
"""simple docstring"""





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

from accelerate.utils.dataclasses import DistributedType


class A_	:




  '''simple docstring'''







  def __init__(		self				,   lowercase_=2				,   lowercase_=3				,   lowercase_=64				,   lowercase_=None  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    np.random.default_rng(_UpperCamelCase  )
     UpperCAmelCase_					:					Dict			     =    length
     UpperCAmelCase_					:					Tuple			     =    rng.normal(size=(length,)  ).astype(np.floataa  )
     UpperCAmelCase_					:					str			     =    a * self.x + b + rng.normal(scale=0.1				,   size=(length,)  ).astype(np.floataa  )







  def __len__(		self  ):

     """simple docstring"""
     return self.length







  def __getitem__(		self				,   lowercase_  ):

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




class A_	(torch.nn.Module      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_=0				,   lowercase_=0				,   lowercase_=False  ):

     """simple docstring"""
     super().__init__()
     UpperCAmelCase_					:					Dict			     =    torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     UpperCAmelCase_					:					Optional[Any]			     =    torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     UpperCAmelCase_					:					List[str]			     =    True







  def        UpperCamelCase__					(		self				,   lowercase_=None  ):

     """simple docstring"""
     if self.first_batch:
        print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
        UpperCAmelCase_					:					List[Any]			     =    False
     return x * self.a[0] + self.b[0]




class A_	(torch.nn.Module      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_=0				,   lowercase_=0				,   lowercase_=False  ):

     """simple docstring"""
     super().__init__()
     UpperCAmelCase_					:					str			     =    torch.nn.Parameter(torch.tensor(_UpperCamelCase  ).float()  )
     UpperCAmelCase_					:					Optional[Any]			     =    torch.nn.Parameter(torch.tensor(_UpperCamelCase  ).float()  )
     UpperCAmelCase_					:					Dict			     =    True







  def        UpperCamelCase__					(		self				,   lowercase_=None  ):

     """simple docstring"""
     if self.first_batch:
        print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
        UpperCAmelCase_					:					str			     =    False
     return x * self.a + self.b







def 	__a     (		__lowerCamelCase,		__lowerCamelCase = 16  ):
   from datasets import load_dataset
   from transformers import AutoTokenizer

   UpperCAmelCase_					:					Optional[int]			     =    AutoTokenizer.from_pretrained("bert-base-cased"  )
   UpperCAmelCase_					:					Dict			     =    {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
   UpperCAmelCase_					:					List[Any]			     =    load_dataset("csv",		data_files=__lowerCAmelCase  )
   UpperCAmelCase_					:					str			     =    datasets["train"].unique("label"  )

   UpperCAmelCase_					:					Optional[Any]			     =    {v: i for i, v in enumerate(__lowerCAmelCase  )}

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

   def collate_fn(__lowerCamelCase  ):
      # 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(__lowerCAmelCase,		padding="max_length",		max_length=128,		return_tensors="pt"  )
      return tokenizer.pad(__lowerCAmelCase,		padding="longest",		return_tensors="pt"  )

   # Instantiate dataloaders.
   UpperCAmelCase_					:					Optional[Any]			     =    DataLoader(tokenized_datasets["train"],		shuffle=__lowerCAmelCase,		collate_fn=__lowerCAmelCase,		batch_size=2  )
   UpperCAmelCase_					:					Dict			     =    DataLoader(tokenized_datasets["validation"],		shuffle=__lowerCAmelCase,		collate_fn=__lowerCAmelCase,		batch_size=1  )

   return train_dataloader, eval_dataloader




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A   =     {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["XGLMTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["XGLMTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XGLMForCausalLM",
			    "XGLMModel",
			    "XGLMPreTrainedModel",
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "FlaxXGLMForCausalLM",
			    "FlaxXGLMModel",
			    "FlaxXGLMPreTrainedModel",
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFXGLMForCausalLM",
			    "TFXGLMModel",
			    "TFXGLMPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure)



