




"""simple docstring"""



import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Any:
 A__	=    os.path.abspath(lowercase_						)
 logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""						)
 # Load weights from TF model
 A__	=    tf.train.list_variables(lowercase_						)
 A__	=    []
 A__	=    []
 A__	=    []
 for full_name, shape in init_vars:
  # logger.info(f"Loading TF weight {name} with shape {shape}")
  A__	=    full_name.split("/"						)
  if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
   logger.info(f"""Skipping non-model layer {full_name}"""						)
   continue
  if "optimizer" in full_name:
   logger.info(f"""Skipping optimization layer {full_name}"""						)
   continue
  if name[0] == "model":
   # ignore initial 'model'
   A__	=    name[1:]
  # figure out how many levels deep the name is
  A__	=    0
  for _name in name:
   if _name.startswith("layer_with_weights"						):
    depth += 1
   else:
    break
  layer_depth.append(lowercase_						)
  # read data
  A__	=    tf.train.load_variable(lowercase_      ,			lowercase_						)
  names.append("/".join(lowercase_						)						)
  arrays.append(lowercase_						)
 logger.info(f"""Read a total of {len(lowercase_						):,} layers"""						)

 # Sanity check
 if len(set(lowercase_						)						) != 1:
  raise ValueError(f"""Found layer names with different depths (layer depth {list(set(lowercase_						)						)})"""						)
 A__	=    list(set(lowercase_						)						)[0]
 if layer_depth != 1:
  raise ValueError(
      "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
      " heads."						)

 # convert layers
 logger.info("Converting weights..."						)
 for full_name, array in zip(lowercase_      ,			lowercase_						):
  A__	=    full_name.split("/"						)
  A__	=    model
  A__	=    []
  for i, m_name in enumerate(lowercase_						):
   if m_name == ".ATTRIBUTES":
    # variable names end with .ATTRIBUTES/VARIABLE_VALUE
    break
   if m_name.startswith("layer_with_weights"						):
    A__	=    int(m_name.split("-"						)[-1]						)
    if layer_num <= 2:
     # embedding layers
     # layer_num 0: word_embeddings
     # layer_num 1: position_embeddings
     # layer_num 2: token_type_embeddings
     continue
    elif layer_num == 3:
     # embedding LayerNorm
     trace.extend(["embeddings", "LayerNorm"]						)
     A__	=    getattr(lowercase_      ,			"embeddings"						)
     A__	=    getattr(lowercase_      ,			"LayerNorm"						)
    elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
     # encoder layers
     trace.extend(["encoder", "layer", str(layer_num - 4						)]						)
     A__	=    getattr(lowercase_      ,			"encoder"						)
     A__	=    getattr(lowercase_      ,			"layer"						)
     A__	=    pointer[layer_num - 4]
    elif layer_num == config.num_hidden_layers + 4:
     # pooler layer
     trace.extend(["pooler", "dense"]						)
     A__	=    getattr(lowercase_      ,			"pooler"						)
     A__	=    getattr(lowercase_      ,			"dense"						)
   elif m_name == "embeddings":
    trace.append("embeddings"						)
    A__	=    getattr(lowercase_      ,			"embeddings"						)
    if layer_num == 0:
     trace.append("word_embeddings"						)
     A__	=    getattr(lowercase_      ,			"word_embeddings"						)
    elif layer_num == 1:
     trace.append("position_embeddings"						)
     A__	=    getattr(lowercase_      ,			"position_embeddings"						)
    elif layer_num == 2:
     trace.append("token_type_embeddings"						)
     A__	=    getattr(lowercase_      ,			"token_type_embeddings"						)
    else:
     raise ValueError(f"""Unknown embedding layer with name {full_name}"""						)
    trace.append("weight"						)
    A__	=    getattr(lowercase_      ,			"weight"						)
   elif m_name == "_attention_layer":
    # self-attention layer
    trace.extend(["attention", "self"]						)
    A__	=    getattr(lowercase_      ,			"attention"						)
    A__	=    getattr(lowercase_      ,			"self"						)
   elif m_name == "_attention_layer_norm":
    # output attention norm
    trace.extend(["attention", "output", "LayerNorm"]						)
    A__	=    getattr(lowercase_      ,			"attention"						)
    A__	=    getattr(lowercase_      ,			"output"						)
    A__	=    getattr(lowercase_      ,			"LayerNorm"						)
   elif m_name == "_attention_output_dense":
    # output attention dense
    trace.extend(["attention", "output", "dense"]						)
    A__	=    getattr(lowercase_      ,			"attention"						)
    A__	=    getattr(lowercase_      ,			"output"						)
    A__	=    getattr(lowercase_      ,			"dense"						)
   elif m_name == "_output_dense":
    # output dense
    trace.extend(["output", "dense"]						)
    A__	=    getattr(lowercase_      ,			"output"						)
    A__	=    getattr(lowercase_      ,			"dense"						)
   elif m_name == "_output_layer_norm":
    # output dense
    trace.extend(["output", "LayerNorm"]						)
    A__	=    getattr(lowercase_      ,			"output"						)
    A__	=    getattr(lowercase_      ,			"LayerNorm"						)
   elif m_name == "_key_dense":
    # attention key
    trace.append("key"						)
    A__	=    getattr(lowercase_      ,			"key"						)
   elif m_name == "_query_dense":
    # attention query
    trace.append("query"						)
    A__	=    getattr(lowercase_      ,			"query"						)
   elif m_name == "_value_dense":
    # attention value
    trace.append("value"						)
    A__	=    getattr(lowercase_      ,			"value"						)
   elif m_name == "_intermediate_dense":
    # attention intermediate dense
    trace.extend(["intermediate", "dense"]						)
    A__	=    getattr(lowercase_      ,			"intermediate"						)
    A__	=    getattr(lowercase_      ,			"dense"						)
   elif m_name == "_output_layer_norm":
    # output layer norm
    trace.append("output"						)
    A__	=    getattr(lowercase_      ,			"output"						)
   # weights & biases
   elif m_name in ["bias", "beta"]:
    trace.append("bias"						)
    A__	=    getattr(lowercase_      ,			"bias"						)
   elif m_name in ["kernel", "gamma"]:
    trace.append("weight"						)
    A__	=    getattr(lowercase_      ,			"weight"						)
   else:
    logger.warning(f"""Ignored {m_name}"""						)
        # for certain layers reshape is necessary
  A__	=    ".".join(lowercase_						)
  if re.match(R"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"      ,			lowercase_						) or re.match(
      R"(\S+)\.attention\.output\.dense\.weight"      ,			lowercase_						):
   A__	=    array.reshape(pointer.data.shape						)
  if "kernel" in full_name:
   A__	=    array.transpose()
  if pointer.shape == array.shape:
   A__	=    torch.from_numpy(lowercase_						)
  else:
   raise ValueError(
       f"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
       f""" {array.shape}"""						)
  logger.info(f"""Successfully set variable {full_name} to PyTorch layer {trace}"""						)
 return model







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		Tuple:
 # Instantiate model
 logger.info(f"""Loading model based on config from {config_path}..."""						)
 A__	=    BertConfig.from_json_file(lowercase_						)
 A__	=    BertModel(lowercase_						)

 # Load weights from checkpoint
 logger.info(f"""Loading weights from checkpoint {tf_checkpoint_path}..."""						)
 load_tfa_weights_in_bert(lowercase_      ,			lowercase_      ,			lowercase_						)

 # Save pytorch-model
 logger.info(f"""Saving PyTorch model to {pytorch_dump_path}..."""						)
 torch.save(model.state_dict()      ,			lowercase_						)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	argparse.ArgumentParser()
      parser.add_argument(
          "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x 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 (must include filename).",
      )
      SCREAMING_SNAKE_CASE    =	parser.parse_args()
      convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE    =	{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	["MobileViTFeatureExtractor"]
      SCREAMING_SNAKE_CASE    =	["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

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





