"""simple docstring"""
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def  lowerCAmelCase__     (      _UpperCamelCase							:					List[str]			,      _UpperCamelCase							:					Tuple			,      _UpperCamelCase							:					List[str]       )       ->					Dict:





						"""simple docstring"""






						snake_case             =      TaConfig.from_pretrained(_UpperCamelCase       )
						snake_case             =      FlaxTaForConditionalGeneration(config=_UpperCamelCase       )
						snake_case             =      checkpoints.load_tax_checkpoint(_UpperCamelCase       )

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

						# Encoder
						for layer_index in range(config.num_layers       ):
												snake_case             =      f"""layers_{str(_UpperCamelCase       )}"""

												# Self-Attention
												snake_case             =      tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
												snake_case             =      tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
												snake_case             =      tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
												snake_case             =      tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

												# Layer Normalization
												snake_case             =      tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

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

												snake_case             =      tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

												# Layer Normalization
												snake_case             =      tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

												# Assigning
												snake_case             =      tax_attention_key
												snake_case             =      tax_attention_out
												snake_case             =      tax_attention_query
												snake_case             =      tax_attention_value

												snake_case             =      tax_attention_layer_norm

												if split_mlp_wi:
																		snake_case             =      tax_mlp_wi_a
																		snake_case             =      tax_mlp_wi_a
												else:
																		snake_case             =      tax_mlp_wi

												snake_case             =      tax_mlp_wo
												snake_case             =      tax_mlp_layer_norm

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

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

						# Decoder
						for layer_index in range(config.num_decoder_layers       ):
												snake_case             =      f"""layers_{str(_UpperCamelCase       )}"""

												# Self-Attention
												snake_case             =      tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
												snake_case             =      tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
												snake_case             =      tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
												snake_case             =      tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

												# Layer Normalization
												snake_case             =      tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
												    'scale'
												]

												# Encoder-Decoder-Attention
												snake_case             =      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['key'][
												    'kernel'
												]
												snake_case             =      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
												    'kernel'
												]
												snake_case             =      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
												    'kernel'
												]
												snake_case             =      tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['value'][
												    'kernel'
												]

												# Layer Normalization
												snake_case             =      tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

												# MLP
												if split_mlp_wi:
																		snake_case             =      tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
																		snake_case             =      tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
												else:
																		snake_case             =      tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

												snake_case             =      tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

												# Layer Normalization
												snake_case             =      tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

												# Assigning
												snake_case             =      tax_attention_key
												snake_case             =      tax_attention_out
												snake_case             =      tax_attention_query
												snake_case             =      tax_attention_value

												snake_case             =      tax_pre_attention_layer_norm

												snake_case             =      tax_enc_dec_attention_key
												snake_case             =      tax_enc_dec_attention_out
												snake_case             =      tax_enc_dec_attention_query
												snake_case             =      tax_enc_dec_attention_value

												snake_case             =      tax_cross_layer_norm

												if split_mlp_wi:
																		snake_case             =      tax_mlp_wi_a
																		snake_case             =      tax_mlp_wi_a
												else:
																		snake_case             =      tax_mlp_wi

												snake_case             =      tax_mlp_wo

												snake_case             =      txa_mlp_layer_norm

						# Decoder Normalization
						snake_case             =      tax_model['target']['decoder']['decoder_norm']['scale']
						snake_case             =      txa_decoder_norm

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

						# Token Embeddings
						snake_case             =      tax_model['target']['token_embedder']['embedding']
						snake_case             =      txa_token_embeddings

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

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__       =	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."
					)
					SCREAMING_SNAKE_CASE__       =	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,
)


SCREAMING_SNAKE_CASE__       =	{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "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

					SCREAMING_SNAKE_CASE__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


