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


__lowerCamelCase :     List[str]          =  logging.get_logger(__name__)

__lowerCamelCase :     str          =  {
    '''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__						( A__ ):
					A     	=							'm2m_100'
					A     	=							['past_key_values']
					A     	=							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

					def __init__(   self				:	List[Any],_A				:	int=12_8112,_A				:	Tuple=1024,_A				:	List[Any]=12,_A				:	Tuple=4096,_A				:	Any=16,_A				:	List[Any]=12,_A				:	int=4096,_A				:	List[str]=16,_A				:	Union[str, Any]=0.05,_A				:	Optional[int]=0.05,_A				:	int=True,_A				:	Optional[int]=True,_A				:	List[Any]="relu",_A				:	str=1024,_A				:	Union[str, Any]=0.1,_A				:	Union[str, Any]=0.1,_A				:	Optional[Any]=0.0,_A				:	List[Any]=0.02,_A				:	Dict=2,_A				:	List[Any]=True,_A				:	Optional[int]=1,_A				:	Optional[int]=0,_A				:	Optional[Any]=2,**_A				:	List[str],):







							"""simple docstring"""






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

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







class 		a__						( A__ ):

					@property
					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   OrderedDict(
							    [
							        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
							        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
							    ]   )

							if self.use_past:
									SCREAMING_SNAKE_CASE_  :		str    				=   {0: "batch"}
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   {0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   {0: "batch", 1: "decoder_sequence"}
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   {0: "batch", 1: "decoder_sequence"}

							if self.use_past:
									self.fill_with_past_key_values_(_A,direction="inputs"   )
							return common_inputs

					def     __UpperCamelCase       (   self				:	int,_A				:	PreTrainedTokenizer,_A				:	int = -1,_A				:	int = -1,_A				:	bool = False,_A				:	Optional[TensorType] = None,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   compute_effective_axis_dimension(
							    _A,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
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   tokenizer.num_special_tokens_to_add(_A   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   compute_effective_axis_dimension(
							    _A,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=_A   )

							# Generate dummy inputs according to compute batch and sequence
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   [" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
							SCREAMING_SNAKE_CASE_  :		Dict    				=   dict(tokenizer(_A,return_tensors=_A   )   )
							return common_inputs

					def     __UpperCamelCase       (   self				:	Optional[Any],_A				:	PreTrainedTokenizer,_A				:	int = -1,_A				:	int = -1,_A				:	bool = False,_A				:	Optional[TensorType] = None,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _A,_A,_A,_A,_A   )

							# Generate decoder inputs
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   seq_length if not self.use_past else 1
							SCREAMING_SNAKE_CASE_  :		int    				=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    _A,_A,_A,_A,_A   )
							SCREAMING_SNAKE_CASE_  :		int    				=   {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
							SCREAMING_SNAKE_CASE_  :		Dict    				=   dict(**_A,**_A   )

							if self.use_past:
									if not is_torch_available():
											raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
									else:
											import torch
									SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Any    				=   common_inputs["input_ids"].shape
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   common_inputs["decoder_input_ids"].shape[1]
									SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		List[str]    				=   self.num_attention_heads
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   (
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   decoder_seq_length + 3
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   (
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   torch.cat(
									    [common_inputs["decoder_attention_mask"], torch.ones(_A,_A   )],dim=1   )

									SCREAMING_SNAKE_CASE_  :		Tuple    				=   []
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.num_layers
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   min(_A,_A   )
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   max(_A,_A   ) - min_num_layers
									SCREAMING_SNAKE_CASE_  :		str    				=   "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

									for _ in range(_A   ):
											common_inputs["past_key_values"].append(
											    (
											        torch.zeros(_A   ),
											        torch.zeros(_A   ),
											        torch.zeros(_A   ),
											        torch.zeros(_A   ),
											    )   )
									# TODO: test this.
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   encoder_shape if remaining_side_name == "encoder" else decoder_shape
									for _ in range(_A,_A   ):
											common_inputs["past_key_values"].append((torch.zeros(_A   ), torch.zeros(_A   ))   )
							return common_inputs






					A     	=							_generate_dummy_inputs_for_default_and_seqaseq_lm

__lowerCamelCase :     List[Any]          =  {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'




def 				_snake_case     (       lowerCAmelCase							:       str     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   hex_string.strip().lower()
		if not hex_string:
				raise ValueError("Empty string was passed to the function"     )
		SCREAMING_SNAKE_CASE_  :		str    				=   hex_string[0] == "-"
		if is_negative:
				SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   hex_string[1:]
		if not all(char in hex_table for char in hex_string     ):
				raise ValueError("Non-hexadecimal value was passed to the function"     )
		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   0
		for char in hex_string:
				SCREAMING_SNAKE_CASE_  :		Tuple    				=   1_6 * decimal_number + hex_table[char]
		return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
		from doctest import testmod

		testmod()
