
"""simple docstring"""
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Any:


					'''simple docstring'''
					__a:						Optional[int]		    =					TaConfig.from_pretrained(lowerCamelCase_ )
					__a:						List[str]		    =					FlaxTaForConditionalGeneration(config=lowerCamelCase_ )
					__a:						Tuple		    =					checkpoints.load_tax_checkpoint(lowerCamelCase_ )

					__a:						Union[str, Any]		    =					"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

					# Encoder
					for layer_index in range(config.num_layers ):
										__a:						Optional[Any]		    =					f"""layers_{str(lowerCamelCase_ )}"""

										# Self-Attention
										__a:						Union[str, Any]		    =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
										__a:						Union[str, Any]		    =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
										__a:						str		    =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
										__a:						Any		    =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

										# Layer Normalization
										__a:						List[str]		    =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

										if split_mlp_wi:
															__a:						Dict		    =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
															__a:						str		    =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
										else:
															__a:						Any		    =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

										__a:						Union[str, Any]		    =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

										# Layer Normalization
										__a:						int		    =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

										# Assigning
										__a:						Optional[Any]		    =					tax_attention_key
										__a:						Union[str, Any]		    =					tax_attention_out
										__a:						Any		    =					tax_attention_query
										__a:						Optional[int]		    =					tax_attention_value

										__a:						Tuple		    =					tax_attention_layer_norm

										if split_mlp_wi:
															__a:						Union[str, Any]		    =					tax_mlp_wi_a
															__a:						Union[str, Any]		    =					tax_mlp_wi_a
										else:
															__a:						List[Any]		    =					tax_mlp_wi

										__a:						Dict		    =					tax_mlp_wo
										__a:						int		    =					tax_mlp_layer_norm

					# Only for layer 0:
					__a:						Any		    =					tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
					__a:						List[Any]		    =					tax_encoder_rel_embedding

					# Assigning
					__a:						Union[str, Any]		    =					tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
					__a:						Optional[int]		    =					tax_encoder_norm

					# Decoder
					for layer_index in range(config.num_decoder_layers ):
										__a:						Tuple		    =					f"""layers_{str(lowerCamelCase_ )}"""

										# Self-Attention
										__a:						Dict		    =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
										__a:						Optional[Any]		    =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
										__a:						List[Any]		    =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
										__a:						Dict		    =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

										# Layer Normalization
										__a:						List[str]		    =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
										    """scale"""
										]

										# Encoder-Decoder-Attention
										__a:						int		    =					tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
										    """kernel"""
										]
										__a:						Dict		    =					tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
										    """kernel"""
										]
										__a:						Dict		    =					tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
										    """kernel"""
										]
										__a:						Optional[Any]		    =					tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
										    """kernel"""
										]

										# Layer Normalization
										__a:						Union[str, Any]		    =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

										# MLP
										if split_mlp_wi:
															__a:						Any		    =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
															__a:						Any		    =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
										else:
															__a:						Tuple		    =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

										__a:						Dict		    =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

										# Layer Normalization
										__a:						Any		    =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

										# Assigning
										__a:						List[str]		    =					tax_attention_key
										__a:						List[Any]		    =					tax_attention_out
										__a:						List[str]		    =					tax_attention_query
										__a:						List[str]		    =					tax_attention_value

										__a:						Dict		    =					tax_pre_attention_layer_norm

										__a:						Optional[int]		    =					tax_enc_dec_attention_key
										__a:						List[str]		    =					tax_enc_dec_attention_out
										__a:						Optional[int]		    =					tax_enc_dec_attention_query
										__a:						Dict		    =					tax_enc_dec_attention_value

										__a:						List[str]		    =					tax_cross_layer_norm

										if split_mlp_wi:
															__a:						Optional[Any]		    =					tax_mlp_wi_a
															__a:						Union[str, Any]		    =					tax_mlp_wi_a
										else:
															__a:						int		    =					tax_mlp_wi

										__a:						List[str]		    =					tax_mlp_wo

										__a:						str		    =					txa_mlp_layer_norm

					# Decoder Normalization
					__a:						Union[str, Any]		    =					tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
					__a:						str		    =					txa_decoder_norm

					# Only for layer 0:
					__a:						int		    =					tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
					__a:						Tuple		    =					tax_decoder_rel_embedding

					# Token Embeddings
					__a:						List[str]		    =					tax_model["""target"""]["""token_embedder"""]["""embedding"""]
					__a:						Tuple		    =					txa_token_embeddings

					# LM Head (only in v1.1 checkpoints)
					if "logits_dense" in tax_model["target"]["decoder"]:
										__a:						Any		    =					tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

					flax_model.save_pretrained(lowerCamelCase_ )
					print("""T5X Model was sucessfully converted!""" )


if __name__ == "__main__":
			lowercase		:						Dict =					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.'
			)
			lowercase		:						List[str] =					parser.parse_args()
			convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase		:						List[str] =					{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Tuple =					['LlamaTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Dict =					['LlamaTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						List[Any] =					[
			    'OpenLlamaForCausalLM',
			    'OpenLlamaModel',
			    'OpenLlamaPreTrainedModel',
			    'OpenLlamaForSequenceClassification',
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowercase		:						Dict =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
