

'''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


lowerCAmelCase							:						List[str]     			=		logging.get_logger(__name__)

lowerCAmelCase							:						int     			=		{
    """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 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							int    							=  '''m2m_100'''
			_UpperCAmelCase     :							int    							=  ['''past_key_values''']
			_UpperCAmelCase     :							List[str]    							=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



			def __init__(				self						:  Tuple     ,		lowerCAmelCase__						:  int=12_8112     ,		lowerCAmelCase__						:  List[str]=1024     ,		lowerCAmelCase__						:  Union[str, Any]=12     ,		lowerCAmelCase__						:  Optional[Any]=4096     ,		lowerCAmelCase__						:  Union[str, Any]=16     ,		lowerCAmelCase__						:  Union[str, Any]=12     ,		lowerCAmelCase__						:  Union[str, Any]=4096     ,		lowerCAmelCase__						:  List[Any]=16     ,		lowerCAmelCase__						:  List[Any]=0.05     ,		lowerCAmelCase__						:  Tuple=0.05     ,		lowerCAmelCase__						:  List[Any]=True     ,		lowerCAmelCase__						:  List[str]=True     ,		lowerCAmelCase__						:  int="relu"     ,		lowerCAmelCase__						:  Optional[int]=1024     ,		lowerCAmelCase__						:  Dict=0.1     ,		lowerCAmelCase__						:  Tuple=0.1     ,		lowerCAmelCase__						:  Optional[Any]=0.0     ,		lowerCAmelCase__						:  Tuple=0.02     ,		lowerCAmelCase__						:  List[Any]=2     ,		lowerCAmelCase__						:  int=True     ,		lowerCAmelCase__						:  List[str]=1     ,		lowerCAmelCase__						:  Dict=0     ,		lowerCAmelCase__						:  int=2     ,		**lowerCAmelCase__						:  Union[str, Any]     ,		):
									SCREAMING_SNAKE_CASE_:				List[str]							= vocab_size
									SCREAMING_SNAKE_CASE_:				str							= max_position_embeddings
									SCREAMING_SNAKE_CASE_:				str							= d_model
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= encoder_ffn_dim
									SCREAMING_SNAKE_CASE_:				Optional[int]							= encoder_layers
									SCREAMING_SNAKE_CASE_:				Any							= encoder_attention_heads
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= decoder_ffn_dim
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= decoder_layers
									SCREAMING_SNAKE_CASE_:				Dict							= decoder_attention_heads
									SCREAMING_SNAKE_CASE_:				List[Any]							= dropout
									SCREAMING_SNAKE_CASE_:				List[Any]							= attention_dropout
									SCREAMING_SNAKE_CASE_:				List[str]							= activation_dropout
									SCREAMING_SNAKE_CASE_:				List[Any]							= activation_function
									SCREAMING_SNAKE_CASE_:				Optional[int]							= init_std
									SCREAMING_SNAKE_CASE_:				Dict							= encoder_layerdrop
									SCREAMING_SNAKE_CASE_:				Tuple							= decoder_layerdrop
									SCREAMING_SNAKE_CASE_:				Optional[int]							= use_cache
									SCREAMING_SNAKE_CASE_:				int							= encoder_layers
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= scale_embedding  # scale factor will be sqrt(d_model) if True

									super().__init__(
									    pad_token_id=lowerCAmelCase__     ,		bos_token_id=lowerCAmelCase__     ,		eos_token_id=lowerCAmelCase__     ,		is_encoder_decoder=lowerCAmelCase__     ,		decoder_start_token_id=lowerCAmelCase__     ,		**lowerCAmelCase__     ,		)







class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									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_:				Dict							= {0: "batch"}
															SCREAMING_SNAKE_CASE_:				Optional[int]							= {0: "batch", 1: "past_decoder_sequence + sequence"}
									else:
															SCREAMING_SNAKE_CASE_:				Optional[int]							= {0: "batch", 1: "decoder_sequence"}
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= {0: "batch", 1: "decoder_sequence"}

									if self.use_past:
															self.fill_with_past_key_values_(lowerCAmelCase__     ,		direction="inputs")
									return common_inputs



			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  PreTrainedTokenizer     ,		lowerCAmelCase__						:  int = -1     ,		lowerCAmelCase__						:  int = -1     ,		lowerCAmelCase__						:  bool = False     ,		lowerCAmelCase__						:  Optional[TensorType] = 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
									SCREAMING_SNAKE_CASE_:				Any							= compute_effective_axis_dimension(
									    lowerCAmelCase__     ,		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(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Tuple							= compute_effective_axis_dimension(
									    lowerCAmelCase__     ,		fixed_dimension=OnnxConfig.default_fixed_sequence     ,		num_token_to_add=lowerCAmelCase__)

									# Generate dummy inputs according to compute batch and sequence
									SCREAMING_SNAKE_CASE_:				Dict							= [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
									SCREAMING_SNAKE_CASE_:				Tuple							= dict(tokenizer(lowerCAmelCase__     ,		return_tensors=lowerCAmelCase__))
									return common_inputs



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]     ,		lowerCAmelCase__						:  PreTrainedTokenizer     ,		lowerCAmelCase__						:  int = -1     ,		lowerCAmelCase__						:  int = -1     ,		lowerCAmelCase__						:  bool = False     ,		lowerCAmelCase__						:  Optional[TensorType] = None     ,		):
									SCREAMING_SNAKE_CASE_:				Tuple							= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__)

									# Generate decoder inputs
									SCREAMING_SNAKE_CASE_:				Optional[int]							= seq_length if not self.use_past else 1
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
									SCREAMING_SNAKE_CASE_:				int							= dict(**lowerCAmelCase__     ,		**lowerCAmelCase__)

									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_:				int							= common_inputs["input_ids"].shape
															SCREAMING_SNAKE_CASE_:				int							= common_inputs["decoder_input_ids"].shape[1]
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.num_attention_heads
															SCREAMING_SNAKE_CASE_:				int							= (
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															SCREAMING_SNAKE_CASE_:				int							= 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_:				Union[str, Any]							= torch.cat(
															    [common_inputs["decoder_attention_mask"], torch.ones(lowerCAmelCase__     ,		lowerCAmelCase__)]     ,		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_:				List[Any]							= self.num_layers
															SCREAMING_SNAKE_CASE_:				Optional[int]							= min(lowerCAmelCase__     ,		lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:				List[str]							= max(lowerCAmelCase__     ,		lowerCAmelCase__) - min_num_layers
															SCREAMING_SNAKE_CASE_:				Any							= "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

															for _ in range(lowerCAmelCase__):
																					common_inputs["past_key_values"].append(
																					    (
																					        torch.zeros(lowerCAmelCase__),
																					        torch.zeros(lowerCAmelCase__),
																					        torch.zeros(lowerCAmelCase__),
																					        torch.zeros(lowerCAmelCase__),
																					    ))
															# TODO: test this.
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= encoder_shape if remaining_side_name == "encoder" else decoder_shape
															for _ in range(lowerCAmelCase__     ,		lowerCAmelCase__):
																					common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase__), torch.zeros(lowerCAmelCase__)))
									return common_inputs




			_UpperCAmelCase     :							str    							=  _generate_dummy_inputs_for_default_and_seqaseq_lm









import io
import json
import subprocess


lowerCAmelCase							:						Dict     			=		[
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

lowerCAmelCase							:						List[str]     			=		8







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Dict							= {}
						for src, tgt in pairs:
												SCREAMING_SNAKE_CASE_:				Tuple							= f"{src}-{tgt}"
												SCREAMING_SNAKE_CASE_:				List[str]							= f"sacrebleu -t wmt19 -l {pair} --echo src".split()
												SCREAMING_SNAKE_CASE_:				int							= subprocess.run(_UpperCAmelCase       ,			stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
												SCREAMING_SNAKE_CASE_:				Dict							= subprocess.run(_UpperCAmelCase       ,			stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
												SCREAMING_SNAKE_CASE_:				Tuple							= {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
						return text


lowerCAmelCase							:						Tuple     			=		get_all_data(pairs, n_objs)
lowerCAmelCase							:						Optional[Any]     			=		"""./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
						lowerCAmelCase							:						List[str]     			=		json.dump(text, f, indent=2, ensure_ascii=False)






