

"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   TaConfig.from_pretrained(lowercase__ )
					A     =   FlaxTaForConditionalGeneration(config=lowercase__ )
					A     =   checkpoints.load_tax_checkpoint(lowercase__ )

					A     =   "wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

					# Encoder
					for layer_index in range(config.num_layers ):
										A     =   F"""layers_{str(lowercase__ )}"""

										# 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(lowercase__ )}"""

										# 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(lowercase__ )
					print("T5X Model was sucessfully converted!" )


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






'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A_	   = {
    "configuration_convnextv2": [
        "CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConvNextV2Config",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_	   = [
						    "CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "ConvNextV2ForImageClassification",
						    "ConvNextV2Model",
						    "ConvNextV2PreTrainedModel",
						    "ConvNextV2Backbone",
						]


if TYPE_CHECKING:
						from .configuration_convnextva import (
						    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ConvNextVaConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_convnextva import (
												    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConvNextVaBackbone,
												    ConvNextVaForImageClassification,
												    ConvNextVaModel,
												    ConvNextVaPreTrainedModel,
												)

else:
						import sys

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





