




"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


a_				:    Tuple										=     logging.get_logger(__name__)

a_				:    Any										=     {
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						str     		= 'marian'
					__a						:						str     		= ['past_key_values']
					__a						:						str     		= {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
					def __init__(    self						,	__a=58101						,	__a=None						,	__a=1024						,	__a=12						,	__a=4096						,	__a=16						,	__a=12						,	__a=4096						,	__a=16						,	__a=0.0						,	__a=0.0						,	__a=True						,	__a=True						,	__a="gelu"						,	__a=1024						,	__a=0.1						,	__a=0.0						,	__a=0.0						,	__a=0.02						,	__a=58100						,	__a=False						,	__a=58100						,	__a=0						,	__a=0						,	__a=True						,	**__a						,	):
								__lowerCamelCase					:	Tuple	       =				vocab_size
								__lowerCamelCase					:	Optional[int]	       =				decoder_vocab_size or vocab_size
								__lowerCamelCase					:	List[str]	       =				max_position_embeddings
								__lowerCamelCase					:	List[str]	       =				d_model
								__lowerCamelCase					:	Union[str, Any]	       =				encoder_ffn_dim
								__lowerCamelCase					:	Optional[Any]	       =				encoder_layers
								__lowerCamelCase					:	List[Any]	       =				encoder_attention_heads
								__lowerCamelCase					:	Dict	       =				decoder_ffn_dim
								__lowerCamelCase					:	Optional[int]	       =				decoder_layers
								__lowerCamelCase					:	Tuple	       =				decoder_attention_heads
								__lowerCamelCase					:	int	       =				dropout
								__lowerCamelCase					:	Tuple	       =				attention_dropout
								__lowerCamelCase					:	str	       =				activation_dropout
								__lowerCamelCase					:	List[Any]	       =				activation_function
								__lowerCamelCase					:	Union[str, Any]	       =				init_std
								__lowerCamelCase					:	List[Any]	       =				encoder_layerdrop
								__lowerCamelCase					:	List[str]	       =				decoder_layerdrop
								__lowerCamelCase					:	Optional[int]	       =				use_cache
								__lowerCamelCase					:	Optional[Any]	       =				encoder_layers
								__lowerCamelCase					:	Any	       =				scale_embedding  # scale factor will be sqrt(d_model) if True
								__lowerCamelCase					:	Optional[Any]	       =				share_encoder_decoder_embeddings
								super().__init__(
								    pad_token_id=__a						,	eos_token_id=__a						,	is_encoder_decoder=__a						,	decoder_start_token_id=__a						,	forced_eos_token_id=__a						,	**__a						,	)



class __lowercase(      lowercase__				):




					'''simple docstring'''


					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def     snake_case_				(    self     ):
								if self.task in ["default", "seq2seq-lm"]:
											__lowerCamelCase					:	Tuple	       =				OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
											    ]     )

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

											if self.use_past:
														self.fill_with_past_key_values_(__a						,	direction='inputs'     )
								elif self.task == "causal-lm":
											# TODO: figure this case out.
											__lowerCamelCase					:	Tuple	       =				OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
											    ]     )
											if self.use_past:
														__lowerCamelCase       ,      __lowerCamelCase					:	int	       =				self.num_layers
														for i in range(__a     ):
																	__lowerCamelCase					:	Dict	       =				{0: 'batch', 2: 'past_sequence + sequence'}
																	__lowerCamelCase					:	str	       =				{0: 'batch', 2: 'past_sequence + sequence'}
								else:
											__lowerCamelCase					:	List[Any]	       =				OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
											        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
											        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
											    ]     )

								return common_inputs
					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
					def     snake_case_				(    self     ):
								if self.task in ["default", "seq2seq-lm"]:
											__lowerCamelCase					:	Optional[int]	       =				super().outputs
								else:
											__lowerCamelCase					:	Any	       =				super(__a						,	self     ).outputs
											if self.use_past:
														__lowerCamelCase       ,      __lowerCamelCase					:	Tuple	       =				self.num_layers
														for i in range(__a     ):
																	__lowerCamelCase					:	Optional[int]	       =				{0: 'batch', 2: 'past_sequence + sequence'}
																	__lowerCamelCase					:	List[str]	       =				{0: 'batch', 2: 'past_sequence + sequence'}
								return common_outputs
					def     snake_case_				(    self						,	__a						,	__a = -1						,	__a = -1						,	__a = False						,	__a = None						,	):
								__lowerCamelCase					:	List[str]	       =				self._generate_dummy_inputs_for_encoder_and_decoder(
								    __a						,	__a						,	__a						,	__a						,	__a     )

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

											__lowerCamelCase					:	str	       =				torch.cat(
											    [common_inputs['decoder_attention_mask'], torch.ones(__a						,	__a     )]						,	dim=1     )

											__lowerCamelCase					:	str	       =				[]
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											__lowerCamelCase       ,      __lowerCamelCase					:	int	       =				self.num_layers
											__lowerCamelCase					:	Dict	       =				min(__a						,	__a     )
											__lowerCamelCase					:	Any	       =				max(__a						,	__a     ) - min_num_layers
											__lowerCamelCase					:	Optional[int]	       =				'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					:	List[str]	       =				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
					def     snake_case_				(    self						,	__a						,	__a = -1						,	__a = -1						,	__a = False						,	__a = None						,	):
								__lowerCamelCase					:	str	       =				self._generate_dummy_inputs_for_encoder_and_decoder(
								    __a						,	__a						,	__a						,	__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					:	Any	       =				common_inputs['input_ids'].shape
											# Not using the same length for past_key_values
											__lowerCamelCase					:	List[str]	       =				seqlen + 2
											__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				self.num_layers
											__lowerCamelCase       ,      __lowerCamelCase					:	Union[str, Any]	       =				self.num_attention_heads
											__lowerCamelCase					:	Optional[Any]	       =				(
											    batch,
											    num_encoder_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)

											__lowerCamelCase					:	Optional[Any]	       =				common_inputs['attention_mask'].dtype
											__lowerCamelCase					:	Optional[Any]	       =				torch.cat(
											    [common_inputs['attention_mask'], torch.ones(__a						,	__a						,	dtype=__a     )]						,	dim=1     )
											__lowerCamelCase					:	List[str]	       =				[
											    (torch.zeros(__a     ), torch.zeros(__a     )) for _ in range(__a     )
											]
								return common_inputs
					def     snake_case_				(    self						,	__a						,	__a = -1						,	__a = -1						,	__a = False						,	__a = 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
								__lowerCamelCase					:	int	       =				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					:	Any	       =				tokenizer.num_special_tokens_to_add(__a     )
								__lowerCamelCase					:	int	       =				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					:	Optional[int]	       =				[' '.join([tokenizer.unk_token]     ) * seq_length] * batch_size
								__lowerCamelCase					:	List[Any]	       =				dict(tokenizer(__a						,	return_tensors=__a     )     )
								return common_inputs
					def     snake_case_				(    self						,	__a						,	__a = -1						,	__a = -1						,	__a = False						,	__a = None						,	):
								if self.task in ["default", "seq2seq-lm"]:
											__lowerCamelCase					:	Tuple	       =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    __a						,	batch_size=__a						,	seq_length=__a						,	is_pair=__a						,	framework=__a     )

								else:
											__lowerCamelCase					:	Optional[int]	       =				self._generate_dummy_inputs_for_causal_lm(
											    __a						,	batch_size=__a						,	seq_length=__a						,	is_pair=__a						,	framework=__a     )

								return common_inputs
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a     ):
								if self.task in ["default", "seq2seq-lm"]:
											__lowerCamelCase					:	Optional[int]	       =				super()._flatten_past_key_values_(__a						,	__a						,	__a						,	__a     )
								else:
											__lowerCamelCase					:	Tuple	       =				super(__a						,	self     )._flatten_past_key_values_(
											    __a						,	__a						,	__a						,	__a     )





					@property
					def     snake_case_				(    self     ):
								return 1E-4









"""simple docstring"""
import random
from typing import Any





def 						UpperCAmelCase				(			A__:    list     )  ->     list[Any]:
			for _ in range(len(A__     )     ):
						__lowerCamelCase					:	List[Any]	       =				random.randint(0							,				len(A__     ) - 1     )
						__lowerCamelCase					:	Optional[Any]	       =				random.randint(0							,				len(A__     ) - 1     )
						__lowerCamelCase       ,      __lowerCamelCase					:	Any	       =				data[b], data[a]
			return data


if __name__ == "__main__":
					a_				:    Any										=     [0, 1, 2, 3, 4, 5, 6, 7]
					a_				:    int										=     ['''python''', '''says''', '''hello''', '''!''']
					print('''Fisher-Yates Shuffle:''')
					print('''List''', integers, strings)
					print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))



