


"""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()
UpperCAmelCase__					=logging.get_logger(__name__)


def lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			Dict					, UpperCamelCase__						:			List[Any]			):






 """simple docstring"""







 __lowercase						=						os.path.abspath(UpperCamelCase__			)
 logger.info(f'''Converting TensorFlow checkpoint from {tf_path}'''			)
 # Load weights from TF model
 __lowercase						=						tf.train.list_variables(UpperCamelCase__			)
 __lowercase						=						[]
 __lowercase						=						[]
 __lowercase						=						[]
 for full_name, shape in init_vars:
  # logger.info(f"Loading TF weight {name} with shape {shape}")
  __lowercase						=						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'
   __lowercase						=						name[1:]
  # figure out how many levels deep the name is
  __lowercase						=						0
  for _name in name:
   if _name.startswith("""layer_with_weights"""			):
    depth += 1
   else:
    break
  layer_depth.append(UpperCamelCase__			)
  # read data
  __lowercase						=						tf.train.load_variable(UpperCamelCase__					, UpperCamelCase__			)
  names.append("""/""".join(UpperCamelCase__			)			)
  arrays.append(UpperCamelCase__			)
 logger.info(f'''Read a total of {len(UpperCamelCase__			):,} layers'''			)

 # Sanity check
 if len(set(UpperCamelCase__			)			) != 1:
  raise ValueError(f'''Found layer names with different depths (layer depth {list(set(UpperCamelCase__			)			)})'''			)
 __lowercase						=						list(set(UpperCamelCase__			)			)[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(UpperCamelCase__					, UpperCamelCase__			):
  __lowercase						=						full_name.split("""/"""			)
  __lowercase						=						model
  __lowercase						=						[]
  for i, m_name in enumerate(UpperCamelCase__			):
   if m_name == ".ATTRIBUTES":
    # variable names end with .ATTRIBUTES/VARIABLE_VALUE
    break
   if m_name.startswith("""layer_with_weights"""			):
    __lowercase						=						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"""]			)
     __lowercase						=						getattr(UpperCamelCase__					, """embeddings"""			)
     __lowercase						=						getattr(UpperCamelCase__					, """LayerNorm"""			)
    elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
     # encoder layers
     trace.extend(["""encoder""", """layer""", str(layer_num - 4			)]			)
     __lowercase						=						getattr(UpperCamelCase__					, """encoder"""			)
     __lowercase						=						getattr(UpperCamelCase__					, """layer"""			)
     __lowercase						=						pointer[layer_num - 4]
    elif layer_num == config.num_hidden_layers + 4:
     # pooler layer
     trace.extend(["""pooler""", """dense"""]			)
     __lowercase						=						getattr(UpperCamelCase__					, """pooler"""			)
     __lowercase						=						getattr(UpperCamelCase__					, """dense"""			)
   elif m_name == "embeddings":
    trace.append("""embeddings"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """embeddings"""			)
    if layer_num == 0:
     trace.append("""word_embeddings"""			)
     __lowercase						=						getattr(UpperCamelCase__					, """word_embeddings"""			)
    elif layer_num == 1:
     trace.append("""position_embeddings"""			)
     __lowercase						=						getattr(UpperCamelCase__					, """position_embeddings"""			)
    elif layer_num == 2:
     trace.append("""token_type_embeddings"""			)
     __lowercase						=						getattr(UpperCamelCase__					, """token_type_embeddings"""			)
    else:
     raise ValueError(f'''Unknown embedding layer with name {full_name}'''			)
    trace.append("""weight"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """weight"""			)
   elif m_name == "_attention_layer":
    # self-attention layer
    trace.extend(["""attention""", """self"""]			)
    __lowercase						=						getattr(UpperCamelCase__					, """attention"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """self"""			)
   elif m_name == "_attention_layer_norm":
    # output attention norm
    trace.extend(["""attention""", """output""", """LayerNorm"""]			)
    __lowercase						=						getattr(UpperCamelCase__					, """attention"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """output"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """LayerNorm"""			)
   elif m_name == "_attention_output_dense":
    # output attention dense
    trace.extend(["""attention""", """output""", """dense"""]			)
    __lowercase						=						getattr(UpperCamelCase__					, """attention"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """output"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """dense"""			)
   elif m_name == "_output_dense":
    # output dense
    trace.extend(["""output""", """dense"""]			)
    __lowercase						=						getattr(UpperCamelCase__					, """output"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """dense"""			)
   elif m_name == "_output_layer_norm":
    # output dense
    trace.extend(["""output""", """LayerNorm"""]			)
    __lowercase						=						getattr(UpperCamelCase__					, """output"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """LayerNorm"""			)
   elif m_name == "_key_dense":
    # attention key
    trace.append("""key"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """key"""			)
   elif m_name == "_query_dense":
    # attention query
    trace.append("""query"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """query"""			)
   elif m_name == "_value_dense":
    # attention value
    trace.append("""value"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """value"""			)
   elif m_name == "_intermediate_dense":
    # attention intermediate dense
    trace.extend(["""intermediate""", """dense"""]			)
    __lowercase						=						getattr(UpperCamelCase__					, """intermediate"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """dense"""			)
   elif m_name == "_output_layer_norm":
    # output layer norm
    trace.append("""output"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """output"""			)
   # weights & biases
   elif m_name in ["bias", "beta"]:
    trace.append("""bias"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """bias"""			)
   elif m_name in ["kernel", "gamma"]:
    trace.append("""weight"""			)
    __lowercase						=						getattr(UpperCamelCase__					, """weight"""			)
   else:
    logger.warning(f'''Ignored {m_name}'''			)
        # for certain layers reshape is necessary
  __lowercase						=						""".""".join(UpperCamelCase__			)
  if re.match(R"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""					, UpperCamelCase__			) or re.match(
      R"""(\S+)\.attention\.output\.dense\.weight"""					, UpperCamelCase__			):
   __lowercase						=						array.reshape(pointer.data.shape			)
  if "kernel" in full_name:
   __lowercase						=						array.transpose()
  if pointer.shape == array.shape:
   __lowercase						=						torch.from_numpy(UpperCamelCase__			)
  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 lowerCAmelCase_   (					UpperCamelCase__						:			int					, UpperCamelCase__						:			Dict					, UpperCamelCase__						:			Any			):






 """simple docstring"""







 logger.info(f'''Loading model based on config from {config_path}...'''			)
 __lowercase						=						BertConfig.from_json_file(UpperCamelCase__			)
 __lowercase						=						BertModel(UpperCamelCase__			)

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

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


if __name__ == "__main__":
       UpperCAmelCase__					=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).",
       )
       UpperCAmelCase__					=parser.parse_args()
       convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




"""simple docstring"""

from PIL import Image


def lowerCAmelCase_   (					UpperCamelCase__						:			Image					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						(259 * (level + 255)) / (255 * (259 - level))

 def contrast(UpperCamelCase__						:			int			) -> int:
  return int(128 + factor * (c - 128)			)

 return img.point(UpperCamelCase__			)


if __name__ == "__main__":
       # Load image
       with Image.open("image_data/lena.jpg") as img:
              # Change contrast to 170
              UpperCAmelCase__					=change_contrast(img, 170)
              cont_img.save("image_data/lena_high_contrast.png", format="png")
