






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 				lowerCamelCase__				(_UpperCAmelCase):
			return ConvertCommand(
			    args.model_type							,       args.tf_checkpoint							,       args.pytorch_dump_output							,       args.config							,       args.finetuning_task_name)


a_		:						Union[str, Any]             =    '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''







class 		_snake_case  (  SCREAMING_SNAKE_CASE__					):






			@staticmethod
			def    SCREAMING_SNAKE_CASE__    (       a)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= 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=_lowercase		,					required=_lowercase		,					help='Model\'s type.')
						train_parser.add_argument(
						    '--tf_checkpoint'		,					type=_lowercase		,					required=_lowercase		,					help='TensorFlow checkpoint path or folder.')
						train_parser.add_argument(
						    '--pytorch_dump_output'		,					type=_lowercase		,					required=_lowercase		,					help='Path to the PyTorch saved model output.')
						train_parser.add_argument('--config'		,					type=_lowercase		,					default=''		,					help='Configuration file path or folder.')
						train_parser.add_argument(
						    '--finetuning_task_name'		,					type=_lowercase		,					default=_lowercase		,					help='Optional fine-tuning task name if the TF model was a finetuned model.'		,					)
						train_parser.set_defaults(func=_lowercase)






			def __init__(       self		,					a		,					a		,					a		,					a		,					a		,					*a		,					)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= logging.get_logger('transformers-cli/converting')

						self._logger.info(f'''Loading model {model_type}''')
						SCREAMING_SNAKE_CASE						= model_type
						SCREAMING_SNAKE_CASE						= tf_checkpoint
						SCREAMING_SNAKE_CASE						= pytorch_dump_output
						SCREAMING_SNAKE_CASE						= config
						SCREAMING_SNAKE_CASE						= finetuning_task_name







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:

						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(_lowercase)

									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(_lowercase)

									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(_lowercase)

									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(_lowercase)

									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(_lowercase)

									if "ckpt" in self._tf_checkpoint.lower():
												SCREAMING_SNAKE_CASE						= self._tf_checkpoint
												SCREAMING_SNAKE_CASE						= """"""
									else:
												SCREAMING_SNAKE_CASE						= self._tf_checkpoint
												SCREAMING_SNAKE_CASE						= """"""
									convert_transfo_xl_checkpoint_to_pytorch(
									    _lowercase		,					self._config		,					self._pytorch_dump_output		,					_lowercase)
						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(_lowercase)

									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(_lowercase)

									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]')












# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


a_		:						Dict             =    Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

a_		:						List[str]             =    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

a_		:						Union[str, Any]             =    'zero2'
a_		:						List[Any]             =    'zero3'
a_		:						List[str]             =    [ZEROa, ZEROa]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# customize the test name generator function as we want both params to appear in the sub-test
			# name, as by default it shows only the first param
			SCREAMING_SNAKE_CASE						= parameterized.to_safe_name('_'.join(str(_UpperCAmelCase) for x in param.args))
			return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
a_		:						List[Any]             =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 		_snake_case  (  A__					):






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[str]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       int:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						# XXX: run_asr is premature and doesn't save any results
						# so all we check for now is that the process didn't fail
						pass






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = True		,					a = True		,					a = True		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= models[model]

						SCREAMING_SNAKE_CASE						= self.run_trainer(
						    stage=a		,					model_name=a		,					eval_steps=a		,					num_train_epochs=1		,					distributed=a		,					fpaa=a		,					)

						self.do_checks(a)

						return output_dir






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = 1		,					a = True		,					a = True		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.get_auto_remove_tmp_dir('./xxx'		,					after=a)
						SCREAMING_SNAKE_CASE						= f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(a)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

						if fpaa:
									args.extend(['--fp16'])

						# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
						# hence the separate config files
						SCREAMING_SNAKE_CASE						= f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
						SCREAMING_SNAKE_CASE						= [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
						SCREAMING_SNAKE_CASE						= self.get_launcher(a)

						SCREAMING_SNAKE_CASE						= launcher + script + args + ds_args
						# keep for quick debug
						# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
						execute_subprocess_async(a		,					env=self.get_env())

						return output_dir







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       Optional[int]:
						# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
						# - it won't be able to handle that
						# 2. for now testing with just 2 gpus max (since some quality tests may give different
						# results with mode gpus because we use very little data)
						SCREAMING_SNAKE_CASE						= min(2		,					get_gpu_count()) if distributed else 1
						return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




