


import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
		import numpy as np
		import torch

		from . import (
		    AlbertForPreTraining,
		    BartForConditionalGeneration,
		    BertForPreTraining,
		    BertForQuestionAnswering,
		    BertForSequenceClassification,
		    CamembertForMaskedLM,
		    CTRLLMHeadModel,
		    DistilBertForMaskedLM,
		    DistilBertForQuestionAnswering,
		    DPRContextEncoder,
		    DPRQuestionEncoder,
		    DPRReader,
		    ElectraForPreTraining,
		    FlaubertWithLMHeadModel,
		    GPTaLMHeadModel,
		    LayoutLMForMaskedLM,
		    LxmertForPreTraining,
		    LxmertVisualFeatureEncoder,
		    OpenAIGPTLMHeadModel,
		    RobertaForMaskedLM,
		    RobertaForSequenceClassification,
		    TaForConditionalGeneration,
		    TransfoXLLMHeadModel,
		    XLMRobertaForMaskedLM,
		    XLMWithLMHeadModel,
		    XLNetLMHeadModel,
		)


logging.set_verbosity_info()

lowercase						:      List[str]									= {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int		,						_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Any=False		,						_lowerCamelCase				:    Optional[Any]=True)      ->					Union[str, Any]:






							'''simple docstring'''





							if model_type not in MODEL_CLASSES:
														raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys())}.')

							__UpperCamelCase			:     Optional[Any]         =      MODEL_CLASSES[model_type]

							# Initialise TF model
							if config_file in aws_config_map:
														__UpperCamelCase			:     Optional[Any]         =      cached_file(_lowerCamelCase		,						_lowerCamelCase		,						force_download=not use_cached_models)
							__UpperCamelCase			:     Any         =      config_class.from_json_file(_lowerCamelCase)
							__UpperCamelCase			:     Dict         =      True
							__UpperCamelCase			:     str         =      True
							print(F'Building TensorFlow model from configuration: {config}')
							__UpperCamelCase			:     Dict         =      model_class(_lowerCamelCase)

							# Load weights from tf checkpoint
							if pytorch_checkpoint_path in aws_config_map.keys():
														__UpperCamelCase			:     Any         =      cached_file(
														    _lowerCamelCase		,						_lowerCamelCase		,						force_download=not use_cached_models)
							# Load PyTorch checkpoint in tf2 model:
							__UpperCamelCase			:     List[str]         =      load_pytorch_checkpoint_in_tfa_model(_lowerCamelCase		,						_lowerCamelCase)

							if compare_with_pt_model:
														__UpperCamelCase			:     Dict         =      tf_model(tf_model.dummy_inputs		,						training=_lowerCamelCase)  # build the network

														__UpperCamelCase			:     Tuple         =      torch.load(_lowerCamelCase		,						map_location="cpu")
														__UpperCamelCase			:     List[Any]         =      pt_model_class.from_pretrained(
														    pretrained_model_name_or_path=_lowerCamelCase		,						config=_lowerCamelCase		,						state_dict=_lowerCamelCase)

														with torch.no_grad():
																					__UpperCamelCase			:     Optional[Any]         =      pt_model(**pt_model.dummy_inputs)

														__UpperCamelCase			:     Dict         =      pto[0].numpy()
														__UpperCamelCase			:     Optional[Any]         =      tfo[0].numpy()
														__UpperCamelCase			:     Tuple         =      np.amax(np.abs(np_pt - np_tf))
														print(F'Max absolute difference between models outputs {diff}')
														assert diff <= 2e-2, F'Error, model absolute difference is >2e-2: {diff}'

							# Save pytorch-model
							print(F'Save TensorFlow model to {tf_dump_path}')
							tf_model.save_weights(_lowerCamelCase		,						save_format="h5")




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    Dict=None		,						_lowerCamelCase				:    Optional[int]=None		,						_lowerCamelCase				:    List[str]=False		,						_lowerCamelCase				:    str=False		,						_lowerCamelCase				:    int=False		,						_lowerCamelCase				:    Union[str, Any]=False		,						)      ->					Union[str, Any]:






							'''simple docstring'''





							if args_model_type is None:
														__UpperCamelCase			:     str         =      list(MODEL_CLASSES.keys())
							else:
														__UpperCamelCase			:     int         =      [args_model_type]

							for j, model_type in enumerate(_lowerCamelCase		,						start=1):
														print("=" * 100)
														print(F' Converting model type {j}/{len(_lowerCamelCase)}: {model_type}')
														print("=" * 100)
														if model_type not in MODEL_CLASSES:
																					raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys())}.')

														__UpperCamelCase			:     Union[str, Any]         =      MODEL_CLASSES[model_type]

														if model_shortcut_names_or_path is None:
																					__UpperCamelCase			:     Any         =      list(aws_model_maps.keys())
														if config_shortcut_names_or_path is None:
																					__UpperCamelCase			:     int         =      model_shortcut_names_or_path

														for i, (model_shortcut_name, config_shortcut_name) in enumerate(
														    zip(_lowerCamelCase		,						_lowerCamelCase)		,						start=1):
																					print("-" * 100)
																					if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
																												if not only_convert_finetuned_models:
																																			print(F'    Skipping finetuned checkpoint {model_shortcut_name}')
																																			continue
																												__UpperCamelCase			:     Optional[Any]         =      model_shortcut_name
																					elif only_convert_finetuned_models:
																												print(F'    Skipping not finetuned checkpoint {model_shortcut_name}')
																												continue
																					print(
																					    F'    Converting checkpoint {i}/{len(_lowerCamelCase)}: {model_shortcut_name} - model_type {model_type}')
																					print("-" * 100)

																					if config_shortcut_name in aws_config_map:
																												__UpperCamelCase			:     Tuple         =      cached_file(_lowerCamelCase		,						_lowerCamelCase		,						force_download=not use_cached_models)
																					else:
																												__UpperCamelCase			:     int         =      config_shortcut_name

																					if model_shortcut_name in aws_model_maps:
																												__UpperCamelCase			:     str         =      cached_file(_lowerCamelCase		,						_lowerCamelCase		,						force_download=not use_cached_models)
																					else:
																												__UpperCamelCase			:     Dict         =      model_shortcut_name

																					if os.path.isfile(_lowerCamelCase):
																												__UpperCamelCase			:     Any         =      "converted_model"

																					convert_pt_checkpoint_to_tf(
																					    model_type=_lowerCamelCase		,						pytorch_checkpoint_path=_lowerCamelCase		,						config_file=_lowerCamelCase		,						tf_dump_path=os.path.join(_lowerCamelCase		,						model_shortcut_name + "-tf_model.h5")		,						compare_with_pt_model=_lowerCamelCase		,						)
																					if remove_cached_files:
																												os.remove(_lowerCamelCase)
																												os.remove(_lowerCamelCase)


if __name__ == "__main__":
		lowercase						:      List[Any]									= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
		)
		parser.add_argument(
		    '--model_type',
		    default=None,
		    type=str,
		    help=(
		        f"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and "
		        'convert all the models from AWS.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_checkpoint_path',
		    default=None,
		    type=str,
		    help=(
		        'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
		        'If not given, will download and convert all the checkpoints from AWS.'
		    ),
		)
		parser.add_argument(
		    '--config_file',
		    default=None,
		    type=str,
		    help=(
		        'The config json file corresponding to the pre-trained model. \n'
		        'This specifies the model architecture. If not given and '
		        '--pytorch_checkpoint_path is not given or is a shortcut name '
		        'use the configuration associated to the shortcut name on the AWS'
		    ),
		)
		parser.add_argument(
		    '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
		)
		parser.add_argument(
		    '--use_cached_models',
		    action='store_true',
		    help='Use cached models if possible instead of updating to latest checkpoint versions.',
		)
		parser.add_argument(
		    '--remove_cached_files',
		    action='store_true',
		    help='Remove pytorch models after conversion (save memory when converting in batches).',
		)
		parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
		lowercase						:      int									= parser.parse_args()

		# if args.pytorch_checkpoint_path is not None:
		#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
		#                                 args.pytorch_checkpoint_path,
		#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
		#                                 args.tf_dump_path,
		#                                 compare_with_pt_model=args.compare_with_pt_model,
		#                                 use_cached_models=args.use_cached_models)
		# else:
		convert_all_pt_checkpoints_to_tf(
		    args.model_type.lower() if args.model_type is not None else None,
		    args.tf_dump_path,
		    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
		    if args.pytorch_checkpoint_path is not None
		    else None,
		    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
		    compare_with_pt_model=args.compare_with_pt_model,
		    use_cached_models=args.use_cached_models,
		    remove_cached_files=args.remove_cached_files,
		    only_convert_finetuned_models=args.only_convert_finetuned_models,
		)

import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  ComputeEnvironment.AMAZON_SAGEMAKER
	_A			=  True
	_A			=  'ml.p3.2xlarge'
	_A			=  'accelerate_sagemaker_execution_role'
	_A			=  'hf-sm'
	_A			=  'us-east-1'
	_A			=  1
	_A			=  'accelerate-sagemaker-1'
	_A			=  '1.6'
	_A			=  '4.4'
	_A			=  'train.py'
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    'False',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]
	_A			=  [
	    '--model_name_or_path',
	    'bert',
	    '--do_train',
	    '--do_test',
	    'False',
	    '--do_predict',
	    '--epochs',
	    '3',
	    '--learning_rate',
	    '5e-5',
	    '--max_steps',
	    '50.5',
	]




class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Tuple		)	->				str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								__UpperCamelCase			:     str         =      _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
								assert isinstance(converted_args["model_name_or_path"]  ,     a		)
								assert isinstance(converted_args["do_train"]  ,     a		)
								assert isinstance(converted_args["epochs"]  ,     a		)
								assert isinstance(converted_args["learning_rate"]  ,     a		)
								assert isinstance(converted_args["max_steps"]  ,     a		)

								with pytest.raises(a		):
															_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args		)