



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()

SCREAMING_SNAKE_CASE__   			=   {
    '''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    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=False			,						__UpperCamelCase=True       )    ->   List[Any]:
			if model_type not in MODEL_CLASSES:
						raise ValueError(f'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()       )}.'''       )

			A__  ,			A__  ,			A__  ,			A__	  =     MODEL_CLASSES[model_type]

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

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

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

						A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )
						A__	  =     pt_model_class.from_pretrained(
						    pretrained_model_name_or_path=__UpperCamelCase			,						config=__UpperCamelCase			,						state_dict=__UpperCamelCase       )

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

						A__	  =     pto[0].numpy()
						A__	  =     tfo[0].numpy()
						A__	  =     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 A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=None			,						__UpperCamelCase=None			,						__UpperCamelCase=False			,						__UpperCamelCase=False			,						__UpperCamelCase=False			,						__UpperCamelCase=False			,						)    ->   Optional[int]:
			if args_model_type is None:
						A__	  =     list(MODEL_CLASSES.keys()       )
			else:
						A__	  =     [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()       )}.'''       )

						A__  ,			A__  ,			A__  ,			A__  ,			A__	  =     MODEL_CLASSES[model_type]

						if model_shortcut_names_or_path is None:
									A__	  =     list(aws_model_maps.keys()       )
						if config_shortcut_names_or_path is None:
									A__	  =     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
												A__	  =     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:
												A__	  =     cached_file(__UpperCamelCase			,						__UpperCamelCase			,						force_download=not use_cached_models       )
									else:
												A__	  =     config_shortcut_name

									if model_shortcut_name in aws_model_maps:
												A__	  =     cached_file(__UpperCamelCase			,						__UpperCamelCase			,						force_download=not use_cached_models       )
									else:
												A__	  =     model_shortcut_name

									if os.path.isfile(__UpperCamelCase       ):
												A__	  =     '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__":
							SCREAMING_SNAKE_CASE__   			=   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.''')
							SCREAMING_SNAKE_CASE__   			=   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,
							)









from __future__ import annotations


def A    (							__UpperCamelCase = 4       )    ->   list[list[int]]:
			A__	  =     abs(__UpperCamelCase       ) or 4
			return [[1 + x + y * row_size for x in range(__UpperCamelCase       )] for y in range(__UpperCamelCase       )]


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_column(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(reverse_column(__UpperCamelCase       )       )
			# OR.. reverse_column(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_column(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [list(__UpperCamelCase       ) for x in zip(*__UpperCamelCase       )]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     matrix[::-1]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [x[::-1] for x in matrix]
			return matrix


def A    (							__UpperCamelCase       )    ->   None:
			for i in matrix:
						print(*__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 90 counterclockwise:\n''')
							print_matrix(rotate_aa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 180:\n''')
							print_matrix(rotate_aaa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 270 counterclockwise:\n''')
							print_matrix(rotate_aaa(matrix))




