





'''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()

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 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Optional[int]    ,       UpperCamelCase__    : Union[str, Any]    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : Any=False    ,       UpperCamelCase__    : Optional[Any]=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()		)}.'''		)

    A       :       Optional[Any]							=						MODEL_CLASSES[model_type]

    # Initialise TF model
    if config_file in aws_config_map:
        A       :       Optional[Any]							=						cached_file(_lowerCamelCase    ,       _lowerCamelCase    ,       force_download=not use_cached_models		)
    A       :       Any							=						config_class.from_json_file(_lowerCamelCase		)
    A       :       Dict							=						True
    A       :       str							=						True
    print(F'''Building TensorFlow model from configuration: {config}'''		)
    A       :       Dict							=						model_class(_lowerCamelCase		)

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

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

        A       :       Tuple							=						torch.load(_lowerCamelCase    ,       map_location='''cpu'''		)
        A       :       List[Any]							=						pt_model_class.from_pretrained(
            pretrained_model_name_or_path=_lowerCamelCase    ,       config=_lowerCamelCase    ,       state_dict=_lowerCamelCase		)

        with torch.no_grad():
            A       :       Optional[Any]							=						pt_model(**pt_model.dummy_inputs		)

        A       :       Dict							=						pto[0].numpy()
        A       :       Optional[Any]							=						tfo[0].numpy()
        A       :       Tuple							=						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(_lowerCamelCase    ,       save_format='''h5'''		)
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[Any]    ,       UpperCamelCase__    : int    ,       UpperCamelCase__    : Dict=None    ,       UpperCamelCase__    : Optional[int]=None    ,       UpperCamelCase__    : List[str]=False    ,       UpperCamelCase__    : str=False    ,       UpperCamelCase__    : int=False    ,       UpperCamelCase__    : Union[str, Any]=False    ,       )		->  Union[str, Any]:

    '''simple docstring'''





    if args_model_type is None:
        A       :       str							=						list(MODEL_CLASSES.keys()		)
    else:
        A       :       int							=						[args_model_type]

    for j, model_type in enumerate(_lowerCamelCase    ,       start=1		):
        print('''=''' * 100		)
        print(F''' Converting model type {j}/{len(_lowerCamelCase		)}: {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       :       Union[str, Any]							=						MODEL_CLASSES[model_type]

        if model_shortcut_names_or_path is None:
            A       :       Any							=						list(aws_model_maps.keys()		)
        if config_shortcut_names_or_path is None:
            A       :       int							=						model_shortcut_names_or_path

        for i, (model_shortcut_name, config_shortcut_name) in enumerate(
            zip(_lowerCamelCase    ,       _lowerCamelCase		)    ,       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       :       Optional[Any]							=						model_shortcut_name
            elif only_convert_finetuned_models:
                print(F'''    Skipping not finetuned checkpoint {model_shortcut_name}'''		)
                continue
            print(
                F'''    Converting checkpoint {i}/{len(_lowerCamelCase		)}: {model_shortcut_name} - model_type {model_type}'''		)
            print('''-''' * 100		)

            if config_shortcut_name in aws_config_map:
                A       :       Tuple							=						cached_file(_lowerCamelCase    ,       _lowerCamelCase    ,       force_download=not use_cached_models		)
            else:
                A       :       int							=						config_shortcut_name

            if model_shortcut_name in aws_model_maps:
                A       :       str							=						cached_file(_lowerCamelCase    ,       _lowerCamelCase    ,       force_download=not use_cached_models		)
            else:
                A       :       Dict							=						model_shortcut_name

            if os.path.isfile(_lowerCamelCase		):
                A       :       Any							=						"converted_model"

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


if __name__ == "__main__":
     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.""")
     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,
     )







'''simple docstring'''






import requests

snake_case_     =	"""YOUR API KEY"""
def 							_lowerCamelCase(  UpperCamelCase__    : str    ,       UpperCamelCase__    : str = giphy_api_key		)		->  list:
    A       :       Optional[Any]							=						'''+'''.join(query.split()		)
    A       :       List[Any]							=						F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
    A       :       int							=						requests.get(UpperCamelCase__		).json()['''data''']
    return [gif["url"] for gif in gifs]


if __name__ == "__main__":
     print("""\n""".join(get_gifs("""space ship""")))
