





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase       :   Optional[Any]				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   Any				       =							{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """trocr"""
       lowerCAmelCase__      		=     ["""past_key_values"""]
       lowerCAmelCase__      		=     {
           """num_attention_heads""": """decoder_attention_heads""",
           """hidden_size""": """d_model""",
           """num_hidden_layers""": """decoder_layers""",
       }




       def __init__(  self :    Dict							,				A :    List[str]=50_265							,				A :    str=1_024							,				A :    int=12							,				A :    str=16							,				A :    Optional[Any]=4_096							,				A :    Union[str, Any]="gelu"							,				A :    List[Any]=512							,				A :    int=0.1							,				A :    List[str]=0.0							,				A :    Union[str, Any]=0.0							,				A :    Union[str, Any]=2							,				A :    Any=0.02							,				A :    List[Any]=0.0							,				A :    List[Any]=True							,				A :    Optional[int]=False							,				A :    Union[str, Any]=True							,				A :    List[str]=True							,				A :    List[Any]=1							,				A :    List[Any]=0							,				A :    Any=2							,				**A :    Optional[int]							,				):
         __snake_case:						Any   =						vocab_size
         __snake_case:						Tuple   =						d_model
         __snake_case:						Dict   =						decoder_layers
         __snake_case:						str   =						decoder_attention_heads
         __snake_case:						str   =						decoder_ffn_dim
         __snake_case:						int   =						activation_function
         __snake_case:						str   =						max_position_embeddings
         __snake_case:						Optional[Any]   =						dropout
         __snake_case:						Dict   =						attention_dropout
         __snake_case:						Tuple   =						activation_dropout
         __snake_case:						int   =						init_std
         __snake_case:						Union[str, Any]   =						decoder_layerdrop
         __snake_case:						str   =						use_cache
         __snake_case:						Optional[Any]   =						scale_embedding
         __snake_case:						Any   =						use_learned_position_embeddings
         __snake_case:						Union[str, Any]   =						layernorm_embedding

         super().__init__(
             pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				decoder_start_token_id=A							,				**A							,				)













from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






