






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()

_UpperCAmelCase :			Any              =	{
    "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 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase=False   ,	lowercase=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     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					MODEL_CLASSES[model_type]

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

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

		if compare_with_pt_model:
				UpperCamelCase          =					tf_model(tf_model.dummy_inputs   ,	training=lowercase			)  # build the network

				UpperCamelCase          =					torch.load(lowercase   ,	map_location='cpu'			)
				UpperCamelCase          =					pt_model_class.from_pretrained(
				    pretrained_model_name_or_path=lowercase   ,	config=lowercase   ,	state_dict=lowercase			)

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

				UpperCamelCase          =					pto[0].numpy()
				UpperCamelCase          =					tfo[0].numpy()
				UpperCamelCase          =					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(lowercase   ,	save_format='h5'			)


def 				A	( lowercase   ,	lowercase   ,	lowercase=None   ,	lowercase=None   ,	lowercase=False   ,	lowercase=False   ,	lowercase=False   ,	lowercase=False   ,	)       ->  Union[str, Any]:
		'''simple docstring'''

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

		for j, model_type in enumerate(lowercase   ,	start=1			):
				print('=' * 100			)
				print(f''' Converting model type {j}/{len(lowercase			)}: {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     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					MODEL_CLASSES[model_type]

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

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

						if config_shortcut_name in aws_config_map:
								UpperCamelCase          =					cached_file(lowercase   ,	lowercase   ,	force_download=not use_cached_models			)
						else:
								UpperCamelCase          =					config_shortcut_name

						if model_shortcut_name in aws_model_maps:
								UpperCamelCase          =					cached_file(lowercase   ,	lowercase   ,	force_download=not use_cached_models			)
						else:
								UpperCamelCase          =					model_shortcut_name

						if os.path.isfile(lowercase			):
								UpperCamelCase          =					'converted_model'

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


if __name__ == "__main__":
					_UpperCAmelCase :			List[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.")
					_UpperCAmelCase :			Optional[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 json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Any     					=    CTRLTokenizer
		__lowercase	:	Any     					=    False
		__lowercase	:	Union[str, Any]     					=    False



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				UpperCamelCase          =					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
				UpperCamelCase          =					dict(zip(A_    ,				range(len(A_	)	)	)	)
				UpperCamelCase          =					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
				UpperCamelCase          =					{'unk_token': '<unk>'}

				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file']	)
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file']	)
				with open(self.vocab_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write(json.dumps(A_	) + '\n'	)
				with open(self.merges_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write('\n'.join(A_	)	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> List[Any]:
				"""simple docstring"""
				kwargs.update(self.special_tokens_map	)
				return CTRLTokenizer.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt react readapt apt'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					CTRLTokenizer(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map	)
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
				UpperCamelCase          =					tokenizer.tokenize(A_	)
				self.assertListEqual(A_    ,				A_	)

				UpperCamelCase          =					tokens + [tokenizer.unk_token]

				UpperCamelCase          =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)    ,				A_	)
