

"""simple docstring"""





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

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


_SCREAMING_SNAKE_CASE							:	Tuple										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	List[Any]										=			{
    """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json""",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}

class      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    'm2m_100'
			SCREAMING_SNAKE_CASE_      =    ['past_key_values']
			SCREAMING_SNAKE_CASE_      =    {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






			def __init__(				self  :       Dict    ,	lowercase_  :       Dict=12_8112    ,	lowercase_  :       Dict=1024    ,	lowercase_  :       Optional[Any]=12    ,	lowercase_  :       Union[str, Any]=4096    ,	lowercase_  :       Optional[Any]=16    ,	lowercase_  :       Optional[int]=12    ,	lowercase_  :       Optional[Any]=4096    ,	lowercase_  :       Optional[int]=16    ,	lowercase_  :       Any=0.0_5    ,	lowercase_  :       Optional[Any]=0.0_5    ,	lowercase_  :       Dict=True    ,	lowercase_  :       Optional[int]=True    ,	lowercase_  :       Optional[int]="relu"    ,	lowercase_  :       int=1024    ,	lowercase_  :       int=0.1    ,	lowercase_  :       Tuple=0.1    ,	lowercase_  :       Optional[int]=0.0    ,	lowercase_  :       List[Any]=0.0_2    ,	lowercase_  :       Optional[int]=2    ,	lowercase_  :       List[Any]=True    ,	lowercase_  :       List[Any]=1    ,	lowercase_  :       Dict=0    ,	lowercase_  :       Tuple=2    ,	**lowercase_  :       Any    ,	):
								UpperCamelCase__		:     Dict								=vocab_size
								UpperCamelCase__		:     List[Any]								=max_position_embeddings
								UpperCamelCase__		:     Tuple								=d_model
								UpperCamelCase__		:     Optional[Any]								=encoder_ffn_dim
								UpperCamelCase__		:     str								=encoder_layers
								UpperCamelCase__		:     Dict								=encoder_attention_heads
								UpperCamelCase__		:     int								=decoder_ffn_dim
								UpperCamelCase__		:     List[str]								=decoder_layers
								UpperCamelCase__		:     List[Any]								=decoder_attention_heads
								UpperCamelCase__		:     str								=dropout
								UpperCamelCase__		:     Optional[Any]								=attention_dropout
								UpperCamelCase__		:     Optional[Any]								=activation_dropout
								UpperCamelCase__		:     Union[str, Any]								=activation_function
								UpperCamelCase__		:     Tuple								=init_std
								UpperCamelCase__		:     str								=encoder_layerdrop
								UpperCamelCase__		:     List[str]								=decoder_layerdrop
								UpperCamelCase__		:     List[str]								=use_cache
								UpperCamelCase__		:     Union[str, Any]								=encoder_layers
								UpperCamelCase__		:     Union[str, Any]								=scale_embedding  # scale factor will be sqrt(d_model) if True

								super().__init__(
								    pad_token_id=lowercase_    ,	bos_token_id=lowercase_    ,	eos_token_id=lowercase_    ,	is_encoder_decoder=lowercase_    ,	decoder_start_token_id=lowercase_    ,	**lowercase_    ,	)



class      __a (   snake_case__     ):

			"""simple docstring"""

			@property
			def  _lowerCAmelCase      (				self  :       str    ):
								UpperCamelCase__		:     int								=OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								    ]    )

								if self.use_past:
													UpperCamelCase__		:     Optional[Any]								={0: '''batch'''}
													UpperCamelCase__		:     Tuple								={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
													UpperCamelCase__		:     Optional[Any]								={0: '''batch''', 1: '''decoder_sequence'''}
													UpperCamelCase__		:     Any								={0: '''batch''', 1: '''decoder_sequence'''}

								if self.use_past:
													self.fill_with_past_key_values_(lowercase_    ,	direction='''inputs'''    )
								return common_inputs






			def  _lowerCAmelCase      (				self  :       List[Any]    ,	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
								UpperCamelCase__		:     Optional[int]								=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__		:     Tuple								=tokenizer.num_special_tokens_to_add(lowercase_    )
								UpperCamelCase__		:     str								=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__		:     List[Any]								=[''' '''.join([tokenizer.unk_token]    ) * seq_length] * batch_size
								UpperCamelCase__		:     List[Any]								=dict(tokenizer(lowercase_    ,	return_tensors=lowercase_    )    )
								return common_inputs






			def  _lowerCAmelCase      (				self  :       Union[str, Any]    ,	lowercase_  :       PreTrainedTokenizer    ,	lowercase_  :       int = -1    ,	lowercase_  :       int = -1    ,	lowercase_  :       bool = False    ,	lowercase_  :       Optional[TensorType] = None    ,	):
								UpperCamelCase__		:     Optional[int]								=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowercase_    ,	lowercase_    ,	lowercase_    ,	lowercase_    ,	lowercase_    )

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

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

													UpperCamelCase__		:     int								=[]
													# If the number of encoder and decoder layers are present in the model configuration, both are considered
													UpperCamelCase__	,      UpperCamelCase__		:     Tuple								=self.num_layers
													UpperCamelCase__		:     Optional[Any]								=min(lowercase_    ,	lowercase_    )
													UpperCamelCase__		:     Any								=max(lowercase_    ,	lowercase_    ) - min_num_layers
													UpperCamelCase__		:     Tuple								='''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__		:     List[str]								=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




			SCREAMING_SNAKE_CASE_      =    _generate_dummy_inputs_for_default_and_seqaseq_lm





"""simple docstring"""





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


_SCREAMING_SNAKE_CASE							:	Any										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	Optional[Any]										=			{
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}

class      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    'graphormer'
			SCREAMING_SNAKE_CASE_      =    ['past_key_values']






			def __init__(				self  :       Optional[int]    ,	lowercase_  :       int = 1    ,	lowercase_  :       int = 512 * 9    ,	lowercase_  :       int = 512 * 3    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 512    ,	lowercase_  :       int = 128    ,	lowercase_  :       int = 5    ,	lowercase_  :       int = 1024    ,	lowercase_  :       str = "multi_hop"    ,	lowercase_  :       int = 512    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 12    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 768    ,	lowercase_  :       int = 32    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.1    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       bool = False    ,	lowercase_  :       str = "gelu"    ,	lowercase_  :       float = None    ,	lowercase_  :       bool = False    ,	lowercase_  :       int = 0    ,	lowercase_  :       bool = False    ,	lowercase_  :       float = 0.0    ,	lowercase_  :       int = 8    ,	lowercase_  :       int = None    ,	lowercase_  :       int = None    ,	lowercase_  :       bool = True    ,	lowercase_  :       bool = True    ,	lowercase_  :       List[str]=0    ,	lowercase_  :       Union[str, Any]=1    ,	lowercase_  :       str=2    ,	**lowercase_  :       str    ,	):
								UpperCamelCase__		:     Dict								=num_classes
								UpperCamelCase__		:     str								=num_atoms
								UpperCamelCase__		:     Union[str, Any]								=num_in_degree
								UpperCamelCase__		:     Optional[int]								=num_out_degree
								UpperCamelCase__		:     List[str]								=num_edges
								UpperCamelCase__		:     List[str]								=num_spatial
								UpperCamelCase__		:     Dict								=num_edge_dis
								UpperCamelCase__		:     Tuple								=edge_type
								UpperCamelCase__		:     str								=multi_hop_max_dist
								UpperCamelCase__		:     Optional[Any]								=spatial_pos_max
								UpperCamelCase__		:     Tuple								=max_nodes
								UpperCamelCase__		:     Optional[Any]								=num_hidden_layers
								UpperCamelCase__		:     Optional[Any]								=embedding_dim
								UpperCamelCase__		:     Dict								=embedding_dim
								UpperCamelCase__		:     Dict								=ffn_embedding_dim
								UpperCamelCase__		:     str								=num_attention_heads
								UpperCamelCase__		:     Tuple								=dropout
								UpperCamelCase__		:     List[str]								=attention_dropout
								UpperCamelCase__		:     List[str]								=layerdrop
								UpperCamelCase__		:     Tuple								=encoder_normalize_before
								UpperCamelCase__		:     List[Any]								=pre_layernorm
								UpperCamelCase__		:     Optional[Any]								=apply_graphormer_init
								UpperCamelCase__		:     Tuple								=activation_fn
								UpperCamelCase__		:     Any								=embed_scale
								UpperCamelCase__		:     List[str]								=freeze_embeddings
								UpperCamelCase__		:     Optional[Any]								=num_trans_layers_to_freeze
								UpperCamelCase__		:     List[Any]								=share_input_output_embed
								UpperCamelCase__		:     Union[str, Any]								=traceable
								UpperCamelCase__		:     List[Any]								=q_noise
								UpperCamelCase__		:     Union[str, Any]								=qn_block_size

								# These parameters are here for future extensions
								# atm, the model only supports self attention
								UpperCamelCase__		:     Any								=kdim
								UpperCamelCase__		:     Tuple								=vdim
								UpperCamelCase__		:     Any								=self_attention
								UpperCamelCase__		:     List[str]								=bias

								super().__init__(
								    pad_token_id=lowercase_    ,	bos_token_id=lowercase_    ,	eos_token_id=lowercase_    ,	**lowercase_    ,	)


