




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 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A=False     ,	_A=True							):







	'''simple docstring'''





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

	snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_		    =     MODEL_CLASSES[model_type]

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

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

	if compare_with_pt_model:
		snake_case_		    =     tf_model(tf_model.dummy_inputs     ,	training=_A							)  # build the network

		snake_case_		    =     torch.load(_A     ,	map_location="cpu"							)
		snake_case_		    =     pt_model_class.from_pretrained(
		    pretrained_model_name_or_path=_A     ,	config=_A     ,	state_dict=_A							)

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

		snake_case_		    =     pto[0].numpy()
		snake_case_		    =     tfo[0].numpy()
		snake_case_		    =     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(_A     ,	save_format="h5"							)


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A=None     ,	_A=None     ,	_A=False     ,	_A=False     ,	_A=False     ,	_A=False     ,	):







	'''simple docstring'''





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

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

		snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_    ,      snake_case_		    =     MODEL_CLASSES[model_type]

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

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

			if config_shortcut_name in aws_config_map:
				snake_case_		    =     cached_file(_A     ,	_A     ,	force_download=not use_cached_models							)
			else:
				snake_case_		    =     config_shortcut_name

			if model_shortcut_name in aws_model_maps:
				snake_case_		    =     cached_file(_A     ,	_A     ,	force_download=not use_cached_models							)
			else:
				snake_case_		    =     model_shortcut_name

			if os.path.isfile(_A							):
				snake_case_		    =     "converted_model"

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


if __name__ == "__main__":
		lowercase__				:     str 		=						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,
		)






def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	_enforce_args(_A     ,	_A							)
	if n == 0:
		return 0
	snake_case_		    =     float("-inf"							)
	for i in range(1     ,	n + 1							):
		snake_case_		    =     max(
		    _A     ,	prices[i - 1] + naive_cut_rod_recursive(n - i     ,	_A							)							)

	return max_revue


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	_enforce_args(_A     ,	_A							)
	snake_case_		    =     [float("-inf"							) for _ in range(n + 1							)]
	return _top_down_cut_rod_recursive(_A     ,	_A     ,	_A							)


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





	if max_rev[n] >= 0:
		return max_rev[n]
	elif n == 0:
		return 0
	else:
		snake_case_		    =     float("-inf"							)
		for i in range(1     ,	n + 1							):
			snake_case_		    =     max(
			    _A     ,	prices[i - 1] + _top_down_cut_rod_recursive(n - i     ,	_A     ,	_A							)     ,	)

		snake_case_		    =     max_revenue

	return max_rev[n]


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	_enforce_args(_A     ,	_A							)

	# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
	# length 0.
	snake_case_		    =     [float("-inf"							) for _ in range(n + 1							)]
	snake_case_		    =     0

	for i in range(1     ,	n + 1							):
		snake_case_		    =     max_rev[i]
		for j in range(1     ,	i + 1							):
			snake_case_		    =     max(_A     ,	prices[j - 1] + max_rev[i - j]							)

		snake_case_		    =     max_revenue_i

	return max_rev[n]


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	if n < 0:
		snake_case_		    =     f"n must be greater than or equal to 0. Got n = {n}"
		raise ValueError(_A							)

	if n > len(_A							):
		snake_case_		    =     (
		    "Each integral piece of rod must have a corresponding price. "
		    f"Got n = {n} but length of prices = {len(_A							)}"
		)
		raise ValueError(_A							)


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     [6, 10, 12, 15, 20, 23]
	snake_case_		    =     len(_A							)

	# the best revenue comes from cutting the rod into 6 pieces, each
	# of length 1 resulting in a revenue of 6 * 6 = 36.
	snake_case_		    =     36

	snake_case_		    =     top_down_cut_rod(_A     ,	_A							)
	snake_case_		    =     bottom_up_cut_rod(_A     ,	_A							)
	snake_case_		    =     naive_cut_rod_recursive(_A     ,	_A							)

	assert expected_max_revenue == max_rev_top_down
	assert max_rev_top_down == max_rev_bottom_up
	assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
		main()
