






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


__A          = logging.get_logger(__name__)

__A          = {
    '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 (       snake_case    ):





							"""simple docstring"""

							A_       =    "m2m_100"
							A_       =    ["past_key_values"]
							A_       =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

							def __init__(     self:      int						,   __A:      List[str]=12_81_12						,   __A:      Any=10_24						,   __A:      Optional[Any]=12						,   __A:      Dict=40_96						,   __A:      Dict=16						,   __A:      List[Any]=12						,   __A:      List[str]=40_96						,   __A:      Optional[int]=16						,   __A:      Union[str, Any]=0.05						,   __A:      List[Any]=0.05						,   __A:      Dict=True						,   __A:      Optional[Any]=True						,   __A:      List[str]="relu"						,   __A:      Any=10_24						,   __A:      Optional[Any]=0.1						,   __A:      Optional[Any]=0.1						,   __A:      Any=0.0						,   __A:      Any=0.02						,   __A:      int=2						,   __A:      Optional[int]=True						,   __A:      int=1						,   __A:      Tuple=0						,   __A:      List[Any]=2						,   **__A:      Tuple						,   )			-> Any:
												_A									=       vocab_size
												_A									=       max_position_embeddings
												_A									=       d_model
												_A									=       encoder_ffn_dim
												_A									=       encoder_layers
												_A									=       encoder_attention_heads
												_A									=       decoder_ffn_dim
												_A									=       decoder_layers
												_A									=       decoder_attention_heads
												_A									=       dropout
												_A									=       attention_dropout
												_A									=       activation_dropout
												_A									=       activation_function
												_A									=       init_std
												_A									=       encoder_layerdrop
												_A									=       decoder_layerdrop
												_A									=       use_cache
												_A									=       encoder_layers
												_A									=       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 (       snake_case    ):





							"""simple docstring"""

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

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

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

							def 					__A       (     self:      int						,   __A:      PreTrainedTokenizer						,   __A:      int = -1						,   __A:      int = -1						,   __A:      bool = False						,   __A:      Optional[TensorType] = 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
												_A									=       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
												_A									=       tokenizer.num_special_tokens_to_add(__A      )
												_A									=       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
												_A									=       [''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
												_A									=       dict(tokenizer(__A						,   return_tensors=__A      )      )
												return common_inputs

							def 					__A       (     self:      Union[str, Any]						,   __A:      PreTrainedTokenizer						,   __A:      int = -1						,   __A:      int = -1						,   __A:      bool = False						,   __A:      Optional[TensorType] = None						,   )			-> Mapping[str, Any]:
												_A									=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    __A						,   __A						,   __A						,   __A						,   __A      )

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

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

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











class   SCREAMING_SNAKE_CASE :





							"""simple docstring"""

							def __init__(     self:      Any      )			-> None:
												# numbermap keys are the number and its values are lists of indexes sorted
												# in ascending order
												_A									=       {}
												# indexmap keys are an index and it's values are the number at that index
												_A									=       {}

							def 					__A       (     self:      Any						,   __A:      list | str | range						,   __A:      int      )			-> list[int]:
												if isinstance(__A						,   (range, str)      ):
																	_A									=       list(__A      )
												elif not isinstance(__A						,   __A      ):
																	raise TypeError(
																	    '''binary_search_delete() only accepts either a list, range or str'''      )

												_A									=       0
												_A									=       len(__A      ) - 1

												while low <= high:
																	_A									=       (low + high) // 2
																	if array[mid] == item:
																						array.pop(__A      )
																						return array
																	elif array[mid] < item:
																						_A									=       mid + 1
																	else:
																						_A									=       mid - 1
												raise ValueError(
												    '''Either the item is not in the array or the array was unsorted'''      )

							def 					__A       (     self:      int						,   __A:      list | str | range						,   __A:      int      )			-> list[int]:
												if isinstance(__A						,   (range, str)      ):
																	_A									=       list(__A      )
												elif not isinstance(__A						,   __A      ):
																	raise TypeError(
																	    '''binary_search_insert() only accepts either a list, range or str'''      )

												_A									=       0
												_A									=       len(__A      ) - 1

												while low <= high:
																	_A									=       (low + high) // 2
																	if array[mid] == index:
																						# If the item already exists in the array,
																						# insert it after the existing item
																						array.insert(mid + 1						,   __A      )
																						return array
																	elif array[mid] < index:
																						_A									=       mid + 1
																	else:
																						_A									=       mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
												array.insert(__A						,   __A      )
												return array

							def 					__A       (     self:      Union[str, Any]						,   __A:      int						,   __A:      int      )			-> None:
												# Remove previous index
												if index in self.indexmap:
																	_A									=       self.indexmap[index]
																	if len(self.numbermap[n]      ) == 1:
																						del self.numbermap[n]
																	else:
																						_A									=       self.binary_search_delete(self.numbermap[n]						,   __A      )

        # Set new index
												_A									=       number

												# Number not seen before or empty so insert number value
												if number not in self.numbermap:
																	_A									=       [index]

												# Here we need to perform a binary search insertion in order to insert
												# The item in the correct place
												else:
																	_A									=       self.binary_search_insert(
																	    self.numbermap[number]						,   __A      )



							def 					__A       (     self:      List[Any]						,   __A:      int      )			-> int:
												return self.numbermap.get(__A						,   [-1]      )[0]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



