





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Dict:
  lowercase__			    =						os.path.join(args.tf_model_dir	,		'parameters.json'	)
  lowercase__			    =						json.loads(open(_SCREAMING_SNAKE_CASE	).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(_SCREAMING_SNAKE_CASE	).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(_SCREAMING_SNAKE_CASE	)
      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(_SCREAMING_SNAKE_CASE	)
        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(_SCREAMING_SNAKE_CASE	)
        elif key_name.endswith('/wo/kernel'	) or key_name.endswith('/wi/kernel'	):
          lowercase__			    =						key_name[-9:-7]
          for i in range(16	):
            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(_SCREAMING_SNAKE_CASE	)
      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(_SCREAMING_SNAKE_CASE	)
        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(_SCREAMING_SNAKE_CASE	)
        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(_SCREAMING_SNAKE_CASE	)
        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(_SCREAMING_SNAKE_CASE	)
      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(_SCREAMING_SNAKE_CASE	)
        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(_SCREAMING_SNAKE_CASE	)
      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(_SCREAMING_SNAKE_CASE	)
          lowercase__			    =						'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
          lowercase__			    =						'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
        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(_SCREAMING_SNAKE_CASE	)
      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(_SCREAMING_SNAKE_CASE	)
        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(_SCREAMING_SNAKE_CASE	)
      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(_SCREAMING_SNAKE_CASE	)
        if key_name.startswith('model/wte'	):
          lowercase__			    =						'lm_head.weight'
          lowercase__			    =						vnp.copy()  # same in embedded
          lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
      elif key_name.startswith('model/wob'	):
        lowercase__			    =						'final_logits_bias'
        lowercase__			    =						vnp.copy()  # same in embedded
        lowercase__			    =						state.reshape((1, -1)	)
        lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
      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(_SCREAMING_SNAKE_CASE	)
      elif key_name == "model/dense_1/bias":
        lowercase__			    =						'model.last_project.bias'
        lowercase__			    =						vnp.copy()  # same because it is one dimensional
        lowercase__			    =						torch.tensor(_SCREAMING_SNAKE_CASE	)
  torch.save(_SCREAMING_SNAKE_CASE	,		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)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_					     =	{
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	["""SqueezeBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	[
          """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SqueezeBertForMaskedLM""",
          """SqueezeBertForMultipleChoice""",
          """SqueezeBertForQuestionAnswering""",
          """SqueezeBertForSequenceClassification""",
          """SqueezeBertForTokenClassification""",
          """SqueezeBertModel""",
          """SqueezeBertModule""",
          """SqueezeBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_squeezebert import (
          SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SqueezeBertConfig,
          SqueezeBertOnnxConfig,
      )
      from .tokenization_squeezebert import SqueezeBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_squeezebert import (
                SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SqueezeBertForMaskedLM,
                SqueezeBertForMultipleChoice,
                SqueezeBertForQuestionAnswering,
                SqueezeBertForSequenceClassification,
                SqueezeBertForTokenClassification,
                SqueezeBertModel,
                SqueezeBertModule,
                SqueezeBertPreTrainedModel,
            )

else:
      import sys

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


