






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


UpperCAmelCase__      			=						logging.get_logger(__name__)

UpperCAmelCase__      			=						{
    '''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_       (    __UpperCamelCase      ):

						"""simple docstring"""



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



						def __init__(self:					str ,					__UpperCAmelCase:					str=58101 ,					__UpperCAmelCase:					Optional[int]=None ,					__UpperCAmelCase:					Tuple=1024 ,					__UpperCAmelCase:					List[Any]=12 ,					__UpperCAmelCase:					str=4096 ,					__UpperCAmelCase:					List[str]=16 ,					__UpperCAmelCase:					Tuple=12 ,					__UpperCAmelCase:					List[str]=4096 ,					__UpperCAmelCase:					List[Any]=16 ,					__UpperCAmelCase:					Optional[Any]=0.0 ,					__UpperCAmelCase:					Optional[Any]=0.0 ,					__UpperCAmelCase:					Optional[Any]=True ,					__UpperCAmelCase:					Tuple=True ,					__UpperCAmelCase:					Optional[int]="gelu" ,					__UpperCAmelCase:					Optional[Any]=1024 ,					__UpperCAmelCase:					Any=0.1 ,					__UpperCAmelCase:					str=0.0 ,					__UpperCAmelCase:					Optional[Any]=0.0 ,					__UpperCAmelCase:					Optional[Any]=0.02 ,					__UpperCAmelCase:					str=58100 ,					__UpperCAmelCase:					Any=False ,					__UpperCAmelCase:					str=58100 ,					__UpperCAmelCase:					str=0 ,					__UpperCAmelCase:					Tuple=0 ,					__UpperCAmelCase:					Any=True ,					**__UpperCAmelCase:					int ,					)    ->     Optional[int]:




											'''simple docstring'''



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

						"""simple docstring"""



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




											'''simple docstring'''



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

																if self.use_past:
																					__a					:						List[str]      =					{0: "batch"}
																					__a					:						str      =					{0: "batch", 1: "past_decoder_sequence + sequence"}
																else:
																					__a					:						List[str]      =					{0: "batch", 1: "decoder_sequence"}
																					__a					:						List[Any]      =					{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.
																__a					:						Union[str, Any]      =					OrderedDict(
																    [
																        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																    ]   )
																if self.use_past:
																					__a      ,			__a					:						int      =					self.num_layers
																					for i in range(__UpperCAmelCase   ):
																										__a					:						Dict      =					{0: "batch", 2: "past_sequence + sequence"}
																										__a					:						Optional[int]      =					{0: "batch", 2: "past_sequence + sequence"}
											else:
																__a					:						List[str]      =					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        UpperCAmelCase__					(self:					Dict   )    ->     Mapping[str, Mapping[int, str]]:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					PreTrainedTokenizer ,					__UpperCAmelCase:					int = -1 ,					__UpperCAmelCase:					int = -1 ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					Optional[TensorType] = None ,					)    ->     Mapping[str, Any]:




											'''simple docstring'''



											__a					:						List[str]      =					self._generate_dummy_inputs_for_encoder_and_decoder(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )

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

																__a					:						Any      =					torch.cat(
																    [common_inputs["decoder_attention_mask"], torch.ones(__UpperCAmelCase ,					__UpperCAmelCase   )] ,					dim=1   )

																__a					:						List[Any]      =					[]
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																__a      ,			__a					:						Union[str, Any]      =					self.num_layers
																__a					:						str      =					min(__UpperCAmelCase ,					__UpperCAmelCase   )
																__a					:						List[Any]      =					max(__UpperCAmelCase ,					__UpperCAmelCase   ) - min_num_layers
																__a					:						Optional[Any]      =					"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.
																__a					:						Optional[Any]      =					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        UpperCAmelCase__					(self:					Union[str, Any] ,					__UpperCAmelCase:					PreTrainedTokenizer ,					__UpperCAmelCase:					int = -1 ,					__UpperCAmelCase:					int = -1 ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					Optional[TensorType] = None ,					)    ->     Mapping[str, Any]:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					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
																__a      ,			__a					:						Any      =					common_inputs["input_ids"].shape
																# Not using the same length for past_key_values
																__a					:						Optional[Any]      =					seqlen + 2
																__a      ,			__a					:						Union[str, Any]      =					self.num_layers
																__a      ,			__a					:						Union[str, Any]      =					self.num_attention_heads
																__a					:						Dict      =					(
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																__a					:						Tuple      =					common_inputs["attention_mask"].dtype
																__a					:						int      =					torch.cat(
																    [common_inputs["attention_mask"], torch.ones(__UpperCAmelCase ,					__UpperCAmelCase ,					dtype=__UpperCAmelCase   )] ,					dim=1   )
																__a					:						List[str]      =					[
																    (torch.zeros(__UpperCAmelCase   ), torch.zeros(__UpperCAmelCase   )) for _ in range(__UpperCAmelCase   )
																]
											return common_inputs



						def        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					PreTrainedTokenizer ,					__UpperCAmelCase:					int = -1 ,					__UpperCAmelCase:					int = -1 ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					Optional[TensorType] = None ,					)    ->     Mapping[str, Any]:




											'''simple docstring'''



											__a					:						Dict      =					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
											__a					:						Tuple      =					tokenizer.num_special_tokens_to_add(__UpperCAmelCase   )
											__a					:						int      =					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
											__a					:						List[Any]      =					[" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
											__a					:						Tuple      =					dict(tokenizer(__UpperCAmelCase ,					return_tensors=__UpperCAmelCase   )   )
											return common_inputs



						def        UpperCAmelCase__					(self:					Dict ,					__UpperCAmelCase:					PreTrainedTokenizer ,					__UpperCAmelCase:					int = -1 ,					__UpperCAmelCase:					int = -1 ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					Optional[TensorType] = None ,					)    ->     Mapping[str, Any]:




											'''simple docstring'''



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

											else:
																__a					:						Optional[Any]      =					self._generate_dummy_inputs_for_causal_lm(
																    __UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					seq_length=__UpperCAmelCase ,					is_pair=__UpperCAmelCase ,					framework=__UpperCAmelCase   )

											return common_inputs



						def        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					Tuple ,					__UpperCAmelCase:					List[Any] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					int   )    ->     Dict:




											'''simple docstring'''



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



						@property
						def        UpperCAmelCase__					(self:					Union[str, Any]   )    ->     float:




											'''simple docstring'''



											return 1E-4













from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__      			=						logging.get_logger(__name__)

UpperCAmelCase__      			=						{
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """glpn"""



						def __init__(self:					Tuple ,					__UpperCAmelCase:					Tuple=3 ,					__UpperCAmelCase:					Dict=4 ,					__UpperCAmelCase:					Any=[2, 2, 2, 2] ,					__UpperCAmelCase:					Optional[int]=[8, 4, 2, 1] ,					__UpperCAmelCase:					Dict=[32, 64, 160, 256] ,					__UpperCAmelCase:					List[str]=[7, 3, 3, 3] ,					__UpperCAmelCase:					Dict=[4, 2, 2, 2] ,					__UpperCAmelCase:					Optional[int]=[1, 2, 5, 8] ,					__UpperCAmelCase:					Dict=[4, 4, 4, 4] ,					__UpperCAmelCase:					List[str]="gelu" ,					__UpperCAmelCase:					str=0.0 ,					__UpperCAmelCase:					List[Any]=0.0 ,					__UpperCAmelCase:					Dict=0.02 ,					__UpperCAmelCase:					List[str]=0.1 ,					__UpperCAmelCase:					Union[str, Any]=1E-6 ,					__UpperCAmelCase:					Dict=64 ,					__UpperCAmelCase:					Dict=10 ,					__UpperCAmelCase:					Union[str, Any]=-1 ,					**__UpperCAmelCase:					Dict ,					)    ->     str:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )

											__a					:						Optional[Any]      =					num_channels
											__a					:						Tuple      =					num_encoder_blocks
											__a					:						int      =					depths
											__a					:						Union[str, Any]      =					sr_ratios
											__a					:						str      =					hidden_sizes
											__a					:						List[Any]      =					patch_sizes
											__a					:						Optional[Any]      =					strides
											__a					:						Any      =					mlp_ratios
											__a					:						Any      =					num_attention_heads
											__a					:						Any      =					hidden_act
											__a					:						List[Any]      =					hidden_dropout_prob
											__a					:						int      =					attention_probs_dropout_prob
											__a					:						List[str]      =					initializer_range
											__a					:						Optional[int]      =					drop_path_rate
											__a					:						Any      =					layer_norm_eps
											__a					:						List[Any]      =					decoder_hidden_size
											__a					:						Any      =					max_depth
											__a					:						int      =					head_in_index





