'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	int				,     _a	:	Dict			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  1.5
    __UpperCamelCase	:	Optional[int]		    =  int(factor * num_class_images			)
    __UpperCamelCase	:	Any		    =  ClipClient(
        url='https://knn.laion.ai/knn-service'				,     indice_name='laion_400m'				,     num_images=__snake_case				,     aesthetic_weight=0.1			)

    os.makedirs(f"""{class_data_dir}/images"""				,     exist_ok=__snake_case			)
    if len(list(Path(f"""{class_data_dir}/images"""			).iterdir()			)			) >= num_class_images:
        return

    while True:
        __UpperCamelCase	:	str		    =  client.query(text=__snake_case			)
        if len(__snake_case			) >= factor * num_class_images or num_images > 1E4:
            break
        else:
            __UpperCamelCase	:	Tuple		    =  int(factor * num_images			)
            __UpperCamelCase	:	int		    =  ClipClient(
                url='https://knn.laion.ai/knn-service'				,     indice_name='laion_400m'				,     num_images=__snake_case				,     aesthetic_weight=0.1				,     )

    __UpperCamelCase	:	Optional[int]		    =  0
    __UpperCamelCase	:	int		    =  0
    __UpperCamelCase	:	Optional[int]		    =  tqdm(desc='downloading real regularization images'				,     total=__snake_case			)

    with open(f"""{class_data_dir}/caption.txt"""				,     'w'			) as fa, open(f"""{class_data_dir}/urls.txt"""				,     'w'			) as fa, open(
        f"""{class_data_dir}/images.txt"""				,     'w'			) as fa:
        while total < num_class_images:
            __UpperCamelCase	:	Union[str, Any]		    =  class_images[count]
            count += 1
            try:
                __UpperCamelCase	:	Tuple		    =  requests.get(images['url']			)
                if img.status_code == 2_0_0:
                    __UpperCamelCase	:	Dict		    =  Image.open(BytesIO(img.content			)			)
                    with open(f"""{class_data_dir}/images/{total}.jpg"""				,     'wb'			) as f:
                        f.write(img.content			)
                    fa.write(images['caption'] + '\n'			)
                    fa.write(images['url'] + '\n'			)
                    fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n'			)
                    total += 1
                    pbar.update(1			)
                else:
                    continue
            except Exception:
                continue
    return







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  argparse.ArgumentParser(''				,     add_help=__snake_case			)
    parser.add_argument('--class_prompt'				,     help='text prompt to retrieve images'				,     required=__snake_case				,     type=__snake_case			)
    parser.add_argument('--class_data_dir'				,     help='path to save images'				,     required=__snake_case				,     type=__snake_case			)
    parser.add_argument('--num_class_images'				,     help='number of images to download'				,     default=2_0_0				,     type=__snake_case			)
    return parser.parse_args()


if __name__ == "__main__":
  a=  parse_args()
  retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)






'''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=  {
    '''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  _UpperCamelCase (     _a	:	int				,     _a	:	Union[str, Any]				,     _a	:	str				,     _a	:	List[str]				,     _a	:	Dict=False				,     _a	:	str=True			):




    """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	:	Optional[int]		    =  MODEL_CLASSES[model_type]

    # Initialise TF model
    if config_file in aws_config_map:
        __UpperCamelCase	:	List[Any]		    =  cached_file(_a				,     _a				,     force_download=not use_cached_models			)
    __UpperCamelCase	:	Union[str, Any]		    =  config_class.from_json_file(_a			)
    __UpperCamelCase	:	Union[str, Any]		    =  True
    __UpperCamelCase	:	List[str]		    =  True
    print(f"""Building TensorFlow model from configuration: {config}"""			)
    __UpperCamelCase	:	Optional[int]		    =  model_class(_a			)

    # Load weights from tf checkpoint
    if pytorch_checkpoint_path in aws_config_map.keys():
        __UpperCamelCase	:	Union[str, Any]		    =  cached_file(
            _a				,     _a				,     force_download=not use_cached_models			)
    # Load PyTorch checkpoint in tf2 model:
    __UpperCamelCase	:	Any		    =  load_pytorch_checkpoint_in_tfa_model(_a				,     _a			)

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

        __UpperCamelCase	:	List[str]		    =  torch.load(_a				,     map_location='cpu'			)
        __UpperCamelCase	:	Union[str, Any]		    =  pt_model_class.from_pretrained(
            pretrained_model_name_or_path=_a				,     config=_a				,     state_dict=_a			)

        with torch.no_grad():
            __UpperCamelCase	:	Any		    =  pt_model(**pt_model.dummy_inputs			)

        __UpperCamelCase	:	Optional[Any]		    =  pto[0].numpy()
        __UpperCamelCase	:	int		    =  tfo[0].numpy()
        __UpperCamelCase	:	Union[str, Any]		    =  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  _UpperCamelCase (     _a	:	str				,     _a	:	Optional[Any]				,     _a	:	Dict=None				,     _a	:	Tuple=None				,     _a	:	int=False				,     _a	:	Union[str, Any]=False				,     _a	:	int=False				,     _a	:	List[str]=False				,     ):




    """simple docstring"""



    if args_model_type is None:
        __UpperCamelCase	:	Dict		    =  list(MODEL_CLASSES.keys()			)
    else:
        __UpperCamelCase	:	Union[str, Any]		    =  [args_model_type]

    for j, model_type in enumerate(_a				,     start=1			):
        print('=' * 1_0_0			)
        print(f""" Converting model type {j}/{len(_a			)}: {model_type}"""			)
        print('=' * 1_0_0			)
        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	:	str		    =  MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            __UpperCamelCase	:	List[str]		    =  list(aws_model_maps.keys()			)
        if config_shortcut_names_or_path is None:
            __UpperCamelCase	:	Optional[int]		    =  model_shortcut_names_or_path

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(_a				,     _a			)				,     start=1			):
            print('-' * 1_0_0			)
            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	:	Dict		    =  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('-' * 1_0_0			)

            if config_shortcut_name in aws_config_map:
                __UpperCamelCase	:	Tuple		    =  cached_file(_a				,     _a				,     force_download=not use_cached_models			)
            else:
                __UpperCamelCase	:	Dict		    =  config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                __UpperCamelCase	:	Dict		    =  cached_file(_a				,     _a				,     force_download=not use_cached_models			)
            else:
                __UpperCamelCase	:	Tuple		    =  model_shortcut_name

            if os.path.isfile(_a			):
                __UpperCamelCase	:	int		    =  '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__":
  a=  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=  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,
  )



