




import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=False					)  ->       Optional[Any]:
			try:
						snake_case				:    Tuple           =	os.environ[key]
			except KeyError:
						# KEY isn't set, default to `default`.
						snake_case				:    List[str]           =	default
			else:
						# KEY is set, convert it to True or False.
						try:
									snake_case				:    Union[str, Any]           =	strtobool(lowercase					)
						except ValueError:
									# More values are supported, but let's keep the message simple.
									raise ValueError(f"""If set, {key} must be yes or no."""					)
			return _value


lowerCamelCase	:	Dict				=   parse_flag_from_env('RUN_SLOW', default=False)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
			return unittest.skip("""Test was skipped"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
			return unittest.skipUnless(_run_slow_tests						,"""test is slow"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Tuple:
			return unittest.skipUnless(not torch.cuda.is_available()						,"""test requires only a CPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Tuple:
			return unittest.skipUnless(torch.cuda.is_available()						,"""test requires a GPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
			return unittest.skipUnless(is_xpu_available()						,"""test requires a XPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
			return unittest.skipUnless(is_mps_available()						,"""test requires a `mps` backend support in `torch`"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       str:
			return unittest.skipUnless(
			    is_transformers_available() and is_datasets_available()						,"""test requires the Hugging Face suite"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
			return unittest.skipUnless(is_bnb_available()						,"""test requires the bitsandbytes library"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[Any]:
			return unittest.skipUnless(is_tpu_available()						,"""test requires TPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Dict:
			return unittest.skipUnless(torch.cuda.device_count() == 1						,"""test requires a GPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Dict:
			return unittest.skipUnless(torch.xpu.device_count() == 1						,"""test requires a XPU"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Any:
			return unittest.skipUnless(torch.cuda.device_count() > 1						,"""test requires multiple GPUs"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
			return unittest.skipUnless(torch.xpu.device_count() > 1						,"""test requires multiple XPUs"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Dict:
			return unittest.skipUnless(is_safetensors_available()						,"""test requires safetensors"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[int]:
			return unittest.skipUnless(is_deepspeed_available()						,"""test requires DeepSpeed"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[Any]:
			return unittest.skipUnless(is_torch_version(""">="""						,"""1.12.0"""					)						,"""test requires torch version >= 1.12.0"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase=None						,lowercase=None					)  ->       int:
			if test_case is None:
						return partial(lowercase						,version=lowercase					)
			return unittest.skipUnless(is_torch_version(""">="""						,lowercase					)						,f"""test requires torch version >= {version}"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       int:
			return unittest.skipUnless(is_tensorboard_available()						,"""test requires Tensorboard"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Any:
			return unittest.skipUnless(is_wandb_available()						,"""test requires wandb"""					)(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       str:
			return unittest.skipUnless(is_comet_ml_available()						,"""test requires comet_ml"""					)(lowercase					)


lowerCamelCase	:	int				=   (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[Any]:
			return unittest.skipUnless(
			    _atleast_one_tracker_available						,"""test requires at least one tracker to be available and for `comet_ml` to not be installed"""						,)(lowercase					)




class 						__lowercase		(unittest.TestCase							):




						"""simple docstring"""





						_snake_case												=						True

						@classmethod
						def      UpperCAmelCase				(				cls     )		->    Union[str, Any]:
									snake_case				:    Any           =	tempfile.mkdtemp()

						@classmethod
						def      UpperCAmelCase				(				cls     )		->    str:
									if os.path.exists(cls.tmpdir     ):
												shutil.rmtree(cls.tmpdir     )

						def      UpperCAmelCase				(				self     )		->    Union[str, Any]:
									if self.clear_on_setup:
												for path in Path(self.tmpdir     ).glob("""**/*"""     ):
															if path.is_file():
																		path.unlink()
															elif path.is_dir():
																		shutil.rmtree(A     )




class 						__lowercase		(unittest.TestCase							):




						"""simple docstring"""





						def      UpperCAmelCase				(				self     )		->    Tuple:
									super().tearDown()
									# Reset the state of the AcceleratorState singleton.
									AcceleratorState._reset_state()
									PartialState._reset_state()




class 						__lowercase		(unittest.TestCase							):




						"""simple docstring"""





						def      UpperCAmelCase				(				self    ,				A     )		->    int:
									snake_case				:    Optional[Any]           =	mocks if isinstance(A    ,				(tuple, list)     ) else [mocks]
									for m in self.mocks:
												m.start()
												self.addCleanup(m.stop     )




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
			snake_case				:    Union[str, Any]           =	AcceleratorState()
			snake_case				:    List[str]           =	tensor[None].clone().to(state.device					)
			snake_case				:    Optional[Any]           =	gather(lowercase					).cpu()
			snake_case				:    List[Any]           =	tensor[0].cpu()
			for i in range(tensors.shape[0]					):
						if not torch.equal(tensors[i]						,lowercase					):
									return False
			return True




class 						__lowercase		:




						"""simple docstring"""





						def __init__(				self    ,				A    ,				A    ,				A     )		->    Any:
									snake_case				:    Dict           =	returncode
									snake_case				:    str           =	stdout
									snake_case				:    int           =	stderr




async def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Union[str, Any]:
			while True:
						snake_case				:    int           =	await stream.readline()
						if line:
									callback(lowercase					)
						else:
									break




async def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=None						,lowercase=None						,lowercase=None						,lowercase=False						,lowercase=False					)  ->       _RunOutput:
			if echo:
						print("""\nRunning: """						,""" """.join(lowercase					)					)

			snake_case				:    Any           =	await asyncio.create_subprocess_exec(
			    cmd[0]						,*cmd[1:]						,stdin=lowercase						,stdout=asyncio.subprocess.PIPE						,stderr=asyncio.subprocess.PIPE						,env=lowercase						,)

			# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
			# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
			#
			# If it starts hanging, will need to switch to the following code. The problem is that no data
			# will be seen until it's done and if it hangs for example there will be no debug info.
			# out, err = await p.communicate()
			# return _RunOutput(p.returncode, out, err)

			snake_case				:    Optional[int]           =	[]
			snake_case				:    List[Any]           =	[]

			def tee(lowercase						,lowercase						,lowercase						,lowercase=""					):
						snake_case				:    List[Any]           =	line.decode("""utf-8"""					).rstrip()
						sink.append(lowercase					)
						if not quiet:
									print(lowercase						,lowercase						,file=lowercase					)

    # XXX: the timeout doesn't seem to make any difference here
			await asyncio.wait(
			    [
			        asyncio.create_task(_read_stream(p.stdout						,lambda lowercase					: tee(lowercase						,lowercase						,sys.stdout						,label="""stdout:"""					)					)					),
			        asyncio.create_task(_read_stream(p.stderr						,lambda lowercase					: tee(lowercase						,lowercase						,sys.stderr						,label="""stderr:"""					)					)					),
			    ]						,timeout=lowercase						,)
			return _RunOutput(await p.wait()						,lowercase						,lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=None						,lowercase=None						,lowercase=180						,lowercase=False						,lowercase=True					)  ->       _RunOutput:
			snake_case				:    Optional[Any]           =	asyncio.get_event_loop()
			snake_case				:    Tuple           =	loop.run_until_complete(
			    _stream_subprocess(lowercase						,env=lowercase						,stdin=lowercase						,timeout=lowercase						,quiet=lowercase						,echo=lowercase					)					)

			snake_case				:    List[Any]           =	""" """.join(lowercase					)
			if result.returncode > 0:
						snake_case				:    List[Any]           =	"""\n""".join(result.stderr					)
						raise RuntimeError(
						    f"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
						    f"""The combined stderr from workers follows:\n{stderr}"""					)

			return result




class 						__lowercase		(UpperCamelCase__							):




						"""simple docstring"""





						pass




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=False					)  ->       List[Any]:
			try:
						snake_case				:    Dict           =	subprocess.check_output(lowercase						,stderr=subprocess.STDOUT					)
						if return_stdout:
									if hasattr(lowercase						,"""decode"""					):
												snake_case				:    Optional[Any]           =	output.decode("""utf-8"""					)
									return output
			except subprocess.CalledProcessError as e:
						raise SubprocessCallException(
						    f"""Command `{" ".join(lowercase					)}` failed with the following error:\n\n{e.output.decode()}"""					) from e











from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[str]:
   return ConvertCommand(
       args.model_type						,args.tf_checkpoint						,args.pytorch_dump_output						,args.config						,args.finetuning_task_name					)


lowerCamelCase	:	Tuple				=   '\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      @staticmethod
      def      UpperCAmelCase				(				A     )		->    List[str]:
         snake_case				:    Union[str, Any]           =	parser.add_parser(
             """convert"""    ,				help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."""    ,				)
         train_parser.add_argument("""--model_type"""    ,				type=A    ,				required=A    ,				help="""Model's type."""     )
         train_parser.add_argument(
             """--tf_checkpoint"""    ,				type=A    ,				required=A    ,				help="""TensorFlow checkpoint path or folder."""     )
         train_parser.add_argument(
             """--pytorch_dump_output"""    ,				type=A    ,				required=A    ,				help="""Path to the PyTorch saved model output."""     )
         train_parser.add_argument("""--config"""    ,				type=A    ,				default=""""""    ,				help="""Configuration file path or folder."""     )
         train_parser.add_argument(
             """--finetuning_task_name"""    ,				type=A    ,				default=A    ,				help="""Optional fine-tuning task name if the TF model was a finetuned model."""    ,				)
         train_parser.set_defaults(func=A     )

      def __init__(				self    ,				A    ,				A    ,				A    ,				A    ,				A    ,				*A    ,				)		->    List[Any]:
         snake_case				:    Any           =	logging.get_logger("""transformers-cli/converting"""     )

         self._logger.info(f"""Loading model {model_type}"""     )
         snake_case				:    int           =	model_type
         snake_case				:    Any           =	tf_checkpoint
         snake_case				:    int           =	pytorch_dump_output
         snake_case				:    List[str]           =	config
         snake_case				:    Tuple           =	finetuning_task_name

      def      UpperCAmelCase				(				self     )		->    int:
         if self._model_type == "albert":
            try:
               from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                   convert_tf_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "bert":
            try:
               from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                   convert_tf_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "funnel":
            try:
               from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                   convert_tf_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "t5":
            try:
               from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
            except ImportError:
               raise ImportError(A     )

            convert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "gpt":
            from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                convert_openai_checkpoint_to_pytorch,
            )

            convert_openai_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "transfo_xl":
            try:
               from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                   convert_transfo_xl_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            if "ckpt" in self._tf_checkpoint.lower():
               snake_case				:    List[Any]           =	self._tf_checkpoint
               snake_case				:    Tuple           =	""""""
            else:
               snake_case				:    Tuple           =	self._tf_checkpoint
               snake_case				:    Tuple           =	""""""
            convert_transfo_xl_checkpoint_to_pytorch(
                A    ,				self._config    ,				self._pytorch_dump_output    ,				A     )
         elif self._model_type == "gpt2":
            try:
               from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                   convert_gpta_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         elif self._model_type == "xlnet":
            try:
               from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                   convert_xlnet_checkpoint_to_pytorch,
               )
            except ImportError:
               raise ImportError(A     )

            convert_xlnet_checkpoint_to_pytorch(
                self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output    ,				self._finetuning_task_name     )
         elif self._model_type == "xlm":
            from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                convert_xlm_checkpoint_to_pytorch,
            )

            convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._pytorch_dump_output     )
         elif self._model_type == "lxmert":
            from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                convert_lxmert_checkpoint_to_pytorch,
            )

            convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._pytorch_dump_output     )
         elif self._model_type == "rembert":
            from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                convert_rembert_tf_checkpoint_to_pytorch,
            )

            convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint    ,				self._config    ,				self._pytorch_dump_output     )
         else:
            raise ValueError(
                """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""     )





