import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def    A			(					_lowercase	,						_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					str           =							AutoConfig.from_pretrained(_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							FlaxAutoModelForSeqaSeqLM.from_config(config=_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							checkpoints.load_tax_checkpoint(_lowercase		)

						SCREAMING_SNAKE_CASE							:					Tuple           =							'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

						if config.model_type == "t5":
												SCREAMING_SNAKE_CASE							:					int           =							'''SelfAttention'''
						if config.model_type == "longt5" and config.encoder_attention_type == "local":
												SCREAMING_SNAKE_CASE							:					str           =							'''LocalSelfAttention'''
						elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												SCREAMING_SNAKE_CASE							:					List[str]           =							'''TransientGlobalSelfAttention'''
						else:
												raise ValueError(
												    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
												    ''' attribute with a value from [\'local\', \'transient-global].'''		)

						# Encoder
						for layer_index in range(config.num_layers		):
												SCREAMING_SNAKE_CASE							:					List[str]           =							f"""layers_{str(_lowercase		)}"""

												# Self-Attention
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
												SCREAMING_SNAKE_CASE							:					str           =							tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
												SCREAMING_SNAKE_CASE							:					List[str]           =							tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

												# Global input layer norm
												if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																		SCREAMING_SNAKE_CASE							:					List[str]           =							tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

												# Layer Normalization
												SCREAMING_SNAKE_CASE							:					List[str]           =							tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

												if split_mlp_wi:
																		SCREAMING_SNAKE_CASE							:					int           =							tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		SCREAMING_SNAKE_CASE							:					int           =							tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												SCREAMING_SNAKE_CASE							:					Tuple           =							tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												SCREAMING_SNAKE_CASE							:					Any           =							tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												SCREAMING_SNAKE_CASE							:					List[str]           =							flax_model.params['''encoder''']['''block'''][str(_lowercase		)]['''layer''']
												SCREAMING_SNAKE_CASE							:					Dict           =							tax_attention_key
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_attention_out
												SCREAMING_SNAKE_CASE							:					List[str]           =							tax_attention_query
												SCREAMING_SNAKE_CASE							:					List[str]           =							tax_attention_value

												SCREAMING_SNAKE_CASE							:					Any           =							tax_attention_layer_norm

												# Global input layer norm
												if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
																		SCREAMING_SNAKE_CASE							:					Dict           =							tax_global_layer_norm

												if split_mlp_wi:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							tax_mlp_wi_a
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tax_mlp_wi_a
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tax_mlp_wi

												SCREAMING_SNAKE_CASE							:					Tuple           =							tax_mlp_wo
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tax_mlp_layer_norm

												SCREAMING_SNAKE_CASE							:					str           =							flax_model_encoder_layer_block

						# Only for layer 0:
						SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
						SCREAMING_SNAKE_CASE							:					str           =							tax_encoder_rel_embedding

						# Side/global relative position_bias + layer norm
						if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												SCREAMING_SNAKE_CASE							:					int           =							tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
												SCREAMING_SNAKE_CASE							:					int           =							tax_encoder_global_rel_embedding

						# Assigning
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
						SCREAMING_SNAKE_CASE							:					int           =							tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_layers		):
												SCREAMING_SNAKE_CASE							:					List[Any]           =							f"""layers_{str(_lowercase		)}"""

												# Self-Attention
												SCREAMING_SNAKE_CASE							:					List[str]           =							tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
												SCREAMING_SNAKE_CASE							:					Any           =							tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
												SCREAMING_SNAKE_CASE							:					Dict           =							tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

												# Layer Normalization
												SCREAMING_SNAKE_CASE							:					Dict           =							tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
												    '''scale'''
												]

												# Encoder-Decoder-Attention
												SCREAMING_SNAKE_CASE							:					str           =							tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
												SCREAMING_SNAKE_CASE							:					str           =							tax_enc_dec_attention_module['''key''']['''kernel''']
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_enc_dec_attention_module['''out''']['''kernel''']
												SCREAMING_SNAKE_CASE							:					int           =							tax_enc_dec_attention_module['''query''']['''kernel''']
												SCREAMING_SNAKE_CASE							:					List[str]           =							tax_enc_dec_attention_module['''value''']['''kernel''']

												# Layer Normalization
												SCREAMING_SNAKE_CASE							:					Tuple           =							tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

												# MLP
												if split_mlp_wi:
																		SCREAMING_SNAKE_CASE							:					Any           =							tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		SCREAMING_SNAKE_CASE							:					str           =							tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												SCREAMING_SNAKE_CASE							:					Dict           =							tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												SCREAMING_SNAKE_CASE							:					Any           =							flax_model.params['''decoder''']['''block'''][str(_lowercase		)]['''layer''']
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tax_attention_key
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tax_attention_out
												SCREAMING_SNAKE_CASE							:					Dict           =							tax_attention_query
												SCREAMING_SNAKE_CASE							:					Any           =							tax_attention_value

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							tax_pre_attention_layer_norm

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tax_enc_dec_attention_key
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_enc_dec_attention_out
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_enc_dec_attention_query
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tax_enc_dec_attention_value

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tax_cross_layer_norm

												if split_mlp_wi:
																		SCREAMING_SNAKE_CASE							:					Any           =							tax_mlp_wi_a
																		SCREAMING_SNAKE_CASE							:					Dict           =							tax_mlp_wi_a
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							tax_mlp_wi

												SCREAMING_SNAKE_CASE							:					int           =							tax_mlp_wo

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							txa_mlp_layer_norm

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							flax_model_decoder_layer_block

						# Decoder Normalization
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							txa_decoder_norm

						# Only for layer 0:
						SCREAMING_SNAKE_CASE							:					Tuple           =							tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
						SCREAMING_SNAKE_CASE							:					Any           =							tax_decoder_rel_embedding

						# Token Embeddings
						SCREAMING_SNAKE_CASE							:					Any           =							tax_model['''target''']['''token_embedder''']['''embedding''']
						SCREAMING_SNAKE_CASE							:					List[str]           =							txa_token_embeddings

						# LM Head (only in v1.1 and LongT5 checkpoints)
						if "logits_dense" in tax_model["target"]["decoder"]:
												SCREAMING_SNAKE_CASE							:					int           =							tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

						flax_model.save_pretrained(_lowercase		)
						print('''T5X Model was sucessfully converted!'''		)


if __name__ == "__main__":
					__UpperCamelCase	: Optional[int]    		=  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
					)
					parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
					parser.add_argument(
					    '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
					)
					__UpperCamelCase	: List[str]    		=  parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






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


__UpperCamelCase	: str    		=  logging.get_logger(__name__)

__UpperCamelCase	: str    		=  {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""layoutlmv3"""


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]=5_0265					,	UpperCamelCase__			:       List[Any]=768					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Tuple=3072					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       List[Any]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Any=512					,	UpperCamelCase__			:       List[Any]=2					,	UpperCamelCase__			:       List[Any]=0.02					,	UpperCamelCase__			:       Optional[Any]=1E-5					,	UpperCamelCase__			:       List[str]=1					,	UpperCamelCase__			:       int=0					,	UpperCamelCase__			:       str=2					,	UpperCamelCase__			:       List[str]=1024					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       Tuple=True					,	UpperCamelCase__			:       Optional[int]=32					,	UpperCamelCase__			:       Any=128					,	UpperCamelCase__			:       Optional[Any]=64					,	UpperCamelCase__			:       Dict=256					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       Dict=True					,	UpperCamelCase__			:       Dict=224					,	UpperCamelCase__			:       Optional[Any]=3					,	UpperCamelCase__			:       Any=16					,	UpperCamelCase__			:       Any=None					,	**UpperCamelCase__			:       Optional[Any]					,	):





												'''simple docstring'''





												super().__init__(
												    vocab_size=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__					,	max_position_embeddings=UpperCamelCase__					,	type_vocab_size=UpperCamelCase__					,	initializer_range=UpperCamelCase__					,	layer_norm_eps=UpperCamelCase__					,	pad_token_id=UpperCamelCase__					,	bos_token_id=UpperCamelCase__					,	eos_token_id=UpperCamelCase__					,	**UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_ad_position_embeddings
												SCREAMING_SNAKE_CASE							:					List[Any]           =							coordinate_size
												SCREAMING_SNAKE_CASE							:					List[str]           =							shape_size
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							has_relative_attention_bias
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rel_pos_bins
												SCREAMING_SNAKE_CASE							:					str           =							max_rel_pos
												SCREAMING_SNAKE_CASE							:					Any           =							has_spatial_attention_bias
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rel_ad_pos_bins
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_rel_ad_pos
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							text_embed
												SCREAMING_SNAKE_CASE							:					List[str]           =							visual_embed
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_size
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							num_channels
												SCREAMING_SNAKE_CASE							:					List[Any]           =							patch_size
												SCREAMING_SNAKE_CASE							:					List[Any]           =							classifier_dropout






class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	version.parse("""1.12""")


						@property
						def  __A ( self			:       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  __A ( self			:       int		):





												'''simple docstring'''





												return 1E-5


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       "ProcessorMixin"					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	):





												'''simple docstring'''





												setattr(processor.image_processor					,	'''apply_ocr'''					,	UpperCamelCase__		)

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												SCREAMING_SNAKE_CASE							:					Any           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	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
												SCREAMING_SNAKE_CASE							:					List[Any]           =							processor.tokenizer.num_special_tokens_to_add(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	fixed_dimension=OnnxConfig.default_fixed_sequence					,	num_token_to_add=UpperCamelCase__		)
												# Generate dummy inputs according to compute batch and sequence
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[[''' '''.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

												# Generate dummy bounding boxes
												SCREAMING_SNAKE_CASE							:					Any           =							[[[48, 84, 73, 128]]] * 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)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self._generate_dummy_images(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict(
												    processor(
												        UpperCamelCase__					,	text=UpperCamelCase__					,	boxes=UpperCamelCase__					,	return_tensors=UpperCamelCase__					,	)		)

												return inputs





