


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


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


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






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:  Tuple    		=      {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  str    		=      ['DistilBertTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  List[str]    		=      [
     'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DistilBertForMaskedLM',
     'DistilBertForMultipleChoice',
     'DistilBertForQuestionAnswering',
     'DistilBertForSequenceClassification',
     'DistilBertForTokenClassification',
     'DistilBertModel',
     'DistilBertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  Union[str, Any]    		=      [
     'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDistilBertForMaskedLM',
     'TFDistilBertForMultipleChoice',
     'TFDistilBertForQuestionAnswering',
     'TFDistilBertForSequenceClassification',
     'TFDistilBertForTokenClassification',
     'TFDistilBertMainLayer',
     'TFDistilBertModel',
     'TFDistilBertPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  str    		=      [
     'FlaxDistilBertForMaskedLM',
     'FlaxDistilBertForMultipleChoice',
     'FlaxDistilBertForQuestionAnswering',
     'FlaxDistilBertForSequenceClassification',
     'FlaxDistilBertForTokenClassification',
     'FlaxDistilBertModel',
     'FlaxDistilBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:  int    		=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




