
import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 def get_masked_lm_array(lowerCamelCase							):
  lowerCamelCase		:	Optional[int]							=      F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
  lowerCamelCase		:	Union[str, Any]							=      tf.train.load_variable(lowerCamelCase,	lowerCamelCase							)

  if "kernel" in name:
   lowerCamelCase		:	int							=      array.transpose()

  return torch.from_numpy(lowerCamelCase							)

 def get_encoder_array(lowerCamelCase							):
  lowerCamelCase		:	Optional[int]							=      F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
  lowerCamelCase		:	Optional[int]							=      tf.train.load_variable(lowerCamelCase,	lowerCamelCase							)

  if "kernel" in name:
   lowerCamelCase		:	List[str]							=      array.transpose()

  return torch.from_numpy(lowerCamelCase							)

 def get_encoder_layer_array(lowerCamelCase,	lowerCamelCase							):
  lowerCamelCase		:	Union[str, Any]							=      F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
  lowerCamelCase		:	Tuple							=      tf.train.load_variable(lowerCamelCase,	lowerCamelCase							)

  if "kernel" in name:
   lowerCamelCase		:	Dict							=      array.transpose()

  return torch.from_numpy(lowerCamelCase							)

 def get_encoder_attention_layer_array(lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
  lowerCamelCase		:	Optional[int]							=      F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
  lowerCamelCase		:	str							=      tf.train.load_variable(lowerCamelCase,	lowerCamelCase							)
  lowerCamelCase		:	List[str]							=      array.reshape(lowerCamelCase							)

  if "kernel" in name:
   lowerCamelCase		:	Optional[Any]							=      array.transpose()

  return torch.from_numpy(lowerCamelCase							)

 print(F'''Loading model based on config from {config_path}...'''							)
 lowerCamelCase		:	Tuple							=      BertConfig.from_json_file(lowerCamelCase							)
 lowerCamelCase		:	Optional[Any]							=      BertForMaskedLM(lowerCamelCase							)

 # Layers
 for layer_index in range(0,	config.num_hidden_layers							):
  lowerCamelCase		:	BertLayer							=      model.bert.encoder.layer[layer_index]

  # Self-attention
  lowerCamelCase		:	BertSelfAttention							=      layer.attention.self

  lowerCamelCase		:	Dict							=      get_encoder_attention_layer_array(
      lowerCamelCase,	"""_query_dense/kernel""",	self_attn.query.weight.data.shape							)
  lowerCamelCase		:	Any							=      get_encoder_attention_layer_array(
      lowerCamelCase,	"""_query_dense/bias""",	self_attn.query.bias.data.shape							)
  lowerCamelCase		:	Tuple							=      get_encoder_attention_layer_array(
      lowerCamelCase,	"""_key_dense/kernel""",	self_attn.key.weight.data.shape							)
  lowerCamelCase		:	List[str]							=      get_encoder_attention_layer_array(
      lowerCamelCase,	"""_key_dense/bias""",	self_attn.key.bias.data.shape							)
  lowerCamelCase		:	Tuple							=      get_encoder_attention_layer_array(
      lowerCamelCase,	"""_value_dense/kernel""",	self_attn.value.weight.data.shape							)
  lowerCamelCase		:	Optional[int]							=      get_encoder_attention_layer_array(
      lowerCamelCase,	"""_value_dense/bias""",	self_attn.value.bias.data.shape							)

  # Self-attention Output
  lowerCamelCase		:	BertSelfOutput							=      layer.attention.output

  lowerCamelCase		:	List[Any]							=      get_encoder_attention_layer_array(
      lowerCamelCase,	"""_output_dense/kernel""",	self_output.dense.weight.data.shape							)
  lowerCamelCase		:	int							=      get_encoder_attention_layer_array(
      lowerCamelCase,	"""_output_dense/bias""",	self_output.dense.bias.data.shape							)

  lowerCamelCase		:	Dict							=      get_encoder_layer_array(lowerCamelCase,	"""_attention_layer_norm/gamma"""							)
  lowerCamelCase		:	int							=      get_encoder_layer_array(lowerCamelCase,	"""_attention_layer_norm/beta"""							)

  # Intermediate
  lowerCamelCase		:	BertIntermediate							=      layer.intermediate

  lowerCamelCase		:	Optional[int]							=      get_encoder_layer_array(lowerCamelCase,	"""_intermediate_dense/kernel"""							)
  lowerCamelCase		:	Union[str, Any]							=      get_encoder_layer_array(lowerCamelCase,	"""_intermediate_dense/bias"""							)

  # Output
  lowerCamelCase		:	BertOutput							=      layer.output

  lowerCamelCase		:	Tuple							=      get_encoder_layer_array(lowerCamelCase,	"""_output_dense/kernel"""							)
  lowerCamelCase		:	List[str]							=      get_encoder_layer_array(lowerCamelCase,	"""_output_dense/bias"""							)

  lowerCamelCase		:	Dict							=      get_encoder_layer_array(lowerCamelCase,	"""_output_layer_norm/gamma"""							)
  lowerCamelCase		:	List[str]							=      get_encoder_layer_array(lowerCamelCase,	"""_output_layer_norm/beta"""							)

 # Embeddings
 lowerCamelCase		:	Any							=      get_encoder_array("""_position_embedding_layer/embeddings"""							)
 lowerCamelCase		:	Dict							=      get_encoder_array("""_type_embedding_layer/embeddings"""							)
 lowerCamelCase		:	Union[str, Any]							=      get_encoder_array("""_embedding_norm_layer/gamma"""							)
 lowerCamelCase		:	Union[str, Any]							=      get_encoder_array("""_embedding_norm_layer/beta"""							)

 # LM Head
 lowerCamelCase		:	Any							=      model.cls.predictions.transform

 lowerCamelCase		:	Optional[Any]							=      get_masked_lm_array("""dense/kernel"""							)
 lowerCamelCase		:	Optional[int]							=      get_masked_lm_array("""dense/bias"""							)

 lowerCamelCase		:	Dict							=      get_masked_lm_array("""layer_norm/gamma"""							)
 lowerCamelCase		:	Optional[int]							=      get_masked_lm_array("""layer_norm/beta"""							)

 lowerCamelCase		:	List[str]							=      get_masked_lm_array("""embedding_table"""							)

 # Pooling
 lowerCamelCase		:	int							=      BertPooler(config=lowerCamelCase							)
 lowerCamelCase		:	BertPooler							=      get_encoder_array("""_pooler_layer/kernel"""							)
 lowerCamelCase		:	BertPooler							=      get_encoder_array("""_pooler_layer/bias"""							)

 # Export final model
 model.save_pretrained(lowerCamelCase							)

 # Integration test - should load without any errors ;)
 lowerCamelCase		:	List[Any]							=      BertForMaskedLM.from_pretrained(lowerCamelCase							)
 print(new_model.eval()							)

 print("""Model conversion was done sucessfully!"""							)


if __name__ == "__main__":
      _lowerCamelCase					   =argparse.ArgumentParser()
      parser.add_argument(
          """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow Token Dropping checkpoint path."""
      )
      parser.add_argument(
          """--bert_config_file""",
          type=str,
          required=True,
          help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""",
          type=str,
          required=True,
          help="""Path to the output PyTorch model.""",
      )
      _lowerCamelCase					   =parser.parse_args()
      convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






# Copyright 2021 The HuggingFace Inc. 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 packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


_lowerCamelCase					   ="""pytorch_model.bin"""
_lowerCamelCase					   ="""pytorch_model.bin.index.json"""
_lowerCamelCase					   ="""adapter_config.json"""
_lowerCamelCase					   ="""adapter_model.bin"""
_lowerCamelCase					   ="""adapter_model.safetensors"""
_lowerCamelCase					   ="""tf_model.h5"""
_lowerCamelCase					   ="""tf_model.h5.index.json"""
_lowerCamelCase					   ="""model.ckpt"""
_lowerCamelCase					   ="""flax_model.msgpack"""
_lowerCamelCase					   ="""flax_model.msgpack.index.json"""
_lowerCamelCase					   ="""model.safetensors"""
_lowerCamelCase					   ="""model.safetensors.index.json"""
_lowerCamelCase					   ="""config.json"""
_lowerCamelCase					   ="""preprocessor_config.json"""
_lowerCamelCase					   =FEATURE_EXTRACTOR_NAME
_lowerCamelCase					   ="""generation_config.json"""
_lowerCamelCase					   ="""modelcard.json"""

_lowerCamelCase					   ="""▁"""
_lowerCamelCase					   =SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

_lowerCamelCase					   =[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
_lowerCamelCase					   =[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
_lowerCamelCase					   =[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]





def     _a					(						lowerCamelCase							):
 if version.parse(lowerCamelCase							) < version.parse(lowerCamelCase							):
  if "dev" in min_version:
   lowerCamelCase		:	Optional[int]							=      (
       """This example requires a source install from HuggingFace Transformers (see """
       """`https://huggingface.co/docs/transformers/installation#install-from-source`),"""
   )
  else:
   lowerCamelCase		:	int							=      F'''This example requires a minimum version of {min_version},'''
  error_message += F''' but the version found is {__version__}.\n'''
  raise ImportError(
      error_message
      + """Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other """
      """versions of HuggingFace Transformers."""							)




