tuple   ,				lowercase						:	Any   ,				lowercase						:	Any				):



           '''simple docstring'''

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







       @property
       def     A      (	self						:	Any				):



           '''simple docstring'''

           return 1E-4






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A				={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''WavLMForAudioFrameClassification''',
			    '''WavLMForCTC''',
			    '''WavLMForSequenceClassification''',
			    '''WavLMForXVector''',
			    '''WavLMModel''',
			    '''WavLMPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


