




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	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=2  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=3  ,      SCREAMING_SNAKE_CASE__	:	Tuple=64  ,      SCREAMING_SNAKE_CASE__	:	Tuple=None  ) ->			int:
         lowerCAmelCase__            =					np.random.default_rng(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					length
         lowerCAmelCase__            =					rng.normal(size=(length,)  ).astype(np.floataa  )
         lowerCAmelCase__            =					a * self.x + b + rng.normal(scale=0.1  ,      size=(length,)  ).astype(np.floataa  )


    def __len__(			self	:	Dict  ) ->			List[str]:
         return self.length


    def __getitem__(			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	Any  ) ->			Optional[int]:
         return {"x": self.x[i], "y": self.y[i]}






class 					__lowerCamelCase			(       torch.nn.Module       ):




    """simple docstring"""

    def __init__(			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=0  ,      SCREAMING_SNAKE_CASE__	:	Tuple=0  ,      SCREAMING_SNAKE_CASE__	:	str=False  ) ->			Tuple:
         super().__init__()
         lowerCAmelCase__            =					torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
         lowerCAmelCase__            =					torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
         lowerCAmelCase__            =					True


    def   a (			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=None  ) ->			Tuple:
         if self.first_batch:
              print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
              lowerCAmelCase__            =					False
         return x * self.a[0] + self.b[0]






class 					__lowerCamelCase			(       torch.nn.Module       ):




    """simple docstring"""

    def __init__(			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Any=0  ,      SCREAMING_SNAKE_CASE__	:	Any=0  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=False  ) ->			Dict:
         super().__init__()
         lowerCAmelCase__            =					torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__  ).float()  )
         lowerCAmelCase__            =					torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__  ).float()  )
         lowerCAmelCase__            =					True


    def   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Any=None  ) ->			Dict:
         if self.first_batch:
              print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
              lowerCAmelCase__            =					False
         return x * self.a + self.b






def 				_A  (		lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   int = 16     ):
     """simple docstring"""

     from datasets import load_dataset
     from transformers import AutoTokenizer

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

     lowerCAmelCase__            =					{v: i for i, v in enumerate(lowerCAmelCase_     )}

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

     def collate_fn(lowerCAmelCase_	:   Dict     ):
          # 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.
     lowerCAmelCase__            =					DataLoader(tokenized_datasets["train"]  ,							shuffle=lowerCAmelCase_  ,							collate_fn=lowerCAmelCase_  ,							batch_size=2     )
     lowerCAmelCase__            =					DataLoader(tokenized_datasets["validation"]  ,							shuffle=lowerCAmelCase_  ,							collate_fn=lowerCAmelCase_  ,							batch_size=1     )

     return train_dataloader, eval_dataloader







"""simple docstring"""


import argparse
import os
import re

import packaging.version


lowerCamelCase__						= "examples/"
lowerCamelCase__						= {
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
lowerCamelCase__						= {
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
lowerCamelCase__						= "README.md"




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		Dict:




      """simple docstring"""

      with open(lowercase_		,"r"		,encoding="utf-8"		,newline="\n"      ) as f:
            _UpperCamelCase   :						Any    =							f.read()
      _UpperCamelCase,	_UpperCamelCase   :						str    =							REPLACE_PATTERNS[pattern]
      _UpperCamelCase   :						List[str]    =							replace.replace("VERSION"		,lowercase_      )
      _UpperCamelCase   :						Any    =							re_pattern.sub(lowercase_		,lowercase_      )
      with open(lowercase_		,"w"		,encoding="utf-8"		,newline="\n"      ) as f:
            f.write(lowercase_      )




def        lowercase__     (       lowercase_      )       ->		str:




      """simple docstring"""

      for folder, directories, fnames in os.walk(lowercase_      ):
            # Removing some of the folders with non-actively maintained examples from the walk
            if "research_projects" in directories:
                  directories.remove("research_projects"      )
            if "legacy" in directories:
                  directories.remove("legacy"      )
            for fname in fnames:
                  if fname.endswith(".py"      ):
                        update_version_in_file(os.path.join(lowercase_		,lowercase_      )		,lowercase_		,pattern="examples"      )




def        lowercase__     (       lowercase_		,lowercase_=False      )       ->		Any:




      """simple docstring"""

      for pattern, fname in REPLACE_FILES.items():
            update_version_in_file(lowercase_		,lowercase_		,lowercase_      )
      if not patch:
            update_version_in_examples(lowercase_      )




def        lowercase__     (       )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							"🤗 Transformers currently provides the following architectures"
      _UpperCamelCase   :						int    =							"1. Want to contribute a new model?"
      with open(lowercase_		,"r"		,encoding="utf-8"		,newline="\n"      ) as f:
            _UpperCamelCase   :						int    =							f.readlines()

      # Find the start of the list.
      _UpperCamelCase   :						Optional[Any]    =							0
      while not lines[start_index].startswith(_start_prompt      ):
            start_index += 1
      start_index += 1

      _UpperCamelCase   :						Any    =							start_index
      # Update the lines in the model list.
      while not lines[index].startswith(_end_prompt      ):
            if lines[index].startswith("1."      ):
                  _UpperCamelCase   :						Dict    =							lines[index].replace(
                      "https://huggingface.co/docs/transformers/main/model_doc"		,"https://huggingface.co/docs/transformers/model_doc"		,)
            index += 1

      with open(lowercase_		,"w"		,encoding="utf-8"		,newline="\n"      ) as f:
            f.writelines(lowercase_      )




def        lowercase__     (       )       ->		List[str]:




      """simple docstring"""

      with open(REPLACE_FILES["init"]		,"r"      ) as f:
            _UpperCamelCase   :						Dict    =							f.read()
      _UpperCamelCase   :						List[str]    =							REPLACE_PATTERNS["init"][0].search(lowercase_      ).groups()[0]
      return packaging.version.parse(lowercase_      )




def        lowercase__     (       lowercase_=False      )       ->		Any:




      """simple docstring"""

      _UpperCamelCase   :						Tuple    =							get_version()
      if patch and default_version.is_devrelease:
            raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"      )
      if default_version.is_devrelease:
            _UpperCamelCase   :						Union[str, Any]    =							default_version.base_version
      elif patch:
            _UpperCamelCase   :						Union[str, Any]    =							F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
      else:
            _UpperCamelCase   :						Any    =							F'''{default_version.major}.{default_version.minor + 1}.0'''

      # Now let's ask nicely if that's the right one.
      _UpperCamelCase   :						List[str]    =							input(F'''Which version are you releasing? [{default_version}]'''      )
      if len(lowercase_      ) == 0:
            _UpperCamelCase   :						Any    =							default_version

      print(F'''Updating version to {version}.'''      )
      global_version_update(lowercase_		,patch=lowercase_      )
      if not patch:
            print("Cleaning main README, don't forget to run `make fix-copies`."      )
            clean_main_ref_in_model_list()




def        lowercase__     (       )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							get_version()
      _UpperCamelCase   :						List[Any]    =							F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
      _UpperCamelCase   :						Tuple    =							current_version.base_version

      # Check with the user we got that right.
      _UpperCamelCase   :						Union[str, Any]    =							input(F'''Which version are we developing now? [{dev_version}]'''      )
      if len(lowercase_      ) == 0:
            _UpperCamelCase   :						Optional[int]    =							dev_version

      print(F'''Updating version to {version}.'''      )
      global_version_update(lowercase_      )
      print("Cleaning main README, don't forget to run `make fix-copies`."      )
      clean_main_ref_in_model_list()


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()
  parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
  parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
  lowerCamelCase__						= parser.parse_args()
  if not args.post_release:
    pre_release_work(patch=args.patch)
  elif args.patch:
    print("Nothing to do after a patch :-)")
  else:
    post_release_work()
