


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 			lowerCAmelCase (		lowerCAmelCase_ )-> List[str]:
				lowerCAmelCase_ :      str    				=				os.path.join(args.tf_model_dir ,   '''parameters.json''' )
				lowerCAmelCase_ :      Dict    				=				json.loads(open(snake_case__ ).read() )
				if not params:
								raise ValueError(
								    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""" )
				if not args.output.endswith('''.pt''' ):
								lowerCAmelCase_ :      List[Any]    				=				args.output + '''.pt'''
				lowerCAmelCase_ :      str    				=				OrderedDict()
				with tf.device('''/CPU:0''' ):
								lowerCAmelCase_ :      Dict    				=				tf.train.load_checkpoint(args.tf_model_dir )
								lowerCAmelCase_ :      Dict    				=				reader.get_variable_to_shape_map()
								for key_name in shapes.keys():
												lowerCAmelCase_ :      List[Any]    				=				reader.get_tensor(snake_case__ ).astype(np.floataa )
												if key_name.endswith('''/adam_m''' ) or key_name.endswith('''/adam_v''' ):
																continue
												if key_name.startswith('''pasts/''' ):
																if key_name.startswith('''pasts/mlp''' ):
																				lowerCAmelCase_ :      int    				=				int(key_name[9] )
																elif key_name.startswith('''pasts/out''' ):
																				lowerCAmelCase_ :      int    				=				8
																lowerCAmelCase_ :      Any    				=				'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																lowerCAmelCase_ :      int    				=				vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																lowerCAmelCase_ :      Optional[Any]    				=				torch.tensor(snake_case__ )
												elif key_name.startswith('''model/moe''' ):
																lowerCAmelCase_ :      Optional[Any]    				=				int(key_name[9:].split('''/''' )[0] )
																if key_name.endswith('''/switch_gating/kernel''' ):
																				lowerCAmelCase_ :      Any    				=				'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
																				lowerCAmelCase_ :      Union[str, Any]    				=				vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				lowerCAmelCase_ :      List[Any]    				=				torch.tensor(snake_case__ )
																elif key_name.endswith('''/softmlp/kernel''' ):
																				lowerCAmelCase_ :      Tuple    				=				'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
																				lowerCAmelCase_ :      Any    				=				vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				lowerCAmelCase_ :      Optional[Any]    				=				torch.tensor(snake_case__ )
																elif key_name.endswith('''/wo/kernel''' ) or key_name.endswith('''/wi/kernel''' ):
																				lowerCAmelCase_ :      Tuple    				=				key_name[-9:-7]
																				for i in range(16 ):
																								lowerCAmelCase_ :      Union[str, Any]    				=				'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
																								lowerCAmelCase_ :      Tuple    				=				(
																								    vnp[i].transpose([1, 0] ).copy()
																								)  # In Mesh-Tensorflow, it is one array, so it is divided
																								lowerCAmelCase_ :      Tuple    				=				torch.tensor(snake_case__ )
												elif key_name.startswith('''model/mlp''' ):
																lowerCAmelCase_ :      Dict    				=				int(key_name[9:].split('''/''' )[0] )
																if key_name.endswith('''/p1/kernel''' ):
																				lowerCAmelCase_ :      Any    				=				'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
																				lowerCAmelCase_ :      Optional[int]    				=				vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				lowerCAmelCase_ :      str    				=				torch.tensor(snake_case__ )
																elif key_name.endswith('''/p1/bias''' ):
																				lowerCAmelCase_ :      Dict    				=				'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
																				lowerCAmelCase_ :      str    				=				vnp.copy()  # same because it is one dimensional
																				lowerCAmelCase_ :      List[str]    				=				torch.tensor(snake_case__ )
																elif key_name.endswith('''/p2/kernel''' ):
																				lowerCAmelCase_ :      Any    				=				'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
																				lowerCAmelCase_ :      Dict    				=				vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																				lowerCAmelCase_ :      Any    				=				torch.tensor(snake_case__ )
																elif key_name.endswith('''/p2/bias''' ):
																				lowerCAmelCase_ :      Optional[Any]    				=				'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
																				lowerCAmelCase_ :      Any    				=				vnp.copy()  # same because it is one dimensional
																				lowerCAmelCase_ :      Optional[int]    				=				torch.tensor(snake_case__ )
												elif key_name.startswith('''model/ln''' ):
																lowerCAmelCase_ :      str    				=				int(key_name[8:].split('''/''' )[0] )
																if key_name.endswith('''/b''' ):
																				lowerCAmelCase_ :      str    				=				'''model.blocks.%d.feed_forward.norm.bias''' % player
																				lowerCAmelCase_ :      str    				=				vnp.copy()  # same because it is one dimensional
																				lowerCAmelCase_ :      Tuple    				=				torch.tensor(snake_case__ )
																elif key_name.endswith('''/g''' ):
																				lowerCAmelCase_ :      Tuple    				=				'''model.blocks.%d.feed_forward.norm.weight''' % player
																				lowerCAmelCase_ :      Union[str, Any]    				=				vnp.copy()  # same because it is one dimensional
																				lowerCAmelCase_ :      Optional[int]    				=				torch.tensor(snake_case__ )
												elif key_name.startswith('''model/att''' ):
																lowerCAmelCase_ :      Dict    				=				int(key_name[9:].split('''/''' )[0] )
																if key_name.endswith('''/qkv/kernel''' ):
																				lowerCAmelCase_ :      Optional[int]    				=				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																				lowerCAmelCase_ :      int    				=				state[:, 0, :, :]
																				lowerCAmelCase_ :      str    				=				state[:, 1, :, :]
																				lowerCAmelCase_ :      Tuple    				=				state[:, 2, :, :]
																				lowerCAmelCase_ :      Tuple    				=				(
																				    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				lowerCAmelCase_ :      Dict    				=				(
																				    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				lowerCAmelCase_ :      List[str]    				=				(
																				    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
																				    .transpose([1, 0] )
																				    .copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				lowerCAmelCase_ :      Optional[Any]    				=				'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
																				lowerCAmelCase_ :      Dict    				=				torch.tensor(snake_case__ )
																				lowerCAmelCase_ :      List[str]    				=				'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
																				lowerCAmelCase_ :      Any    				=				torch.tensor(snake_case__ )
																				lowerCAmelCase_ :      Optional[Any]    				=				'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
																				lowerCAmelCase_ :      Union[str, Any]    				=				torch.tensor(snake_case__ )
																elif key_name.endswith('''/o/kernel''' ):
																				lowerCAmelCase_ :      Union[str, Any]    				=				'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
																				lowerCAmelCase_ :      Tuple    				=				(
																				    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
																				)  # Mesh-Tensorflow is a diagonal matrix
																				lowerCAmelCase_ :      List[Any]    				=				torch.tensor(snake_case__ )
												elif key_name.startswith('''model/an''' ):
																lowerCAmelCase_ :      List[Any]    				=				int(key_name[8:].split('''/''' )[0] )
																if key_name.endswith('''/b''' ):
																				lowerCAmelCase_ :      Optional[int]    				=				'''model.blocks.%d.self_attn.norm.bias''' % player
																				lowerCAmelCase_ :      Dict    				=				vnp.copy()  # same because it is one dimensional
																				lowerCAmelCase_ :      Optional[int]    				=				torch.tensor(snake_case__ )
																elif key_name.endswith('''/g''' ):
																				lowerCAmelCase_ :      Optional[int]    				=				'''model.blocks.%d.self_attn.norm.weight''' % player
																				lowerCAmelCase_ :      Optional[int]    				=				vnp.copy()  # same because it is one dimensional
																				lowerCAmelCase_ :      Optional[int]    				=				torch.tensor(snake_case__ )
												elif (
												    key_name.startswith('''model/wte''' )
												    or key_name.startswith('''model/wpe''' )
												    or key_name.startswith('''model/ete''' )
												):
																lowerCAmelCase_ :      Optional[Any]    				=				{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
																    key_name[-3:]
																]
																lowerCAmelCase_ :      int    				=				'''model.%s.weight''' % nlayer
																lowerCAmelCase_ :      Optional[Any]    				=				vnp.copy()  # same in embedded
																lowerCAmelCase_ :      Optional[int]    				=				torch.tensor(snake_case__ )
																if key_name.startswith('''model/wte''' ):
																				lowerCAmelCase_ :      Dict    				=				'''lm_head.weight'''
																				lowerCAmelCase_ :      Any    				=				vnp.copy()  # same in embedded
																				lowerCAmelCase_ :      Optional[Any]    				=				torch.tensor(snake_case__ )
												elif key_name.startswith('''model/wob''' ):
																lowerCAmelCase_ :      Tuple    				=				'''final_logits_bias'''
																lowerCAmelCase_ :      str    				=				vnp.copy()  # same in embedded
																lowerCAmelCase_ :      List[str]    				=				state.reshape((1, -1) )
																lowerCAmelCase_ :      Dict    				=				torch.tensor(snake_case__ )
												elif key_name == "model/dense/kernel":
																lowerCAmelCase_ :      Any    				=				'''model.last_project.weight'''
																lowerCAmelCase_ :      Optional[int]    				=				vnp.transpose([1, 0] ).copy()  # Mesh-Tensorflow is a diagonal matrix
																lowerCAmelCase_ :      Optional[int]    				=				torch.tensor(snake_case__ )
												elif key_name == "model/dense_1/bias":
																lowerCAmelCase_ :      List[str]    				=				'''model.last_project.bias'''
																lowerCAmelCase_ :      int    				=				vnp.copy()  # same because it is one dimensional
																lowerCAmelCase_ :      Any    				=				torch.tensor(snake_case__ )
				torch.save(snake_case__ ,   args.output )


if __name__ == "__main__":
						_UpperCAmelCase			:  Union[str, Any]     =argparse.ArgumentParser(
						    description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
						)
						parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
						parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
						_UpperCAmelCase			:  Union[str, Any]     =parser.parse_args()
						convert_tf_gptsan_to_pt(args)



from typing import TYPE_CHECKING

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


_UpperCAmelCase			:  Union[str, Any]     ={
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

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

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Tuple     =[
						    """LlamaForCausalLM""",
						    """LlamaModel""",
						    """LlamaPreTrainedModel""",
						    """LlamaForSequenceClassification""",
						]


if TYPE_CHECKING:
						from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

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

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

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
						import sys

						_UpperCAmelCase			:  List[Any]     =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)