




"""simple docstring"""



from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_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_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


SCREAMING_SNAKE_CASE__:List[Any]			=	"""pytorch_model.bin"""
SCREAMING_SNAKE_CASE__:int			=	"""pytorch_model.bin.index.json"""
SCREAMING_SNAKE_CASE__:Optional[int]			=	"""adapter_config.json"""
SCREAMING_SNAKE_CASE__:int			=	"""adapter_model.bin"""
SCREAMING_SNAKE_CASE__:Dict			=	"""adapter_model.safetensors"""
SCREAMING_SNAKE_CASE__:Tuple			=	"""tf_model.h5"""
SCREAMING_SNAKE_CASE__:int			=	"""tf_model.h5.index.json"""
SCREAMING_SNAKE_CASE__:Tuple			=	"""model.ckpt"""
SCREAMING_SNAKE_CASE__:Union[str, Any]			=	"""flax_model.msgpack"""
SCREAMING_SNAKE_CASE__:Union[str, Any]			=	"""flax_model.msgpack.index.json"""
SCREAMING_SNAKE_CASE__:List[str]			=	"""model.safetensors"""
SCREAMING_SNAKE_CASE__:Dict			=	"""model.safetensors.index.json"""
SCREAMING_SNAKE_CASE__:List[Any]			=	"""config.json"""
SCREAMING_SNAKE_CASE__:Union[str, Any]			=	"""preprocessor_config.json"""
SCREAMING_SNAKE_CASE__:Dict			=	FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE__:Tuple			=	"""generation_config.json"""
SCREAMING_SNAKE_CASE__:List[str]			=	"""modelcard.json"""

SCREAMING_SNAKE_CASE__:str			=	"""▁"""
SCREAMING_SNAKE_CASE__:str			=	SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

SCREAMING_SNAKE_CASE__:List[Any]			=	[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE__:Dict			=	[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE__:Optional[int]			=	[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]



def 					_lowerCamelCase(					a							):
       if version.parse(a							) < version.parse(a							):
              if "dev" in min_version:
                     __a     				=  (
                         "This example requires a source install from HuggingFace Transformers (see "
                         "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
                     )
              else:
                     __a     				=  F"This example requires a minimum version of {min_version},"
              error_message += F" but the version found is {__version__}.\n"
              raise ImportError(
                  error_message
                  + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
                  "versions of HuggingFace Transformers."							)

"""simple docstring"""



import functools
import operator

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


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

SCREAMING_SNAKE_CASE__:str			=	{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			str           =		"""sew-d"""


 def __init__(				self  ,						lowerCamelCase=32  ,						lowerCamelCase=768  ,						lowerCamelCase=12  ,						lowerCamelCase=12  ,						lowerCamelCase=3072  ,						lowerCamelCase=2  ,						lowerCamelCase=512  ,						lowerCamelCase=256  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=("p2c", "c2p")  ,						lowerCamelCase="layer_norm"  ,						lowerCamelCase="gelu_python"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.02  ,						lowerCamelCase=1E-7  ,						lowerCamelCase=1E-5  ,						lowerCamelCase="group"  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,						lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,						lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,						lowerCamelCase=False  ,						lowerCamelCase=128  ,						lowerCamelCase=16  ,						lowerCamelCase=True  ,						lowerCamelCase=0.05  ,						lowerCamelCase=10  ,						lowerCamelCase=2  ,						lowerCamelCase=0.0  ,						lowerCamelCase=10  ,						lowerCamelCase=0  ,						lowerCamelCase="mean"  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=256  ,						lowerCamelCase=0  ,						lowerCamelCase=1  ,						lowerCamelCase=2  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase  ,						pad_token_id=lowerCamelCase  ,						bos_token_id=lowerCamelCase  ,						eos_token_id=lowerCamelCase		)
        __a     				=  hidden_size
        __a     				=  feat_extract_norm
        __a     				=  feat_extract_activation
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  list(lowerCamelCase		)
        __a     				=  conv_bias
        __a     				=  num_conv_pos_embeddings
        __a     				=  num_conv_pos_embedding_groups
        __a     				=  len(self.conv_dim		)
        __a     				=  num_hidden_layers
        __a     				=  intermediate_size
        __a     				=  squeeze_factor
        __a     				=  max_position_embeddings
        __a     				=  position_buckets
        __a     				=  share_att_key
        __a     				=  relative_attention
        __a     				=  norm_rel_ebd
        __a     				=  list(lowerCamelCase		)
        __a     				=  hidden_act
        __a     				=  num_attention_heads
        __a     				=  hidden_dropout
        __a     				=  attention_dropout
        __a     				=  activation_dropout
        __a     				=  feat_proj_dropout
        __a     				=  final_dropout
        __a     				=  layer_norm_eps
        __a     				=  feature_layer_norm_eps
        __a     				=  initializer_range
        __a     				=  vocab_size

        if (
            (len(self.conv_stride		) != self.num_feat_extract_layers)
            or (len(self.conv_kernel		) != self.num_feat_extract_layers)
            or (len(self.conv_dim		) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."		)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a     				=  apply_spec_augment
        __a     				=  mask_time_prob
        __a     				=  mask_time_length
        __a     				=  mask_time_min_masks
        __a     				=  mask_feature_prob
        __a     				=  mask_feature_length
        __a     				=  mask_feature_min_masks

        # ctc loss
        __a     				=  ctc_loss_reduction
        __a     				=  ctc_zero_infinity

        # sequence classification
        __a     				=  use_weighted_layer_sum
        __a     				=  classifier_proj_size






 @property
 def 		a__ (				self		):
        return functools.reduce(operator.mul  ,						self.conv_stride  ,						1		)
