'''simple docstring'''




# Copyright 2021 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 pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


__lowercase        =    '''Create a default config file for Accelerate with only a few flags set.'''






def 							snake_case__				(     _A:   Dict="no" ,  _A:   str = default_json_config_file ,  _A:   bool = False					)       ->      List[str]:

		'''simple docstring'''


		lowerCAmelCase     =    Path(_A					)
		path.parent.mkdir(parents=_A ,  exist_ok=_A					)
		if path.exists():
				print(
				    f"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."					)
				return False
		lowerCAmelCase     =    mixed_precision.lower()
		if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
				raise ValueError(
				    f"`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"					)
		lowerCAmelCase     =    {
		    """compute_environment""": """LOCAL_MACHINE""",
		    """mixed_precision""": mixed_precision,
		}
		if torch.cuda.is_available():
				lowerCAmelCase     =    torch.cuda.device_count()
				lowerCAmelCase     =    num_gpus
				lowerCAmelCase     =    False
				if num_gpus > 1:
						lowerCAmelCase     =    """MULTI_GPU"""
				else:
						lowerCAmelCase     =    """NO"""
		elif is_xpu_available() and use_xpu:
				lowerCAmelCase     =    torch.xpu.device_count()
				lowerCAmelCase     =    num_xpus
				lowerCAmelCase     =    False
				if num_xpus > 1:
						lowerCAmelCase     =    """MULTI_XPU"""
				else:
						lowerCAmelCase     =    """NO"""
		elif is_npu_available():
				lowerCAmelCase     =    torch.npu.device_count()
				lowerCAmelCase     =    num_npus
				lowerCAmelCase     =    False
				if num_npus > 1:
						lowerCAmelCase     =    """MULTI_NPU"""
				else:
						lowerCAmelCase     =    """NO"""
		else:
				lowerCAmelCase     =    0
				lowerCAmelCase     =    True
				lowerCAmelCase     =    1
				lowerCAmelCase     =    """NO"""
		lowerCAmelCase     =    ClusterConfig(**_A					)
		config.to_json_file(_A					)
		return path






def 							snake_case__				(     _A:   str ,  _A:   Any					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    parser.add_parser("""default""" ,  parents=_A ,  help=_A ,  formatter_class=_A					)
		parser.add_argument(
		    """--config_file""" ,  default=_A ,  help=(
		        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
		        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
		        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
		        """with 'huggingface'."""
		    ) ,  dest="""save_location""" ,  )

		parser.add_argument(
		    """--mixed_precision""" ,  choices=["""no""", """fp16""", """bf16"""] ,  type=_A ,  help="""Whether or not to use mixed precision training. """
		    """Choose between FP16 and BF16 (bfloat16) training. """
		    """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.""" ,  default="""no""" ,  )
		parser.set_defaults(func=_A					)
		return parser






def 							snake_case__				(     _A:   Optional[int]					)       ->      Optional[int]:

		'''simple docstring'''


		lowerCAmelCase     =    write_basic_config(args.mixed_precision ,  args.save_location					)
		if config_file:
				print(f"accelerate configuration saved at {config_file}"					)





'''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__				(     _A:   Dict					)       ->      Dict:

		'''simple docstring'''


		lowerCAmelCase     =    os.path.join(args.tf_model_dir ,  """parameters.json"""					)
		lowerCAmelCase     =    json.loads(open(_A					).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     =    args.output + """.pt"""
		lowerCAmelCase     =    OrderedDict()
		with tf.device("""/CPU:0"""					):
				lowerCAmelCase     =    tf.train.load_checkpoint(args.tf_model_dir					)
				lowerCAmelCase     =    reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						lowerCAmelCase     =    reader.get_tensor(_A					).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(key_name[9]					)
								elif key_name.startswith("""pasts/out"""					):
										lowerCAmelCase     =    8
								lowerCAmelCase     =    """model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
								lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/moe"""					):
								lowerCAmelCase     =    int(key_name[9:].split("""/"""					)[0]					)
								if key_name.endswith("""/switch_gating/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/softmlp/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/wo/kernel"""					) or key_name.endswith("""/wi/kernel"""					):
										lowerCAmelCase     =    key_name[-9:-7]
										for i in range(16					):
												lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
												lowerCAmelCase     =    (
												    vnp[i].transpose([1, 0]					).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/mlp"""					):
								lowerCAmelCase     =    int(key_name[9:].split("""/"""					)[0]					)
								if key_name.endswith("""/p1/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wi.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/p1/bias"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wi.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/p2/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wo.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/p2/bias"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wo.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/ln"""					):
								lowerCAmelCase     =    int(key_name[8:].split("""/"""					)[0]					)
								if key_name.endswith("""/b"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.norm.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/g"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.norm.weight""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/att"""					):
								lowerCAmelCase     =    int(key_name[9:].split("""/"""					)[0]					)
								if key_name.endswith("""/qkv/kernel"""					):
										lowerCAmelCase     =    vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										lowerCAmelCase     =    state[:, 0, :, :]
										lowerCAmelCase     =    state[:, 1, :, :]
										lowerCAmelCase     =    state[:, 2, :, :]
										lowerCAmelCase     =    (
										    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     =    (
										    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     =    (
										    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     =    """model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
										lowerCAmelCase     =    torch.tensor(_A					)
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
										lowerCAmelCase     =    torch.tensor(_A					)
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/o/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
										lowerCAmelCase     =    (
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]					).transpose([1, 0]					).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/an"""					):
								lowerCAmelCase     =    int(key_name[8:].split("""/"""					)[0]					)
								if key_name.endswith("""/b"""					):
										lowerCAmelCase     =    """model.blocks.%d.self_attn.norm.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/g"""					):
										lowerCAmelCase     =    """model.blocks.%d.self_attn.norm.weight""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
						elif (
						    key_name.startswith("""model/wte"""					)
						    or key_name.startswith("""model/wpe"""					)
						    or key_name.startswith("""model/ete"""					)
						):
								lowerCAmelCase     =    {"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
								    key_name[-3:]
								]
								lowerCAmelCase     =    """model.%s.weight""" % nlayer
								lowerCAmelCase     =    vnp.copy()  # same in embedded
								lowerCAmelCase     =    torch.tensor(_A					)
								if key_name.startswith("""model/wte"""					):
										lowerCAmelCase     =    """lm_head.weight"""
										lowerCAmelCase     =    vnp.copy()  # same in embedded
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/wob"""					):
								lowerCAmelCase     =    """final_logits_bias"""
								lowerCAmelCase     =    vnp.copy()  # same in embedded
								lowerCAmelCase     =    state.reshape((1, -1)					)
								lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name == "model/dense/kernel":
								lowerCAmelCase     =    """model.last_project.weight"""
								lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
								lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name == "model/dense_1/bias":
								lowerCAmelCase     =    """model.last_project.bias"""
								lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
								lowerCAmelCase     =    torch.tensor(_A					)
		torch.save(_A ,  args.output					)


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




