





'''simple docstring'''



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

from accelerate.utils.dataclasses import DistributedType




class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase=2						,   lowerCamelCase=3						,   lowerCamelCase=64						,   lowerCamelCase=None	):
       _snake_case     		=				np.random.default_rng(lowerCamelCase	)
       _snake_case     		=				length
       _snake_case     		=				rng.normal(size=(length,)	).astype(np.floataa	)
       _snake_case     		=				a * self.x + b + rng.normal(scale=0.1						,   size=(length,)	).astype(np.floataa	)

     def __len__(					self	):
       return self.length






     def __getitem__(					self						,   lowerCamelCase	):
       return {"x": self.x[i], "y": self.y[i]}







class       __SCREAMING_SNAKE_CASE	(						torch.nn.Module			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase=0						,   lowerCamelCase=0						,   lowerCamelCase=False	):
       super().__init__()
       _snake_case     		=				torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
       _snake_case     		=				torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
       _snake_case     		=				True






     def 	UpperCamelCase(					self						,   lowerCamelCase=None	):







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







class       __SCREAMING_SNAKE_CASE	(						torch.nn.Module			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase=0						,   lowerCamelCase=0						,   lowerCamelCase=False	):
       super().__init__()
       _snake_case     		=				torch.nn.Parameter(torch.tensor(lowerCamelCase	).float()	)
       _snake_case     		=				torch.nn.Parameter(torch.tensor(lowerCamelCase	).float()	)
       _snake_case     		=				True






     def 	UpperCamelCase(					self						,   lowerCamelCase=None	):







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







def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = 16       ):







  '''simple docstring'''



  from datasets import load_dataset
  from transformers import AutoTokenizer

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

  _snake_case     		=				{v: i for i, v in enumerate(SCREAMING_SNAKE_CASE_       )}

  def tokenize_function(SCREAMING_SNAKE_CASE__       ):
    # max_length=None => use the model max length (it's actually the default)
    _snake_case     		=				tokenizer(
        examples["sentence1"]					,	examples["sentence2"]					,	truncation=SCREAMING_SNAKE_CASE_					,	max_length=SCREAMING_SNAKE_CASE_					,	padding="max_length"       )
    if "label" in examples:
      _snake_case     		=				[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
  _snake_case     		=				datasets.map(
      SCREAMING_SNAKE_CASE_					,	batched=SCREAMING_SNAKE_CASE_					,	remove_columns=["sentence1", "sentence2", "label"]					,	)

  def collate_fn(SCREAMING_SNAKE_CASE__       ):
    # 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(SCREAMING_SNAKE_CASE_					,	padding="max_length"					,	max_length=1_28					,	return_tensors="pt"       )
    return tokenizer.pad(SCREAMING_SNAKE_CASE_					,	padding="longest"					,	return_tensors="pt"       )

  # Instantiate dataloaders.
  _snake_case     		=				DataLoader(tokenized_datasets["train"]					,	shuffle=SCREAMING_SNAKE_CASE_					,	collate_fn=SCREAMING_SNAKE_CASE_					,	batch_size=2       )
  _snake_case     		=				DataLoader(tokenized_datasets["validation"]					,	shuffle=SCREAMING_SNAKE_CASE_					,	collate_fn=SCREAMING_SNAKE_CASE_					,	batch_size=1       )

  return train_dataloader, eval_dataloader









'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__   :						int    =			logging.get_logger(__name__)

__magic_name__   :						Tuple    =			{
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Union[str, Any]      =   '''xlm'''
     UpperCAmelCase__       :						Dict      =   {
         '''hidden_size''': '''emb_dim''',
         '''num_attention_heads''': '''n_heads''',
         '''num_hidden_layers''': '''n_layers''',
         '''n_words''': '''vocab_size''',  # For backward compatibility
     }

     def __init__(					self						,   lowerCamelCase=30_145						,   lowerCamelCase=2_048						,   lowerCamelCase=12						,   lowerCamelCase=16						,   lowerCamelCase=0.1						,   lowerCamelCase=0.1						,   lowerCamelCase=True						,   lowerCamelCase=False						,   lowerCamelCase=False						,   lowerCamelCase=False						,   lowerCamelCase=1						,   lowerCamelCase=True						,   lowerCamelCase=512						,   lowerCamelCase=2_048**-0.5						,   lowerCamelCase=1e-12						,   lowerCamelCase=0.02						,   lowerCamelCase=0						,   lowerCamelCase=1						,   lowerCamelCase=2						,   lowerCamelCase=3						,   lowerCamelCase=5						,   lowerCamelCase=True						,   lowerCamelCase="first"						,   lowerCamelCase=True						,   lowerCamelCase=None						,   lowerCamelCase=True						,   lowerCamelCase=0.1						,   lowerCamelCase=5						,   lowerCamelCase=5						,   lowerCamelCase=0						,   lowerCamelCase=0						,   lowerCamelCase=2						,   lowerCamelCase=0						,   **lowerCamelCase						,   ):
       _snake_case     		=				vocab_size
       _snake_case     		=				emb_dim
       _snake_case     		=				n_layers
       _snake_case     		=				n_heads
       _snake_case     		=				dropout
       _snake_case     		=				attention_dropout
       _snake_case     		=				gelu_activation
       _snake_case     		=				sinusoidal_embeddings
       _snake_case     		=				causal
       _snake_case     		=				asm
       _snake_case     		=				n_langs
       _snake_case     		=				use_lang_emb
       _snake_case     		=				layer_norm_eps
       _snake_case     		=				bos_index
       _snake_case     		=				eos_index
       _snake_case     		=				pad_index
       _snake_case     		=				unk_index
       _snake_case     		=				mask_index
       _snake_case     		=				is_encoder
       _snake_case     		=				max_position_embeddings
       _snake_case     		=				embed_init_std
       _snake_case     		=				init_std
       _snake_case     		=				summary_type
       _snake_case     		=				summary_use_proj
       _snake_case     		=				summary_activation
       _snake_case     		=				summary_proj_to_labels
       _snake_case     		=				summary_first_dropout
       _snake_case     		=				start_n_top
       _snake_case     		=				end_n_top
       _snake_case     		=				mask_token_id
       _snake_case     		=				lang_id

       if "n_words" in kwargs:
         _snake_case     		=				kwargs["n_words"]

       super().__init__(pad_token_id=lowerCamelCase						,   bos_token_id=lowerCamelCase						,   **lowerCamelCase	)







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     @property
     def 	UpperCamelCase(					self	):
       if self.task == "multiple-choice":
         _snake_case     		=				{0: "batch", 1: "choice", 2: "sequence"}
       else:
         _snake_case     		=				{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ]	)


