





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

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


__UpperCamelCase     :						Optional[Any]						       =  {
    '''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase     :						Any						       =  [
           '''FALCON_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''FalconForCausalLM''',
           '''FalconModel''',
           '''FalconPreTrainedModel''',
           '''FalconForSequenceClassification''',
           '''FalconForTokenClassification''',
           '''FalconForQuestionAnswering''',
       ]


if TYPE_CHECKING:
       from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_falcon import (
                  FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FalconForCausalLM,
                  FalconForQuestionAnswering,
                  FalconForSequenceClassification,
                  FalconForTokenClassification,
                  FalconModel,
                  FalconPreTrainedModel,
              )


else:
       import sys

       __UpperCamelCase     :						str						       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def 				__A(lowerCAmelCase			)		->					Dict:

		"""simple docstring"""






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


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



