import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[str]	)							->					List[str]:







  """simple docstring"""





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


if __name__ == "__main__":
   __lowerCamelCase			: List[Any]							   =		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			: Tuple							   =		parser.parse_args()
   convert_tf_gptsan_to_pt(args)

import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.12.2'''):
   raise Exception('''requires fairseq >= 0.12.2''')
if version.parse(fairseq.__version__) > version.parse('''2'''):
   raise Exception('''requires fairseq < v2''')

logging.set_verbosity_info()
__lowerCamelCase			: Dict							   =		logging.get_logger(__name__)

__lowerCamelCase			: Dict							   =		'''Hello, World!'''
__lowerCamelCase			: Optional[Any]							   =		'''en_XX'''



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       bool	)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Path("""data_bin"""	)
  SCREAMING_SNAKE_CASE__										=      FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(__UpperCamelCase	).parent	) ,     checkpoint_file=Path(__UpperCamelCase	).name ,     _name="""xmod_base""" ,     arch="""xmod_base""" ,     task="""multilingual_masked_lm""" ,     data_name_or_path=str(__UpperCamelCase	) ,     bpe="""sentencepiece""" ,     sentencepiece_model=str(Path(__UpperCamelCase	).parent / """sentencepiece.bpe.model"""	) ,     src_dict=str(data_dir / """dict.txt"""	) ,     )
  xmod.eval()  # disable dropout
  print(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.sentence_encoder
  SCREAMING_SNAKE_CASE__										=      XmodConfig(
      vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,     hidden_size=xmod.cfg.model.encoder_embed_dim ,     num_hidden_layers=xmod.cfg.model.encoder_layers ,     num_attention_heads=xmod.cfg.model.encoder_attention_heads ,     intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,     max_position_embeddings=5_14 ,     type_vocab_size=1 ,     layer_norm_eps=1E-5 ,     pre_norm=xmod.cfg.model.encoder_normalize_before ,     adapter_reduction_factor=getattr(xmod.cfg.model ,     """bottleneck""" ,     2	) ,     adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,     adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,     ln_before_adapter=xmod.cfg.model.ln_before_adapter ,     languages=xmod.cfg.model.languages ,     )
  if classification_head:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

  print("""Our X-MOD config:""" ,     __UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      XmodForSequenceClassification(__UpperCamelCase	) if classification_head else XmodForMaskedLM(__UpperCamelCase	)
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.embed_tokens.weight
  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.embed_positions.weight
  SCREAMING_SNAKE_CASE__										=      torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layernorm_embedding.weight
  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layernorm_embedding.bias

  for i in range(config.num_hidden_layers	):
    # Encoder: start of layer
    SCREAMING_SNAKE_CASE__										=      model.roberta.encoder.layer[i]
    SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layers[i]

    # self attention
    SCREAMING_SNAKE_CASE__										=      layer.attention.self
    if not (
        xmod_layer.self_attn.k_proj.weight.data.shape
        == xmod_layer.self_attn.q_proj.weight.data.shape
        == xmod_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)	)
    ):
      raise AssertionError("""Dimensions of self-attention weights do not match."""	)

    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.q_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.q_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.k_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.k_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.v_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.v_proj.bias

    # self-attention output
    SCREAMING_SNAKE_CASE__										=      layer.attention.output
    if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
      raise AssertionError("""Dimensions of self-attention output weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.out_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.out_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn_layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn_layer_norm.bias

    # intermediate
    SCREAMING_SNAKE_CASE__										=      layer.intermediate
    if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of intermediate weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.bias

    # output
    SCREAMING_SNAKE_CASE__										=      layer.output
    if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of feed-forward weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.final_layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_layer_norm.weight
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_layer_norm.bias

    if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
      raise AssertionError("""Lists of language adapters do not match."""	)
    for lang_code, adapter in xmod_layer.adapter_modules.items():
      SCREAMING_SNAKE_CASE__										=      bert_output.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.weight
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.bias
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.weight
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.bias

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layer_norm.bias

  if classification_head:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].dense.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].dense.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.dense.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.dense.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.layer_norm.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  SCREAMING_SNAKE_CASE__										=      xmod.encode(__UpperCamelCase	).unsqueeze(0	)  # batch of size 1
  model.roberta.set_default_language(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      model(__UpperCamelCase	)[0]
  if classification_head:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""](xmod.extract_features(__UpperCamelCase	)	)
  else:
    SCREAMING_SNAKE_CASE__										=      xmod.model(__UpperCamelCase ,     lang_id=[SAMPLE_LANGUAGE]	)[0]
  print(our_output.shape ,     their_output.shape	)
  SCREAMING_SNAKE_CASE__										=      torch.max(torch.abs(our_output - their_output	)	).item()
  print(f"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
  SCREAMING_SNAKE_CASE__										=      torch.allclose(__UpperCamelCase ,     __UpperCamelCase ,     atol=1E-3	)
  print("""Do both models output the same tensors?""" ,     """🔥""" if success else """💩"""	)
  if not success:
    raise Exception("""Something went wRoNg"""	)

  Path(__UpperCamelCase	).mkdir(parents=__UpperCamelCase ,     exist_ok=__UpperCamelCase	)
  print(f"""Saving model to {pytorch_dump_folder_path}"""	)
  model.save_pretrained(__UpperCamelCase	)


if __name__ == "__main__":
   __lowerCamelCase			: Dict							   =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--xmod_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
   )
   __lowerCamelCase			: str							   =		parser.parse_args()
   convert_xmod_checkpoint_to_pytorch(
       args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )
