





'''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








'''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)

