
"""simple docstring"""






import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 						_snake_case       (	lowercase__		:  List[Any]		)  ->				str:


			'''simple docstring'''





			lowerCAmelCase_    :Union[str, Any]			     =  os.path.join(args.tf_model_dir      ,     """parameters.json"""		)
			lowerCAmelCase_    :str			     =  json.loads(open(_lowercase		).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_    :str			     =  args.output + ".pt"
			lowerCAmelCase_    :Dict			     =  OrderedDict()
			with tf.device("""/CPU:0"""		):
						lowerCAmelCase_    :Optional[int]			     =  tf.train.load_checkpoint(args.tf_model_dir		)
						lowerCAmelCase_    :Optional[Any]			     =  reader.get_variable_to_shape_map()
						for key_name in shapes.keys():
									lowerCAmelCase_    :Dict			     =  reader.get_tensor(_lowercase		).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_    :Union[str, Any]			     =  int(key_name[9]		)
												elif key_name.startswith("""pasts/out"""		):
															lowerCAmelCase_    :Optional[int]			     =  8
												lowerCAmelCase_    :Dict			     =  "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_    :Dict			     =  torch.tensor(_lowercase		)
									elif key_name.startswith("""model/moe"""		):
												lowerCAmelCase_    :List[str]			     =  int(key_name[9:].split("""/"""		)[0]		)
												if key_name.endswith("""/switch_gating/kernel"""		):
															lowerCAmelCase_    :str			     =  "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
															lowerCAmelCase_    :Tuple			     =  vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
															lowerCAmelCase_    :List[str]			     =  torch.tensor(_lowercase		)
												elif key_name.endswith("""/softmlp/kernel"""		):
															lowerCAmelCase_    :Optional[int]			     =  "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
															lowerCAmelCase_    :Tuple			     =  vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
															lowerCAmelCase_    :List[Any]			     =  torch.tensor(_lowercase		)
												elif key_name.endswith("""/wo/kernel"""		) or key_name.endswith("""/wi/kernel"""		):
															lowerCAmelCase_    :Any			     =  key_name[-9:-7]
															for i in range(1_6		):
																		lowerCAmelCase_    :List[Any]			     =  "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
																		lowerCAmelCase_    :str			     =  (
																		    vnp[i].transpose([1, 0]		).copy()
																		)  # In Mesh-Tensorflow, it is one array, so it is divided
																		lowerCAmelCase_    :Dict			     =  torch.tensor(_lowercase		)
									elif key_name.startswith("""model/mlp"""		):
												lowerCAmelCase_    :Union[str, Any]			     =  int(key_name[9:].split("""/"""		)[0]		)
												if key_name.endswith("""/p1/kernel"""		):
															lowerCAmelCase_    :str			     =  "model.blocks.%d.feed_forward.mlp.wi.weight" % player
															lowerCAmelCase_    :Dict			     =  vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
															lowerCAmelCase_    :Optional[int]			     =  torch.tensor(_lowercase		)
												elif key_name.endswith("""/p1/bias"""		):
															lowerCAmelCase_    :str			     =  "model.blocks.%d.feed_forward.mlp.wi.bias" % player
															lowerCAmelCase_    :List[Any]			     =  vnp.copy()  # same because it is one dimensional
															lowerCAmelCase_    :Tuple			     =  torch.tensor(_lowercase		)
												elif key_name.endswith("""/p2/kernel"""		):
															lowerCAmelCase_    :Union[str, Any]			     =  "model.blocks.%d.feed_forward.mlp.wo.weight" % player
															lowerCAmelCase_    :Tuple			     =  vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
															lowerCAmelCase_    :List[Any]			     =  torch.tensor(_lowercase		)
												elif key_name.endswith("""/p2/bias"""		):
															lowerCAmelCase_    :Dict			     =  "model.blocks.%d.feed_forward.mlp.wo.bias" % player
															lowerCAmelCase_    :List[str]			     =  vnp.copy()  # same because it is one dimensional
															lowerCAmelCase_    :str			     =  torch.tensor(_lowercase		)
									elif key_name.startswith("""model/ln"""		):
												lowerCAmelCase_    :List[str]			     =  int(key_name[8:].split("""/"""		)[0]		)
												if key_name.endswith("""/b"""		):
															lowerCAmelCase_    :Optional[Any]			     =  "model.blocks.%d.feed_forward.norm.bias" % player
															lowerCAmelCase_    :Tuple			     =  vnp.copy()  # same because it is one dimensional
															lowerCAmelCase_    :int			     =  torch.tensor(_lowercase		)
												elif key_name.endswith("""/g"""		):
															lowerCAmelCase_    :Optional[Any]			     =  "model.blocks.%d.feed_forward.norm.weight" % player
															lowerCAmelCase_    :List[str]			     =  vnp.copy()  # same because it is one dimensional
															lowerCAmelCase_    :Tuple			     =  torch.tensor(_lowercase		)
									elif key_name.startswith("""model/att"""		):
												lowerCAmelCase_    :Optional[Any]			     =  int(key_name[9:].split("""/"""		)[0]		)
												if key_name.endswith("""/qkv/kernel"""		):
															lowerCAmelCase_    :Union[str, Any]			     =  vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
															lowerCAmelCase_    :List[str]			     =  state[:, 0, :, :]
															lowerCAmelCase_    :Dict			     =  state[:, 1, :, :]
															lowerCAmelCase_    :Union[str, Any]			     =  state[:, 2, :, :]
															lowerCAmelCase_    :str			     =  (
															    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_    :List[str]			     =  (
															    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_    :Dict			     =  (
															    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_    :List[Any]			     =  "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
															lowerCAmelCase_    :Union[str, Any]			     =  torch.tensor(_lowercase		)
															lowerCAmelCase_    :Any			     =  "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
															lowerCAmelCase_    :List[str]			     =  torch.tensor(_lowercase		)
															lowerCAmelCase_    :Optional[Any]			     =  "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
															lowerCAmelCase_    :Optional[Any]			     =  torch.tensor(_lowercase		)
												elif key_name.endswith("""/o/kernel"""		):
															lowerCAmelCase_    :Any			     =  "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
															lowerCAmelCase_    :Optional[int]			     =  (
															    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]		).transpose([1, 0]		).copy()
															)  # Mesh-Tensorflow is a diagonal matrix
															lowerCAmelCase_    :Tuple			     =  torch.tensor(_lowercase		)
									elif key_name.startswith("""model/an"""		):
												lowerCAmelCase_    :List[str]			     =  int(key_name[8:].split("""/"""		)[0]		)
												if key_name.endswith("""/b"""		):
															lowerCAmelCase_    :Optional[int]			     =  "model.blocks.%d.self_attn.norm.bias" % player
															lowerCAmelCase_    :Union[str, Any]			     =  vnp.copy()  # same because it is one dimensional
															lowerCAmelCase_    :List[Any]			     =  torch.tensor(_lowercase		)
												elif key_name.endswith("""/g"""		):
															lowerCAmelCase_    :Any			     =  "model.blocks.%d.self_attn.norm.weight" % player
															lowerCAmelCase_    :str			     =  vnp.copy()  # same because it is one dimensional
															lowerCAmelCase_    :Any			     =  torch.tensor(_lowercase		)
									elif (
									    key_name.startswith("""model/wte"""		)
									    or key_name.startswith("""model/wpe"""		)
									    or key_name.startswith("""model/ete"""		)
									):
												lowerCAmelCase_    :Optional[int]			     =  {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
												    key_name[-3:]
												]
												lowerCAmelCase_    :Tuple			     =  "model.%s.weight" % nlayer
												lowerCAmelCase_    :Any			     =  vnp.copy()  # same in embedded
												lowerCAmelCase_    :Tuple			     =  torch.tensor(_lowercase		)
												if key_name.startswith("""model/wte"""		):
															lowerCAmelCase_    :Optional[int]			     =  "lm_head.weight"
															lowerCAmelCase_    :Optional[int]			     =  vnp.copy()  # same in embedded
															lowerCAmelCase_    :Optional[int]			     =  torch.tensor(_lowercase		)
									elif key_name.startswith("""model/wob"""		):
												lowerCAmelCase_    :Optional[int]			     =  "final_logits_bias"
												lowerCAmelCase_    :Optional[Any]			     =  vnp.copy()  # same in embedded
												lowerCAmelCase_    :Optional[int]			     =  state.reshape((1, -1)		)
												lowerCAmelCase_    :List[Any]			     =  torch.tensor(_lowercase		)
									elif key_name == "model/dense/kernel":
												lowerCAmelCase_    :Optional[int]			     =  "model.last_project.weight"
												lowerCAmelCase_    :Union[str, Any]			     =  vnp.transpose([1, 0]		).copy()  # Mesh-Tensorflow is a diagonal matrix
												lowerCAmelCase_    :List[Any]			     =  torch.tensor(_lowercase		)
									elif key_name == "model/dense_1/bias":
												lowerCAmelCase_    :Dict			     =  "model.last_project.bias"
												lowerCAmelCase_    :Optional[Any]			     =  vnp.copy()  # same because it is one dimensional
												lowerCAmelCase_    :Any			     =  torch.tensor(_lowercase		)
			torch.save(_lowercase      ,     args.output		)


if __name__ == "__main__":
				__UpperCAmelCase       =  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       =  parser.parse_args()
				convert_tf_gptsan_to_pt(args)













"""simple docstring"""




import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict


@dataclass
class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					List[str]      					=ComputeEnvironment.AMAZON_SAGEMAKER
							lowerCamelCase   :					str      					=True
							lowerCamelCase   :					Union[str, Any]      					="""ml.p3.2xlarge"""
							lowerCamelCase   :					str      					="""accelerate_sagemaker_execution_role"""
							lowerCamelCase   :					int      					="""hf-sm"""
							lowerCamelCase   :					int      					="""us-east-1"""
							lowerCamelCase   :					Tuple      					=1
							lowerCamelCase   :					Any      					="""accelerate-sagemaker-1"""
							lowerCamelCase   :					str      					="""1.6"""
							lowerCamelCase   :					Tuple      					="""4.4"""
							lowerCamelCase   :					Optional[int]      					="""train.py"""
							lowerCamelCase   :					Optional[Any]      					=[
							    """--model_name_or_path""",
							    """bert""",
							    """--do_train""",
							    """False""",
							    """--epochs""",
							    """3""",
							    """--learning_rate""",
							    """5e-5""",
							    """--max_steps""",
							    """50.5""",
							]
							lowerCamelCase   :					Union[str, Any]      					=[
							    """--model_name_or_path""",
							    """bert""",
							    """--do_train""",
							    """--do_test""",
							    """False""",
							    """--do_predict""",
							    """--epochs""",
							    """3""",
							    """--learning_rate""",
							    """5e-5""",
							    """--max_steps""",
							    """50.5""",
							]


class        __UpperCamelCase				(						unittest.TestCase						):






							def     __a   (   self      )	-> List[str]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								a      :		str	=				_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args      )
								assert isinstance(converted_args["model_name_or_path"]      ,					lowerCAmelCase__      )
								assert isinstance(converted_args["do_train"]      ,					lowerCAmelCase__      )
								assert isinstance(converted_args["epochs"]      ,					lowerCAmelCase__      )
								assert isinstance(converted_args["learning_rate"]      ,					lowerCAmelCase__      )
								assert isinstance(converted_args["max_steps"]      ,					lowerCAmelCase__      )

								with pytest.raises(lowerCAmelCase__      ):
									_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args      )


