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


__UpperCamelCase		:       Optional[Any]									=     logging.get_logger(__name__)

__UpperCamelCase		:       str									=     {
    '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__						(   UpperCamelCase_):
						UpperCamelCase_     =		"""marian"""
						UpperCamelCase_     =		["""past_key_values"""]
						UpperCamelCase_     =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


						def __init__(       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Union[str, Any]=5_8101    ,		UpperCamelCase__	:				Any=None    ,		UpperCamelCase__	:				List[Any]=1024    ,		UpperCamelCase__	:				Optional[Any]=12    ,		UpperCamelCase__	:				Any=4096    ,		UpperCamelCase__	:				int=16    ,		UpperCamelCase__	:				Optional[Any]=12    ,		UpperCamelCase__	:				Dict=4096    ,		UpperCamelCase__	:				Dict=16    ,		UpperCamelCase__	:				Optional[Any]=0.0    ,		UpperCamelCase__	:				str=0.0    ,		UpperCamelCase__	:				Any=True    ,		UpperCamelCase__	:				List[str]=True    ,		UpperCamelCase__	:				Dict="gelu"    ,		UpperCamelCase__	:				Union[str, Any]=1024    ,		UpperCamelCase__	:				Optional[Any]=0.1    ,		UpperCamelCase__	:				Optional[int]=0.0    ,		UpperCamelCase__	:				List[Any]=0.0    ,		UpperCamelCase__	:				int=0.02    ,		UpperCamelCase__	:				List[str]=5_8100    ,		UpperCamelCase__	:				List[str]=False    ,		UpperCamelCase__	:				Union[str, Any]=5_8100    ,		UpperCamelCase__	:				Dict=0    ,		UpperCamelCase__	:				Optional[Any]=0    ,		UpperCamelCase__	:				Union[str, Any]=True    ,		**UpperCamelCase__	:				str    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							vocab_size
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							decoder_vocab_size or vocab_size
												SCREAMING_SNAKE_CASE							:    Any							=							max_position_embeddings
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							d_model
												SCREAMING_SNAKE_CASE							:    int							=							encoder_ffn_dim
												SCREAMING_SNAKE_CASE							:    str							=							encoder_layers
												SCREAMING_SNAKE_CASE							:    Tuple							=							encoder_attention_heads
												SCREAMING_SNAKE_CASE							:    Any							=							decoder_ffn_dim
												SCREAMING_SNAKE_CASE							:    str							=							decoder_layers
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							decoder_attention_heads
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							dropout
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							attention_dropout
												SCREAMING_SNAKE_CASE							:    str							=							activation_dropout
												SCREAMING_SNAKE_CASE							:    List[str]							=							activation_function
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							init_std
												SCREAMING_SNAKE_CASE							:    Any							=							encoder_layerdrop
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							decoder_layerdrop
												SCREAMING_SNAKE_CASE							:    Dict							=							use_cache
												SCREAMING_SNAKE_CASE							:    Tuple							=							encoder_layers
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							scale_embedding  # scale factor will be sqrt(d_model) if True
												SCREAMING_SNAKE_CASE							:    Any							=							share_encoder_decoder_embeddings
												super().__init__(
												    pad_token_id=UpperCamelCase__    ,		eos_token_id=UpperCamelCase__    ,		is_encoder_decoder=UpperCamelCase__    ,		decoder_start_token_id=UpperCamelCase__    ,		forced_eos_token_id=UpperCamelCase__    ,		**UpperCamelCase__    ,		)






class 					lowercase__						(   UpperCamelCase_):


						@property
						# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												if self.task in ["default", "seq2seq-lm"]:
																		SCREAMING_SNAKE_CASE							:    str							=							OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																		    ]						)

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

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





												'''simple docstring'''





												if self.task in ["default", "seq2seq-lm"]:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							super().outputs
												else:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							super(UpperCamelCase__    ,		self						).outputs
																		if self.use_past:
																								SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.num_layers
																								for i in range(UpperCamelCase__						):
																														SCREAMING_SNAKE_CASE							:    List[str]							=							{0: '''batch''', 2: '''past_sequence + sequence'''}
																														SCREAMING_SNAKE_CASE							:    Optional[int]							=							{0: '''batch''', 2: '''past_sequence + sequence'''}
												return common_outputs


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				PreTrainedTokenizer    ,		UpperCamelCase__	:				int = -1    ,		UpperCamelCase__	:				int = -1    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				Optional[TensorType] = None    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							self._generate_dummy_inputs_for_encoder_and_decoder(
												    UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)

												# Generate decoder inputs
												SCREAMING_SNAKE_CASE							:    List[Any]							=							seq_length if not self.use_past else 1
												SCREAMING_SNAKE_CASE							:    Any							=							self._generate_dummy_inputs_for_encoder_and_decoder(
												    UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							dict(**UpperCamelCase__    ,		**UpperCamelCase__						)

												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							:    Optional[int]							=							common_inputs['''input_ids'''].shape
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							common_inputs['''decoder_input_ids'''].shape[1]
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Dict							=							self.num_attention_heads
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(
																		    batch,
																		    num_encoder_attention_heads,
																		    encoder_seq_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							decoder_seq_length + 3
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(
																		    batch,
																		    num_decoder_attention_heads,
																		    decoder_past_length,
																		    self._config.hidden_size // num_decoder_attention_heads,
																		)

																		SCREAMING_SNAKE_CASE							:    Tuple							=							torch.cat(
																		    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase__    ,		UpperCamelCase__						)]    ,		dim=1						)

																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[]
																		# 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							:    List[str]							=							min(UpperCamelCase__    ,		UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    str							=							max(UpperCamelCase__    ,		UpperCamelCase__						) - min_num_layers
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

																		for _ in range(UpperCamelCase__						):
																								common_inputs["past_key_values"].append(
																								    (
																								        torch.zeros(UpperCamelCase__						),
																								        torch.zeros(UpperCamelCase__						),
																								        torch.zeros(UpperCamelCase__						),
																								        torch.zeros(UpperCamelCase__						),
																								    )						)
																		# TODO: test this.
																		SCREAMING_SNAKE_CASE							:    Dict							=							encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
																		for _ in range(UpperCamelCase__    ,		UpperCamelCase__						):
																								common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase__						), torch.zeros(UpperCamelCase__						))						)
												return common_inputs


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							self._generate_dummy_inputs_for_encoder_and_decoder(
												    UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)

												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							:    Union[str, Any]							=							common_inputs['''input_ids'''].shape
																		# Not using the same length for past_key_values
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							seqlen + 2
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    str							=							self.num_layers
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[Any]							=							self.num_attention_heads
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(
																		    batch,
																		    num_encoder_attention_heads,
																		    past_key_values_length,
																		    self._config.hidden_size // num_encoder_attention_heads,
																		)

																		SCREAMING_SNAKE_CASE							:    Any							=							common_inputs['''attention_mask'''].dtype
																		SCREAMING_SNAKE_CASE							:    Dict							=							torch.cat(
																		    [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase__    ,		UpperCamelCase__    ,		dtype=UpperCamelCase__						)]    ,		dim=1						)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[
																		    (torch.zeros(UpperCamelCase__						), torch.zeros(UpperCamelCase__						)) for _ in range(UpperCamelCase__						)
																		]
												return common_inputs


						def        __A       (       self	:				Dict    ,		UpperCamelCase__	:				PreTrainedTokenizer    ,		UpperCamelCase__	:				int = -1    ,		UpperCamelCase__	:				int = -1    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				Optional[TensorType] = None    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							compute_effective_axis_dimension(
												    UpperCamelCase__    ,		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							:    Dict							=							tokenizer.num_special_tokens_to_add(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Dict							=							compute_effective_axis_dimension(
												    UpperCamelCase__    ,		fixed_dimension=OnnxConfig.default_fixed_sequence    ,		num_token_to_add=UpperCamelCase__						)

												# Generate dummy inputs according to compute batch and sequence
												SCREAMING_SNAKE_CASE							:    int							=							[''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							dict(tokenizer(UpperCamelCase__    ,		return_tensors=UpperCamelCase__						)						)
												return common_inputs


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				PreTrainedTokenizer    ,		UpperCamelCase__	:				int = -1    ,		UpperCamelCase__	:				int = -1    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				Optional[TensorType] = None    ,		):





												'''simple docstring'''





												if self.task in ["default", "seq2seq-lm"]:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																		    UpperCamelCase__    ,		batch_size=UpperCamelCase__    ,		seq_length=UpperCamelCase__    ,		is_pair=UpperCamelCase__    ,		framework=UpperCamelCase__						)

												else:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self._generate_dummy_inputs_for_causal_lm(
																		    UpperCamelCase__    ,		batch_size=UpperCamelCase__    ,		seq_length=UpperCamelCase__    ,		is_pair=UpperCamelCase__    ,		framework=UpperCamelCase__						)

												return common_inputs


						def        __A       (       self	:				Tuple    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				List[str]    ,		UpperCamelCase__	:				Optional[int]						):





												'''simple docstring'''





												if self.task in ["default", "seq2seq-lm"]:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							super()._flatten_past_key_values_(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)
												else:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							super(UpperCamelCase__    ,		self						)._flatten_past_key_values_(
																		    UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)



						@property
						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												return 1E-4






import math
def       A	(    _lowercase						):
						return math.sqrt(_lowercase						) * math.sqrt(_lowercase						) == num
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    Dict							=							0
						SCREAMING_SNAKE_CASE							:    Tuple							=							n
						while left <= right:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(left + right) // 2
												if mid**2 == n:
																		return True
												elif mid**2 > n:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							mid - 1
												else:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							mid + 1
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()





