'''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


lowercase_  		=			logging.get_logger(__name__)

lowercase_  		=			{
    '''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 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   int							=				'm2m_100'
		lowercase :   Tuple							=				['past_key_values']
		lowercase :   List[str]							=				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






		def __init__( self		,    __A=12_8112		,    __A=1024		,    __A=12		,    __A=4096		,    __A=16		,    __A=12		,    __A=4096		,    __A=16		,    __A=0.05		,    __A=0.05		,    __A=True		,    __A=True		,    __A="relu"		,    __A=1024		,    __A=0.1		,    __A=0.1		,    __A=0.0		,    __A=0.02		,    __A=2		,    __A=True		,    __A=1		,    __A=0		,    __A=2		,    **__A		,    )			->  Any:
								_lowerCAmelCase					    =vocab_size
								_lowerCAmelCase					    =max_position_embeddings
								_lowerCAmelCase					    =d_model
								_lowerCAmelCase					    =encoder_ffn_dim
								_lowerCAmelCase					    =encoder_layers
								_lowerCAmelCase					    =encoder_attention_heads
								_lowerCAmelCase					    =decoder_ffn_dim
								_lowerCAmelCase					    =decoder_layers
								_lowerCAmelCase					    =decoder_attention_heads
								_lowerCAmelCase					    =dropout
								_lowerCAmelCase					    =attention_dropout
								_lowerCAmelCase					    =activation_dropout
								_lowerCAmelCase					    =activation_function
								_lowerCAmelCase					    =init_std
								_lowerCAmelCase					    =encoder_layerdrop
								_lowerCAmelCase					    =decoder_layerdrop
								_lowerCAmelCase					    =use_cache
								_lowerCAmelCase					    =encoder_layers
								_lowerCAmelCase					    =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 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""






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

								if self.use_past:
														_lowerCAmelCase					    ={0: 'batch'}
														_lowerCAmelCase					    ={0: 'batch', 1: 'past_decoder_sequence + sequence'}
								else:
														_lowerCAmelCase					    ={0: 'batch', 1: 'decoder_sequence'}
														_lowerCAmelCase					    ={0: 'batch', 1: 'decoder_sequence'}

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






		def 		UpperCamelCase__							( self		,    __A		,    __A = -1		,    __A = -1		,    __A = False		,    __A = None		,    )			->  Mapping[str, Any]:
								# 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
								_lowerCAmelCase					    =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
								_lowerCAmelCase					    =tokenizer.num_special_tokens_to_add(__A					)
								_lowerCAmelCase					    =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
								_lowerCAmelCase					    =[' '.join([tokenizer.unk_token]					) * seq_length] * batch_size
								_lowerCAmelCase					    =dict(tokenizer(__A		,    return_tensors=__A					)					)
								return common_inputs






		def 		UpperCamelCase__							( self		,    __A		,    __A = -1		,    __A = -1		,    __A = False		,    __A = None		,    )			->  Mapping[str, Any]:
								_lowerCAmelCase					    =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __A		,    __A		,    __A		,    __A		,    __A					)

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

														_lowerCAmelCase					    =torch.cat(
														    [common_inputs['decoder_attention_mask'], torch.ones(__A		,    __A					)]		,    dim=1					)

														_lowerCAmelCase					    =[]
														# If the number of encoder and decoder layers are present in the model configuration, both are considered
														_lowerCAmelCase    ,					_lowerCAmelCase					    =self.num_layers
														_lowerCAmelCase					    =min(__A		,    __A					)
														_lowerCAmelCase					    =max(__A		,    __A					) - min_num_layers
														_lowerCAmelCase					    ='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.
														_lowerCAmelCase					    =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






		lowercase :   str							=				_generate_dummy_inputs_for_default_and_seqaseq_lm






'''simple docstring'''

from .prime_check import Test

Test()





