

"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase       :				Optional[int]         =					{
    'configuration_gpt2': ['GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT2Config', 'GPT2OnnxConfig'],
    'tokenization_gpt2': ['GPT2Tokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase       :				List[str]         =					['GPT2TokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase       :				Dict         =					[
        'GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GPT2DoubleHeadsModel',
        'GPT2ForQuestionAnswering',
        'GPT2ForSequenceClassification',
        'GPT2ForTokenClassification',
        'GPT2LMHeadModel',
        'GPT2Model',
        'GPT2PreTrainedModel',
        'load_tf_weights_in_gpt2',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase       :				Dict         =					[
        'TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFGPT2DoubleHeadsModel',
        'TFGPT2ForSequenceClassification',
        'TFGPT2LMHeadModel',
        'TFGPT2MainLayer',
        'TFGPT2Model',
        'TFGPT2PreTrainedModel',
    ]

try:
    if not is_keras_nlp_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase       :				Union[str, Any]         =					['TFGPT2Tokenizer']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase       :				Dict         =					['FlaxGPT2LMHeadModel', 'FlaxGPT2Model', 'FlaxGPT2PreTrainedModel']

if TYPE_CHECKING:
    from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
    from .tokenization_gpta import GPTaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_fast import GPTaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpta import (
            GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTaDoubleHeadsModel,
            GPTaForQuestionAnswering,
            GPTaForSequenceClassification,
            GPTaForTokenClassification,
            GPTaLMHeadModel,
            GPTaModel,
            GPTaPreTrainedModel,
            load_tf_weights_in_gpta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_gpta import (
            TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFGPTaDoubleHeadsModel,
            TFGPTaForSequenceClassification,
            TFGPTaLMHeadModel,
            TFGPTaMainLayer,
            TFGPTaModel,
            TFGPTaPreTrainedModel,
        )

    try:
        if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_tf import TFGPTaTokenizer

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
    import sys

    lowerCAmelCase       :				Dict         =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









from collections import defaultdict

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					defaultdict[str, int]          =       defaultdict(a					)
      # Creating a dictionary containing count of each word
      for word in sentence.split():
            occurrence[word] += 1
      return occurrence


if __name__ == "__main__":
    for word, count in word_occurrence('INPUT STRING').items():
        print(F'{word}: {count}')






