
'''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


UpperCAmelCase__				:	Dict					=						logging.get_logger(__name__)

UpperCAmelCase__				:	List[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       (		lowerCamelCase__      ):
	snake_case__ :List[Any]				       =       'm2m_100'
	snake_case__ :int				       =       ['past_key_values']
	snake_case__ :List[str]				       =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





	def __init__(   self     :    List[Any] ,							__magic_name__     :    Optional[int]=128112 ,							__magic_name__     :    Optional[Any]=1024 ,							__magic_name__     :    Optional[int]=12 ,							__magic_name__     :    Union[str, Any]=4096 ,							__magic_name__     :    int=16 ,							__magic_name__     :    Union[str, Any]=12 ,							__magic_name__     :    List[Any]=4096 ,							__magic_name__     :    Tuple=16 ,							__magic_name__     :    str=0.05 ,							__magic_name__     :    int=0.05 ,							__magic_name__     :    int=True ,							__magic_name__     :    str=True ,							__magic_name__     :    Union[str, Any]="relu" ,							__magic_name__     :    Optional[Any]=1024 ,							__magic_name__     :    Dict=0.1 ,							__magic_name__     :    List[str]=0.1 ,							__magic_name__     :    Tuple=0.0 ,							__magic_name__     :    str=0.02 ,							__magic_name__     :    Any=2 ,							__magic_name__     :    Optional[int]=True ,							__magic_name__     :    Tuple=1 ,							__magic_name__     :    Union[str, Any]=0 ,							__magic_name__     :    List[str]=2 ,							**__magic_name__     :    Union[str, Any] ,							):


				"""simple docstring"""

				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=lowercase_ ,							bos_token_id=lowercase_ ,							eos_token_id=lowercase_ ,							is_encoder_decoder=lowercase_ ,							decoder_start_token_id=lowercase_ ,							**lowercase_ ,							)






class 				A       (		lowerCamelCase__      ):





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""simple docstring"""

				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_(lowercase_ ,							direction="inputs"							)
				return common_inputs





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    int ,							__magic_name__     :    int = -1 ,							__magic_name__     :    Dict = -1 ,							__magic_name__     :    int = False ,							__magic_name__     :    List[str] = None ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							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
				lowerCAmelCase__									=							tokenizer.num_special_tokens_to_add(lowercase_							)
				lowerCAmelCase__									=							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
				lowerCAmelCase__									=							[" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
				lowerCAmelCase__									=							dict(tokenizer(lowercase_ ,							return_tensors=lowercase_							)							)
				return common_inputs





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int] ,							__magic_name__     :    Optional[Any] ,							__magic_name__     :    Tuple = -1 ,							__magic_name__     :    str = -1 ,							__magic_name__     :    List[str] = False ,							__magic_name__     :    Dict = None ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_							)

				# Generate decoder inputs
				lowerCAmelCase__									=							seq_length if not self.use_past else 1
				lowerCAmelCase__									=							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_							)
				lowerCAmelCase__									=							{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
				lowerCAmelCase__									=							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
							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(lowercase_ ,							lowercase_							)] ,							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(lowercase_ ,							lowercase_							)
							lowerCAmelCase__									=							max(lowercase_ ,							lowercase_							) - min_num_layers
							lowerCAmelCase__									=							"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.
							lowerCAmelCase__									=							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







	snake_case__ :List[Any]				       =       _generate_dummy_inputs_for_default_and_seqaseq_lm








'''simple docstring'''

import math







def 					A       ( UpperCamelCase_   :       int )   ->    list:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive integers have prime factors" )
			lowerCAmelCase__									=							[]
			while n % 2 == 0:
						pf.append(2 )
						lowerCAmelCase__									=							int(n / 2 )
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						while n % i == 0:
									pf.append(UpperCamelCase_ )
									lowerCAmelCase__									=							int(n / i )
			if n > 2:
						pf.append(UpperCamelCase_ )
			return pf







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			div *= temp
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						div *= temp
			if n > 1:
						div *= 2
			return div







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			if n <= 0:
						raise ValueError("Only positive numbers are accepted" )
			lowerCAmelCase__									=							1
			lowerCAmelCase__									=							1
			while n % 2 == 0:
						temp += 1
						lowerCAmelCase__									=							int(n / 2 )
			if temp > 1:
						s *= (2**temp - 1) / (2 - 1)
			for i in range(3  ,		int(math.sqrt(UpperCamelCase_ ) ) + 1  ,		2 ):
						lowerCAmelCase__									=							1
						while n % i == 0:
									temp += 1
									lowerCAmelCase__									=							int(n / i )
						if temp > 1:
									s *= (i**temp - 1) / (i - 1)
			return int(UpperCamelCase_ )







def 					A       ( UpperCamelCase_   :       int )   ->    int:







			'''simple docstring'''



			lowerCAmelCase__									=							n
			for x in set(prime_factors(UpperCamelCase_ ) ):
						s *= (x - 1) / x
			return int(UpperCamelCase_ )


if __name__ == "__main__":
		import doctest

		doctest.testmod()





