


"""simple docstring"""


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()

A  :     int    					=    {
    "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 _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase=False					,				_UpperCamelCase=True   ):

			'''simple docstring'''




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

			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    MODEL_CLASSES[model_type]

			# Initialise TF model
			if config_file in aws_config_map:
						__lowerCAmelCase       					=    cached_file(_UpperCamelCase					,				_UpperCamelCase					,				force_download=not use_cached_models   )
			__lowerCAmelCase       					=    config_class.from_json_file(_UpperCamelCase   )
			__lowerCAmelCase       					=    True
			__lowerCAmelCase       					=    True
			print(f"Building TensorFlow model from configuration: {config}"   )
			__lowerCAmelCase       					=    model_class(_UpperCamelCase   )

			# Load weights from tf checkpoint
			if pytorch_checkpoint_path in aws_config_map.keys():
						__lowerCAmelCase       					=    cached_file(
						    _UpperCamelCase					,				_UpperCamelCase					,				force_download=not use_cached_models   )
			# Load PyTorch checkpoint in tf2 model:
			__lowerCAmelCase       					=    load_pytorch_checkpoint_in_tfa_model(_UpperCamelCase					,				_UpperCamelCase   )

			if compare_with_pt_model:
						__lowerCAmelCase       					=    tf_model(tf_model.dummy_inputs					,				training=_UpperCamelCase   )  # build the network

						__lowerCAmelCase       					=    torch.load(_UpperCamelCase					,				map_location="cpu"   )
						__lowerCAmelCase       					=    pt_model_class.from_pretrained(
						    pretrained_model_name_or_path=_UpperCamelCase					,				config=_UpperCamelCase					,				state_dict=_UpperCamelCase   )

						with torch.no_grad():
									__lowerCAmelCase       					=    pt_model(**pt_model.dummy_inputs   )

						__lowerCAmelCase       					=    pto[0].numpy()
						__lowerCAmelCase       					=    tfo[0].numpy()
						__lowerCAmelCase       					=    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(_UpperCamelCase					,				save_format="h5"   )


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase=None					,				_UpperCamelCase=None					,				_UpperCamelCase=False					,				_UpperCamelCase=False					,				_UpperCamelCase=False					,				_UpperCamelCase=False					,				):

			'''simple docstring'''




			if args_model_type is None:
						__lowerCAmelCase       					=    list(MODEL_CLASSES.keys()   )
			else:
						__lowerCAmelCase       					=    [args_model_type]

			for j, model_type in enumerate(_UpperCamelCase					,				start=1   ):
						print("=" * 100   )
						print(f" Converting model type {j}/{len(_UpperCamelCase   )}: {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()   )}."   )

						__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    MODEL_CLASSES[model_type]

						if model_shortcut_names_or_path is None:
									__lowerCAmelCase       					=    list(aws_model_maps.keys()   )
						if config_shortcut_names_or_path is None:
									__lowerCAmelCase       					=    model_shortcut_names_or_path

						for i, (model_shortcut_name, config_shortcut_name) in enumerate(
						    zip(_UpperCamelCase					,				_UpperCamelCase   )					,				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
												__lowerCAmelCase       					=    model_shortcut_name
									elif only_convert_finetuned_models:
												print(f"    Skipping not finetuned checkpoint {model_shortcut_name}"   )
												continue
									print(
									    f"    Converting checkpoint {i}/{len(_UpperCamelCase   )}: {model_shortcut_name} - model_type {model_type}"   )
									print("-" * 100   )

									if config_shortcut_name in aws_config_map:
												__lowerCAmelCase       					=    cached_file(_UpperCamelCase					,				_UpperCamelCase					,				force_download=not use_cached_models   )
									else:
												__lowerCAmelCase       					=    config_shortcut_name

									if model_shortcut_name in aws_model_maps:
												__lowerCAmelCase       					=    cached_file(_UpperCamelCase					,				_UpperCamelCase					,				force_download=not use_cached_models   )
									else:
												__lowerCAmelCase       					=    model_shortcut_name

									if os.path.isfile(_UpperCamelCase   ):
												__lowerCAmelCase       					=    "converted_model"

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


if __name__ == "__main__":
	A  :     Optional[int]    					=    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.")
	A  :     List[Any]    					=    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,
	)








"""simple docstring"""


import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
	from sagemaker import Session, TrainingJobAnalytics
	from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER""" ,"""False"""	)	) is not True ,reason="""Skipping test because should only be run when releasing minor transformers version""" ,)
@pytest.mark.usefixtures("""sm_env"""	)
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]	)
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										if self.framework == "pytorch":
													subprocess.run(
													    f"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split()					,     encoding="utf-8"					,     check=__a					,     )
										assert hasattr(self					,     "env"			)

							def     snake_case       (  self					,     __a			):
										# configuration for running training on smdistributed Model Parallel
										__lowerCAmelCase       					=    {
										    "enabled": True,
										    "processes_per_host": 8,
										}
										__lowerCAmelCase       					=    {
										    "enabled": True,
										    "parameters": {
										        "microbatches": 4,
										        "placement_strategy": "spread",
										        "pipeline": "interleaved",
										        "optimize": "speed",
										        "partitions": 4,
										        "ddp": True,
										    },
										}

										__lowerCAmelCase       					=    {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

										__lowerCAmelCase       					=    "trainer" if self.script == "run_glue.py" else "smtrainer"
										# creates estimator
										return HuggingFace(
										    entry_point=self.script					,     source_dir=self.env.test_path					,     role=self.env.role					,     image_uri=self.env.image_uri					,     base_job_name=f"{self.env.base_job_name}-{instance_count}-smp-{name_extension}"					,     instance_count=__a					,     instance_type=self.instance_type					,     debugger_hook_config=__a					,     hyperparameters={
										        **self.env.hyperparameters,
										        "model_name_or_path": self.model_name_or_path,
										        "max_steps": 5_00,
										    }					,     metric_definitions=self.env.metric_definitions					,     distribution=__a					,     py_version="py36"					,     )

							def     snake_case       (  self					,     __a			):
										TrainingJobAnalytics(__a			).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"			)



							@parameterized.expand([(1,)]			)
							def     snake_case       (  self					,     __a			):
										# create estimator
										__lowerCAmelCase       					=    self.create_estimator(__a			)

										# run training
										estimator.fit()

										# result dataframe
										__lowerCAmelCase       					=    TrainingJobAnalytics(estimator.latest_training_job.name			).dataframe()

										# extract kpis
										__lowerCAmelCase       					=    list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]			)
										__lowerCAmelCase       					=    list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]			)
										# get train time from SageMaker job, this includes starting, preprocessing, stopping
										__lowerCAmelCase       					=    (
										    Session().describe_training_job(estimator.latest_training_job.name			).get("TrainingTimeInSeconds"					,     99_99_99			)
										)

										# assert kpis
										assert train_runtime <= self.results["train_runtime"]
										assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy			)
										assert all(t <= self.results["eval_loss"] for t in eval_loss			)

										# dump tests result into json file to share in PR
										with open(f"{estimator.latest_training_job.name}.json"					,     "w"			) as outfile:
													json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}					,     __a			)




