






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()

lowerCamelCase			:					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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case=False					, __snake_case=True       ):
     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(__snake_case					, __snake_case					, force_download=not use_cached_models       )
     __lowerCAmelCase								=					config_class.from_json_file(__snake_case       )
     __lowerCAmelCase								=					True
     __lowerCAmelCase								=					True
     print(F"""Building TensorFlow model from configuration: {config}"""       )
     __lowerCAmelCase								=					model_class(__snake_case       )

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

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

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

          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(__snake_case					, save_format="h5"       )





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

     for j, model_type in enumerate(__snake_case					, start=1       ):
          print("=" * 100       )
          print(F""" Converting model type {j}/{len(__snake_case       )}: {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(__snake_case					, __snake_case       )					, 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(__snake_case       )}: {model_shortcut_name} - model_type {model_type}"""       )
               print("-" * 100       )

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

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

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

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


if __name__ == "__main__":
 lowerCamelCase			:					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.''')
 lowerCamelCase			:					List[str]         =	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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase			:					int         =	logging.get_logger(__name__)

lowerCamelCase			:					Tuple         =	{
    '''caidas/swin2sr-classicalsr-x2-64''': (
        '''https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'''
    ),
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""swin2sr"""

      snake_case_  =							{
          """hidden_size""": """embed_dim""",
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }


      def __init__(	self					,						__UpperCamelCase=6_4					,						__UpperCamelCase=1					,						__UpperCamelCase=3					,						__UpperCamelCase=1_8_0					,						__UpperCamelCase=[6, 6, 6, 6, 6, 6]					,						__UpperCamelCase=[6, 6, 6, 6, 6, 6]					,						__UpperCamelCase=8					,						__UpperCamelCase=2.0					,						__UpperCamelCase=True					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.1					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=False					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-5					,						__UpperCamelCase=2					,						__UpperCamelCase=1.0					,						__UpperCamelCase="1conv"					,						__UpperCamelCase="pixelshuffle"					,						**__UpperCamelCase					,						)->	Tuple:
           super().__init__(**__UpperCamelCase	)

           __lowerCAmelCase								=					image_size
           __lowerCAmelCase								=					patch_size
           __lowerCAmelCase								=					num_channels
           __lowerCAmelCase								=					embed_dim
           __lowerCAmelCase								=					depths
           __lowerCAmelCase								=					len(__UpperCamelCase	)
           __lowerCAmelCase								=					num_heads
           __lowerCAmelCase								=					window_size
           __lowerCAmelCase								=					mlp_ratio
           __lowerCAmelCase								=					qkv_bias
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					drop_path_rate
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					use_absolute_embeddings
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					upscale
           __lowerCAmelCase								=					img_range
           __lowerCAmelCase								=					resi_connection
           __lowerCAmelCase								=					upsampler






