code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import is_detectrona_available, logging
  
  
  _lowerCamelCase				:	Any				    =       logging.get_logger(__name__)
  
  _lowerCamelCase				:	Tuple				    =       {
      'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
      'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
      # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
  }
  
  # soft dependency
  if is_detectrona_available():
  				import detectrona
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    '''layoutlmv2'''
  
  
  
  
  
  
  
  		def __init__(self						:      List[str]      ,      _lowerCAmelCase						:      str=3_0522      ,      _lowerCAmelCase						:      Union[str, Any]=768      ,      _lowerCAmelCase						:      List[Any]=12      ,      _lowerCAmelCase						:      Tuple=12      ,      _lowerCAmelCase						:      str=3072      ,      _lowerCAmelCase						:      Any="gelu"      ,      _lowerCAmelCase						:      Optional[Any]=0.1      ,      _lowerCAmelCase						:      Optional[int]=0.1      ,      _lowerCAmelCase						:      List[str]=512      ,      _lowerCAmelCase						:      Tuple=2      ,      _lowerCAmelCase						:      Union[str, Any]=0.02      ,      _lowerCAmelCase						:      Dict=1e-12      ,      _lowerCAmelCase						:      Dict=0      ,      _lowerCAmelCase						:      Tuple=1024      ,      _lowerCAmelCase						:      str=128      ,      _lowerCAmelCase						:      Tuple=32      ,      _lowerCAmelCase						:      Tuple=True      ,      _lowerCAmelCase						:      List[Any]=256      ,      _lowerCAmelCase						:      Optional[int]=64      ,      _lowerCAmelCase						:      Dict=True      ,      _lowerCAmelCase						:      List[Any]=[7, 7, 256]      ,      _lowerCAmelCase						:      int=128      ,      _lowerCAmelCase						:      Dict=128      ,      _lowerCAmelCase						:      Any=True      ,      _lowerCAmelCase						:      Union[str, Any]=True      ,      _lowerCAmelCase						:      str=False      ,      _lowerCAmelCase						:      Optional[Any]=None      ,      **_lowerCAmelCase						:      Optional[int]      ,      ):
  					super().__init__(
  					    vocab_size=_lowerCAmelCase      ,      hidden_size=_lowerCAmelCase      ,      num_hidden_layers=_lowerCAmelCase      ,      num_attention_heads=_lowerCAmelCase      ,      intermediate_size=_lowerCAmelCase      ,      hidden_act=_lowerCAmelCase      ,      hidden_dropout_prob=_lowerCAmelCase      ,      attention_probs_dropout_prob=_lowerCAmelCase      ,      max_position_embeddings=_lowerCAmelCase      ,      type_vocab_size=_lowerCAmelCase      ,      initializer_range=_lowerCAmelCase      ,      layer_norm_eps=_lowerCAmelCase      ,      pad_token_id=_lowerCAmelCase      ,      **_lowerCAmelCase      ,      )
  					A							  =      max_ad_position_embeddings
  					A							  =      max_rel_pos
  					A							  =      rel_pos_bins
  					A							  =      fast_qkv
  					A							  =      max_rel_ad_pos
  					A							  =      rel_ad_pos_bins
  					A							  =      convert_sync_batchnorm
  					A							  =      image_feature_pool_shape
  					A							  =      coordinate_size
  					A							  =      shape_size
  					A							  =      has_relative_attention_bias
  					A							  =      has_spatial_attention_bias
  					A							  =      has_visual_segment_embedding
  					A							  =      (
  					    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  					)
  
  
  
  
  
  
  
  		@classmethod
  		def        A     (self						:      List[str]      ):
  					return {
  					    "MODEL.MASK_ON": True,
  					    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
  					    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
  					    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
  					    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
  					    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
  					    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
  					    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
  					    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
  					    "MODEL.POST_NMS_TOPK_TEST": 1000,
  					    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
  					    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
  					    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
  					    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
  					    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
  					    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
  					    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
  					    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
  					    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
  					    "MODEL.RESNETS.DEPTH": 101,
  					    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
  					    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
  					    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
  					    "MODEL.RESNETS.NUM_GROUPS": 32,
  					    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
  					    "MODEL.RESNETS.STRIDE_IN_1X1": False,
  					}
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ):
  					A							  =      detectrona.config.get_cfg()
  					for k, v in self.detectrona_config_args.items():
  								A							  =      k.split("""."""      )
  								A							  =      detectrona_config
  								for attribute in attributes[:-1]:
  											A							  =      getattr(_lowerCAmelCase      ,      _lowerCAmelCase      )
  								setattr(_lowerCAmelCase      ,      attributes[-1]      ,      _lowerCAmelCase      )
  
  					return detectrona_config
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->List[Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      TaConfig.from_pretrained(UpperCAmelCase    )
  	A							  =      FlaxTaForConditionalGeneration(config=UpperCAmelCase    )
  	A							  =      checkpoints.load_tax_checkpoint(UpperCAmelCase    )
  
  	A							  =      """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
  
  	# Encoder
  	for layer_index in range(config.num_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
  
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_attention_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  		A							  =      tax_mlp_layer_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_encoder_rel_embedding
  
  	# Assigning
  	A							  =      tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  	A							  =      tax_encoder_norm
  
  	# Decoder
  	for layer_index in range(config.num_decoder_layers    ):
  		A							  =      f"""layers_{str(UpperCAmelCase    )}"""
  
  		# Self-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
  		    """scale"""
  		]
  
  		# Encoder-Decoder-Attention
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
  		    """kernel"""
  		]
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
  		    """kernel"""
  		]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
  
  		# MLP
  		if split_mlp_wi:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
  		else:
  			A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
  
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
  
  		# Layer Normalization
  		A							  =      tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
  
  		# Assigning
  		A							  =      tax_attention_key
  		A							  =      tax_attention_out
  		A							  =      tax_attention_query
  		A							  =      tax_attention_value
  
  		A							  =      tax_pre_attention_layer_norm
  
  		A							  =      tax_enc_dec_attention_key
  		A							  =      tax_enc_dec_attention_out
  		A							  =      tax_enc_dec_attention_query
  		A							  =      tax_enc_dec_attention_value
  
  		A							  =      tax_cross_layer_norm
  
  		if split_mlp_wi:
  			A							  =      tax_mlp_wi_a
  			A							  =      tax_mlp_wi_a
  		else:
  			A							  =      tax_mlp_wi
  
  		A							  =      tax_mlp_wo
  
  		A							  =      txa_mlp_layer_norm
  
  	# Decoder Normalization
  	A							  =      tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  	A							  =      txa_decoder_norm
  
  	# Only for layer 0:
  	A							  =      tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  	A							  =      tax_decoder_rel_embedding
  
  	# Token Embeddings
  	A							  =      tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  	A							  =      txa_token_embeddings
  
  	# LM Head (only in v1.1 checkpoints)
  	if "logits_dense" in tax_model["target"]["decoder"]:
  		A							  =      tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
  
  	flax_model.save_pretrained(UpperCAmelCase    )
  	print("""T5X Model was sucessfully converted!"""    )
  
  
  if __name__ == "__main__":
  				_lowerCamelCase				:	Tuple				    =       argparse.ArgumentParser()
  				# Required parameters
  				parser.add_argument(
  				    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
  				)
  				parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
  				parser.add_argument(
  				    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
  				)
  				_lowerCamelCase				:	Union[str, Any]				    =       parser.parse_args()
  				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
  
  