


"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def        lowercase							(		lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     List[str]    ,					lowerCAmelCase__ :     Any			) ->      Union[str, Any]:
		__a           =			AutoConfig.from_pretrained(lowerCAmelCase__			)
		__a           =			FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase__			)
		__a           =			checkpoints.load_tax_checkpoint(lowerCAmelCase__			)

		__a           =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

		if config.model_type == "t5":
				__a           =			'''SelfAttention'''
		if config.model_type == "longt5" and config.encoder_attention_type == "local":
				__a           =			'''LocalSelfAttention'''
		elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				__a           =			'''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			):
				__a           =			f'''layers_{str(lowerCAmelCase__			)}'''

				# 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''']

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

				# 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           =			flax_model.params['''encoder''']['''block'''][str(lowerCAmelCase__			)]['''layer''']
				__a           =			tax_attention_key
				__a           =			tax_attention_out
				__a           =			tax_attention_query
				__a           =			tax_attention_value

				__a           =			tax_attention_layer_norm

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__a           =			tax_global_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

				__a           =			flax_model_encoder_layer_block

		# Only for layer 0:
		__a           =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
		__a           =			tax_encoder_rel_embedding

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

		# Assigning
		__a           =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
		__a           =			tax_encoder_norm

		# Decoder
		for layer_index in range(config.num_layers			):
				__a           =			f'''layers_{str(lowerCAmelCase__			)}'''

				# 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''']
				__a           =			tax_enc_dec_attention_module['''key''']['''kernel''']
				__a           =			tax_enc_dec_attention_module['''out''']['''kernel''']
				__a           =			tax_enc_dec_attention_module['''query''']['''kernel''']
				__a           =			tax_enc_dec_attention_module['''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           =			flax_model.params['''decoder''']['''block'''][str(lowerCAmelCase__			)]['''layer''']
				__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

				__a           =			flax_model_decoder_layer_block

		# 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 and LongT5 checkpoints)
		if "logits_dense" in tax_model["target"]["decoder"]:
				__a           =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

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


if __name__ == "__main__":
				lowercase_			     =  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."
				)
				lowercase_			     =  parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)










"""simple docstring"""




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


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				'beit'

		def __init__(  self							, _a=8_192							, _a=768							, _a=12							, _a=12							, _a=3_072							, _a="gelu"							, _a=0.0							, _a=0.0							, _a=0.02							, _a=1E-12							, _a=224							, _a=16							, _a=3							, _a=False							, _a=False							, _a=False							, _a=False							, _a=0.1							, _a=0.1							, _a=True							, _a=[3, 5, 7, 11]							, _a=[1, 2, 3, 6]							, _a=True							, _a=0.4							, _a=256							, _a=1							, _a=False							, _a=255							, **_a							, ):
				super().__init__(**_a      )

				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			initializer_range
				__a           =			layer_norm_eps

				__a           =			image_size
				__a           =			patch_size
				__a           =			num_channels
				__a           =			use_mask_token
				__a           =			use_absolute_position_embeddings
				__a           =			use_relative_position_bias
				__a           =			use_shared_relative_position_bias
				__a           =			layer_scale_init_value
				__a           =			drop_path_rate
				__a           =			use_mean_pooling
				# decode head attributes (semantic segmentation)
				__a           =			out_indices
				__a           =			pool_scales
				# auxiliary head attributes (semantic segmentation)
				__a           =			use_auxiliary_head
				__a           =			auxiliary_loss_weight
				__a           =			auxiliary_channels
				__a           =			auxiliary_num_convs
				__a           =			auxiliary_concat_input
				__a           =			semantic_loss_ignore_index







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[Any]										=				version.parse('1.11'       )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )





		@property
		def 			__UpperCAmelCase       (  self      ):
				return 1E-4






