



"""simple docstring"""





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def        a__     (				lowerCAmelCase__				):
      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(16				):
                                    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)









"""simple docstring"""





# Copyright 2023 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase       	=						{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase       	=						[
      """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MgpstrModel""",
      """MgpstrPreTrainedModel""",
      """MgpstrForSceneTextRecognition""",
  ]

if TYPE_CHECKING:
  from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
  from .processing_mgp_str import MgpstrProcessor
  from .tokenization_mgp_str import MgpstrTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mgp_str import (
        MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
        MgpstrForSceneTextRecognition,
        MgpstrModel,
        MgpstrPreTrainedModel,
    )
else:
  import sys

  lowerCamelCase       	=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




