





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							=     logging.get_logger(__name__)

_lowerCamelCase							=     {
    '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 					UpperCamelCase_    (       UpperCamelCase__      ):
							lowerCamelCase_          =   "m2m_100"
							lowerCamelCase_          =   ["past_key_values"]
							lowerCamelCase_          =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


							def __init__(							self      :List[str]							,			__A      :Dict=12_8112							,			__A      :List[Any]=1024							,			__A      :Dict=12							,			__A      :str=4096							,			__A      :List[str]=16							,			__A      :str=12							,			__A      :int=4096							,			__A      :List[str]=16							,			__A      :Union[str, Any]=0.0_5							,			__A      :Optional[int]=0.0_5							,			__A      :List[str]=True							,			__A      :str=True							,			__A      :str="relu"							,			__A      :List[str]=1024							,			__A      :Optional[int]=0.1							,			__A      :Optional[Any]=0.1							,			__A      :Dict=0.0							,			__A      :Optional[Any]=0.0_2							,			__A      :int=2							,			__A      :Union[str, Any]=True							,			__A      :Any=1							,			__A      :Any=0							,			__A      :int=2							,			**__A      :Optional[Any]							,			)				->				List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= vocab_size
									SCREAMING_SNAKE_CASE__								= max_position_embeddings
									SCREAMING_SNAKE_CASE__								= d_model
									SCREAMING_SNAKE_CASE__								= encoder_ffn_dim
									SCREAMING_SNAKE_CASE__								= encoder_layers
									SCREAMING_SNAKE_CASE__								= encoder_attention_heads
									SCREAMING_SNAKE_CASE__								= decoder_ffn_dim
									SCREAMING_SNAKE_CASE__								= decoder_layers
									SCREAMING_SNAKE_CASE__								= decoder_attention_heads
									SCREAMING_SNAKE_CASE__								= dropout
									SCREAMING_SNAKE_CASE__								= attention_dropout
									SCREAMING_SNAKE_CASE__								= activation_dropout
									SCREAMING_SNAKE_CASE__								= activation_function
									SCREAMING_SNAKE_CASE__								= init_std
									SCREAMING_SNAKE_CASE__								= encoder_layerdrop
									SCREAMING_SNAKE_CASE__								= decoder_layerdrop
									SCREAMING_SNAKE_CASE__								= use_cache
									SCREAMING_SNAKE_CASE__								= encoder_layers
									SCREAMING_SNAKE_CASE__								= 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 					UpperCamelCase_    (       UpperCamelCase__      ):


							@property
							def        _snake_case					(							self      :Optional[Any]						)				->				Mapping[str, Mapping[int, str]]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									    ]						)

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

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


							def        _snake_case					(							self      :Dict							,			__A      :PreTrainedTokenizer							,			__A      :int = -1							,			__A      :int = -1							,			__A      :bool = False							,			__A      :Optional[TensorType] = None							,			)				->				Mapping[str, Any]:



									"""simple docstring"""



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


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __A							,			__A							,			__A							,			__A							,			__A						)

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

											SCREAMING_SNAKE_CASE__								= torch.cat(
											    [common_inputs["""decoder_attention_mask"""], torch.ones(__A							,			__A						)]							,			dim=1						)

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


							lowerCamelCase_          =   _generate_dummy_inputs_for_default_and_seqaseq_lm






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase							=     500000

_lowerCamelCase			,    _lowerCamelCase							=     os.path.split(__file__)
_lowerCamelCase							=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.select(range(0   ,				len(UpperCamelCase__      )   ,				2      )      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.sort("""numbers"""      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.shuffle()






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.train_test_split(0.1      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset   ,				UpperCamelCase__:							Tuple=10      ):
		for shard_id in range(UpperCamelCase__      ):
				SCREAMING_SNAKE_CASE__								= dataset.shard(UpperCamelCase__   ,				UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    ):
		SCREAMING_SNAKE_CASE__								= {"""num examples""": SPEED_TEST_N_EXAMPLES}
		SCREAMING_SNAKE_CASE__								= (select, sort, shuffle, train_test_split, shard)
		with tempfile.TemporaryDirectory() as tmp_dir:
				print("""generating dataset"""      )
				SCREAMING_SNAKE_CASE__								= datasets.Features({"""text""": datasets.Value("""string"""      ), """numbers""": datasets.Value("""float32"""      )}      )
				SCREAMING_SNAKE_CASE__								= generate_example_dataset(
				    os.path.join(UpperCamelCase__   ,				"""dataset.arrow"""      )   ,				UpperCamelCase__   ,				num_examples=UpperCamelCase__      )
				print("""Functions"""      )
				for func in functions:
						print(func.__name__      )
						SCREAMING_SNAKE_CASE__								= func(UpperCamelCase__      )

		with open(UpperCamelCase__   ,				"""wb"""      ) as f:
				f.write(json.dumps(UpperCamelCase__      ).encode("""utf-8"""      )      )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_indices_mapping()