
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
   from ...processing_utils import ProcessorMixin
   from ...utils import TensorType


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}






class   UpperCAmelCase		(						A_ ):
   A__					:		List[str]						     =     "layoutlmv3"



   def __init__(self		:					Any    ,						snake_case__		:					Any=5_02_65    ,						snake_case__		:					List[str]=7_68    ,						snake_case__		:					int=12    ,						snake_case__		:					Union[str, Any]=12    ,						snake_case__		:					str=30_72    ,						snake_case__		:					Dict="gelu"    ,						snake_case__		:					List[str]=0.1    ,						snake_case__		:					Optional[Any]=0.1    ,						snake_case__		:					List[Any]=5_12    ,						snake_case__		:					Union[str, Any]=2    ,						snake_case__		:					int=0.02    ,						snake_case__		:					str=1e-5    ,						snake_case__		:					Optional[Any]=1    ,						snake_case__		:					str=0    ,						snake_case__		:					str=2    ,						snake_case__		:					Tuple=10_24    ,						snake_case__		:					Optional[int]=1_28    ,						snake_case__		:					Optional[Any]=1_28    ,						snake_case__		:					Optional[Any]=True    ,						snake_case__		:					Union[str, Any]=32    ,						snake_case__		:					Tuple=1_28    ,						snake_case__		:					int=64    ,						snake_case__		:					str=2_56    ,						snake_case__		:					List[str]=True    ,						snake_case__		:					Tuple=True    ,						snake_case__		:					Union[str, Any]=True    ,						snake_case__		:					List[Any]=2_24    ,						snake_case__		:					Optional[int]=3    ,						snake_case__		:					int=16    ,						snake_case__		:					Optional[int]=None    ,						**snake_case__		:					Dict    ,						)		->	Union[str, Any]:


          '''simple docstring'''


          super().__init__(
              vocab_size=snake_case__    ,						hidden_size=snake_case__    ,						num_hidden_layers=snake_case__    ,						num_attention_heads=snake_case__    ,						intermediate_size=snake_case__    ,						hidden_act=snake_case__    ,						hidden_dropout_prob=snake_case__    ,						attention_probs_dropout_prob=snake_case__    ,						max_position_embeddings=snake_case__    ,						type_vocab_size=snake_case__    ,						initializer_range=snake_case__    ,						layer_norm_eps=snake_case__    ,						pad_token_id=snake_case__    ,						bos_token_id=snake_case__    ,						eos_token_id=snake_case__    ,						**snake_case__    ,						)
          snake_case       :   Optional[Any]							=      max_ad_position_embeddings
          snake_case       :   Dict							=      coordinate_size
          snake_case       :   List[Any]							=      shape_size
          snake_case       :   str							=      has_relative_attention_bias
          snake_case       :   Dict							=      rel_pos_bins
          snake_case       :   int							=      max_rel_pos
          snake_case       :   str							=      has_spatial_attention_bias
          snake_case       :   List[Any]							=      rel_ad_pos_bins
          snake_case       :   List[Any]							=      max_rel_ad_pos
          snake_case       :   Optional[Any]							=      text_embed
          snake_case       :   Optional[Any]							=      visual_embed
          snake_case       :   List[str]							=      input_size
          snake_case       :   List[str]							=      num_channels
          snake_case       :   Dict							=      patch_size
          snake_case       :   Any							=      classifier_dropout






class   UpperCAmelCase		(						A_ ):
   A__					:		List[Any]						     =     version.parse("1.12" )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          if self.task in ["question-answering", "sequence-classification"]:
                 return OrderedDict(
                     [
                         ("input_ids", {0: "batch", 1: "sequence"}),
                         ("attention_mask", {0: "batch", 1: "sequence"}),
                         ("bbox", {0: "batch", 1: "sequence"}),
                         ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                     ] )
          else:
                 return OrderedDict(
                     [
                         ("input_ids", {0: "batch", 1: "sequence"}),
                         ("bbox", {0: "batch", 1: "sequence"}),
                         ("attention_mask", {0: "batch", 1: "sequence"}),
                         ("pixel_values", {0: "batch", 1: "num_channels"}),
                     ] )



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	float:


          '''simple docstring'''


          return 1e-5



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	int:


          '''simple docstring'''


          return 12



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					"ProcessorMixin"    ,						snake_case__		:					int = -1    ,						snake_case__		:					int = -1    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional["TensorType"] = None    ,						snake_case__		:					int = 3    ,						snake_case__		:					int = 40    ,						snake_case__		:					int = 40    ,						)		->	Mapping[str, Any]:


          '''simple docstring'''


          setattr(processor.image_processor    ,						"apply_ocr"    ,						snake_case__ )

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          snake_case       :   Optional[Any]							=      compute_effective_axis_dimension(
              snake_case__    ,						fixed_dimension=OnnxConfig.default_fixed_batch    ,						num_token_to_add=0 )
          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          snake_case       :   Optional[Any]							=      processor.tokenizer.num_special_tokens_to_add(snake_case__ )
          snake_case       :   Any							=      compute_effective_axis_dimension(
              snake_case__    ,						fixed_dimension=OnnxConfig.default_fixed_sequence    ,						num_token_to_add=snake_case__ )
          # Generate dummy inputs according to compute batch and sequence
          snake_case       :   int							=      [[" ".join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size

          # Generate dummy bounding boxes
          snake_case       :   Optional[int]							=      [[[48, 84, 73, 1_28]]] * batch_size

          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
          snake_case       :   Union[str, Any]							=      self._generate_dummy_images(snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ )

          snake_case       :   List[Any]							=      dict(
              processor(
                  snake_case__    ,						text=snake_case__    ,						boxes=snake_case__    ,						return_tensors=snake_case__    ,						) )

          return inputs







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,
)


__lowerCamelCase							=       {"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""XGLMTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""XGLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XGLMForCausalLM""",
       """XGLMModel""",
       """XGLMPreTrainedModel""",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """FlaxXGLMForCausalLM""",
       """FlaxXGLMModel""",
       """FlaxXGLMPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXGLMForCausalLM""",
       """TFXGLMModel""",
       """TFXGLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm import XGLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm_fast import XGLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xglm import (
          TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXGLMForCausalLM,
          TFXGLMModel,
          TFXGLMPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure)





