


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 : Any		=				logging.get_logger(__name__)

a : Optional[int]		=				{
    '''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 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"marian"
					__UpperCAmelCase            =				["past_key_values"]
					__UpperCAmelCase            =				{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






					def __init__(				self ,       snake_case_=5_8_1_0_1 ,       snake_case_=None ,       snake_case_=1_0_2_4 ,       snake_case_=1_2 ,       snake_case_=4_0_9_6 ,       snake_case_=1_6 ,       snake_case_=1_2 ,       snake_case_=4_0_9_6 ,       snake_case_=1_6 ,       snake_case_=0.0 ,       snake_case_=0.0 ,       snake_case_=True ,       snake_case_=True ,       snake_case_="gelu" ,       snake_case_=1_0_2_4 ,       snake_case_=0.1 ,       snake_case_=0.0 ,       snake_case_=0.0 ,       snake_case_=0.0_2 ,       snake_case_=5_8_1_0_0 ,       snake_case_=False ,       snake_case_=5_8_1_0_0 ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=True ,       **snake_case_ ,       )   ->			Optional[int]:







								'''simple docstring'''

								__lowercase 					=   vocab_size
								__lowercase 					=   decoder_vocab_size or vocab_size
								__lowercase 					=   max_position_embeddings
								__lowercase 					=   d_model
								__lowercase 					=   encoder_ffn_dim
								__lowercase 					=   encoder_layers
								__lowercase 					=   encoder_attention_heads
								__lowercase 					=   decoder_ffn_dim
								__lowercase 					=   decoder_layers
								__lowercase 					=   decoder_attention_heads
								__lowercase 					=   dropout
								__lowercase 					=   attention_dropout
								__lowercase 					=   activation_dropout
								__lowercase 					=   activation_function
								__lowercase 					=   init_std
								__lowercase 					=   encoder_layerdrop
								__lowercase 					=   decoder_layerdrop
								__lowercase 					=   use_cache
								__lowercase 					=   encoder_layers
								__lowercase 					=   scale_embedding  # scale factor will be sqrt(d_model) if True
								__lowercase 					=   share_encoder_decoder_embeddings
								super().__init__(
								    pad_token_id=snake_case_ ,       eos_token_id=snake_case_ ,       is_encoder_decoder=snake_case_ ,       decoder_start_token_id=snake_case_ ,       forced_eos_token_id=snake_case_ ,       **snake_case_ ,       )




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def A      (				self						)   ->			Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

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

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

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







								'''simple docstring'''

								if self.task in ["default", "seq2seq-lm"]:
											__lowercase 					=   super().outputs
								else:
											__lowercase 					=   super(snake_case_ ,       self						).outputs
											if self.use_past:
														__lowercase					, __lowercase 					=   self.num_layers
														for i in range(snake_case_						):
																	__lowercase 					=   {0: '''batch''', 2: '''past_sequence + sequence'''}
																	__lowercase 					=   {0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs






					def A      (				self ,       snake_case_ ,       snake_case_ = -1 ,       snake_case_ = -1 ,       snake_case_ = False ,       snake_case_ = None ,       )   ->			Mapping[str, Any]:







								'''simple docstring'''

								__lowercase 					=   self._generate_dummy_inputs_for_encoder_and_decoder(
								    snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)

								# Generate decoder inputs
								__lowercase 					=   seq_length if not self.use_past else 1
								__lowercase 					=   self._generate_dummy_inputs_for_encoder_and_decoder(
								    snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)
								__lowercase 					=   {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
								__lowercase 					=   dict(**snake_case_ ,       **snake_case_						)

								if self.use_past:
											if not is_torch_available():
														raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
											else:
														import torch
											__lowercase					, __lowercase 					=   common_inputs['''input_ids'''].shape
											__lowercase 					=   common_inputs['''decoder_input_ids'''].shape[1]
											__lowercase					, __lowercase 					=   self.num_attention_heads
											__lowercase 					=   (
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											__lowercase 					=   decoder_seq_length + 3
											__lowercase 					=   (
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											__lowercase 					=   torch.cat(
											    [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case_ ,       snake_case_						)] ,       dim=1						)

											__lowercase 					=   []
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											__lowercase					, __lowercase 					=   self.num_layers
											__lowercase 					=   min(snake_case_ ,       snake_case_						)
											__lowercase 					=   max(snake_case_ ,       snake_case_						) - min_num_layers
											__lowercase 					=   '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

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






					def A      (				self ,       snake_case_ ,       snake_case_ = -1 ,       snake_case_ = -1 ,       snake_case_ = False ,       snake_case_ = None ,       )   ->			Mapping[str, Any]:







								'''simple docstring'''

								__lowercase 					=   self._generate_dummy_inputs_for_encoder_and_decoder(
								    snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)

								if self.use_past:
											if not is_torch_available():
														raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
											else:
														import torch
											__lowercase					, __lowercase 					=   common_inputs['''input_ids'''].shape
											# Not using the same length for past_key_values
											__lowercase 					=   seqlen + 2
											__lowercase					, __lowercase 					=   self.num_layers
											__lowercase					, __lowercase 					=   self.num_attention_heads
											__lowercase 					=   (
											    batch,
											    num_encoder_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)

											__lowercase 					=   common_inputs['''attention_mask'''].dtype
											__lowercase 					=   torch.cat(
											    [common_inputs['''attention_mask'''], torch.ones(snake_case_ ,       snake_case_ ,       dtype=snake_case_						)] ,       dim=1						)
											__lowercase 					=   [
											    (torch.zeros(snake_case_						), torch.zeros(snake_case_						)) for _ in range(snake_case_						)
											]
								return common_inputs






					def A      (				self ,       snake_case_ ,       snake_case_ = -1 ,       snake_case_ = -1 ,       snake_case_ = False ,       snake_case_ = None ,       )   ->			Mapping[str, Any]:







								'''simple docstring'''

								__lowercase 					=   compute_effective_axis_dimension(
								    snake_case_ ,       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
								__lowercase 					=   tokenizer.num_special_tokens_to_add(snake_case_						)
								__lowercase 					=   compute_effective_axis_dimension(
								    snake_case_ ,       fixed_dimension=OnnxConfig.default_fixed_sequence ,       num_token_to_add=snake_case_						)

								# Generate dummy inputs according to compute batch and sequence
								__lowercase 					=   [''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
								__lowercase 					=   dict(tokenizer(snake_case_ ,       return_tensors=snake_case_						)						)
								return common_inputs






					def A      (				self ,       snake_case_ ,       snake_case_ = -1 ,       snake_case_ = -1 ,       snake_case_ = False ,       snake_case_ = None ,       )   ->			Mapping[str, Any]:







								'''simple docstring'''

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

								else:
											__lowercase 					=   self._generate_dummy_inputs_for_causal_lm(
											    snake_case_ ,       batch_size=snake_case_ ,       seq_length=snake_case_ ,       is_pair=snake_case_ ,       framework=snake_case_						)

								return common_inputs






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			Optional[Any]:







								'''simple docstring'''

								if self.task in ["default", "seq2seq-lm"]:
											__lowercase 					=   super()._flatten_past_key_values_(snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)
								else:
											__lowercase 					=   super(snake_case_ ,       self						)._flatten_past_key_values_(
											    snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)






					@property
					def A      (				self						)   ->			float:







								'''simple docstring'''

								return 1e-4






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 	lowerCamelCase_       :



					'''simple docstring'''







					def __init__(				self ,       snake_case_=2 ,       snake_case_=3 ,       snake_case_=6_4 ,       snake_case_=None						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   np.random.default_rng(snake_case_						)
								__lowercase 					=   length
								__lowercase 					=   rng.normal(size=(length,)						).astype(np.floataa						)
								__lowercase 					=   a * self.x + b + rng.normal(scale=0.1 ,       size=(length,)						).astype(np.floataa						)






					def __len__(				self						)   ->			Dict:







								'''simple docstring'''

								return self.length






					def __getitem__(				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								return {"x": self.x[i], "y": self.y[i]}




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			List[Any]:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a[0] + self.b[0]




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			List[str]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			str:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a + self.b




def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = 16  ):


			'''simple docstring'''







			from datasets import load_dataset
			from transformers import AutoTokenizer

			__lowercase 					=   AutoTokenizer.from_pretrained('''bert-base-cased'''  )
			__lowercase 					=   {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
			__lowercase 					=   load_dataset('''csv'''       ,			data_files=_UpperCamelCase  )
			__lowercase 					=   datasets['''train'''].unique('''label'''  )

			__lowercase 					=   {v: i for i, v in enumerate(_UpperCamelCase  )}

			def tokenize_function(_UpperCamelCase  ):
						# max_length=None => use the model max length (it's actually the default)
						__lowercase 					=   tokenizer(
						    examples['''sentence1''']       ,			examples['''sentence2''']       ,			truncation=_UpperCamelCase       ,			max_length=_UpperCamelCase       ,			padding='''max_length'''  )
						if "label" in examples:
									__lowercase 					=   [label_to_id[l] for l in examples['''label''']]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			__lowercase 					=   datasets.map(
			    _UpperCamelCase       ,			batched=_UpperCamelCase       ,			remove_columns=['''sentence1''', '''sentence2''', '''label''']       ,			)

			def collate_fn(_UpperCamelCase  ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_UpperCamelCase       ,			padding='''max_length'''       ,			max_length=1_28       ,			return_tensors='''pt'''  )
						return tokenizer.pad(_UpperCamelCase       ,			padding='''longest'''       ,			return_tensors='''pt'''  )

			# Instantiate dataloaders.
			__lowercase 					=   DataLoader(tokenized_datasets['''train''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=2  )
			__lowercase 					=   DataLoader(tokenized_datasets['''validation''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=1  )

			return train_dataloader, eval_dataloader


