


from typing import TYPE_CHECKING

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


_A		       =						{
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['XLMRobertaTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['XLMRobertaTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLMRobertaForCausalLM',
       'XLMRobertaForMaskedLM',
       'XLMRobertaForMultipleChoice',
       'XLMRobertaForQuestionAnswering',
       'XLMRobertaForSequenceClassification',
       'XLMRobertaForTokenClassification',
       'XLMRobertaModel',
       'XLMRobertaPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLMRobertaForCausalLM',
       'TFXLMRobertaForMaskedLM',
       'TFXLMRobertaForMultipleChoice',
       'TFXLMRobertaForQuestionAnswering',
       'TFXLMRobertaForSequenceClassification',
       'TFXLMRobertaForTokenClassification',
       'TFXLMRobertaModel',
       'TFXLMRobertaPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaxXLMRobertaForMaskedLM',
       'FlaxXLMRobertaForCausalLM',
       'FlaxXLMRobertaForMultipleChoice',
       'FlaxXLMRobertaForQuestionAnswering',
       'FlaxXLMRobertaForSequenceClassification',
       'FlaxXLMRobertaForTokenClassification',
       'FlaxXLMRobertaModel',
       'FlaxXLMRobertaPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_xlm_roberta import (
       XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
       XLMRobertaConfig,
       XLMRobertaOnnxConfig,
   )

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlm_roberta import XLMRobertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm_roberta import (
          XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMRobertaForCausalLM,
          XLMRobertaForMaskedLM,
          XLMRobertaForMultipleChoice,
          XLMRobertaForQuestionAnswering,
          XLMRobertaForSequenceClassification,
          XLMRobertaForTokenClassification,
          XLMRobertaModel,
          XLMRobertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm_roberta import (
          TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMRobertaForCausalLM,
          TFXLMRobertaForMaskedLM,
          TFXLMRobertaForMultipleChoice,
          TFXLMRobertaForQuestionAnswering,
          TFXLMRobertaForSequenceClassification,
          TFXLMRobertaForTokenClassification,
          TFXLMRobertaModel,
          TFXLMRobertaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_xlm_roberta import (
          FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxXLMRobertaForCausalLM,
          FlaxXLMRobertaForMaskedLM,
          FlaxXLMRobertaForMultipleChoice,
          FlaxXLMRobertaForQuestionAnswering,
          FlaxXLMRobertaForSequenceClassification,
          FlaxXLMRobertaForTokenClassification,
          FlaxXLMRobertaModel,
          FlaxXLMRobertaPreTrainedModel,
      )

else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "yolos"






    def __init__(			self					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=1E-12					,   A_=[512, 864]					,   A_=16					,   A_=3					,   A_=True					,   A_=100					,   A_=True					,   A_=False					,   A_=1					,   A_=5					,   A_=2					,   A_=5					,   A_=2					,   A_=0.1					,   **A_					,   )  ->       Any:
         super().__init__(**A_    )

         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=qkv_bias
         __UpperCamelCase    							=num_detection_tokens
         __UpperCamelCase    							=use_mid_position_embeddings
         __UpperCamelCase    							=auxiliary_loss
         # Hungarian matcher
         __UpperCamelCase    							=class_cost
         __UpperCamelCase    							=bbox_cost
         __UpperCamelCase    							=giou_cost
         # Loss coefficients
         __UpperCamelCase    							=bbox_loss_coefficient
         __UpperCamelCase    							=giou_loss_coefficient
         __UpperCamelCase    							=eos_coefficient






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str      =  version.parse("1.11"		)






    @property
    def _a					(			self    )  ->       Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]    )






    @property
    def _a					(			self    )  ->       float:
         return 1E-4






    @property
    def _a					(			self    )  ->       int:
         return 12





