'''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  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				Optional[int]   ,    _lowercase    :				Tuple     )							->	Tuple:
  __UpperCAmelCase:					Optional[int]		= os.path.abspath(_lowercase     )
  logger.info(F'''Converting TensorFlow checkpoint from {tf_path}'''     )
  # Load weights from TF model
  __UpperCAmelCase:					str		= tf.train.list_variables(_lowercase     )
  __UpperCAmelCase:					Union[str, Any]		= []
  __UpperCAmelCase:					List[Any]		= []
  __UpperCAmelCase:					Union[str, Any]		= []
  for full_name, shape in init_vars:
    # logger.info(f"Loading TF weight {name} with shape {shape}")
    __UpperCAmelCase:					Union[str, Any]		= 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'
      __UpperCAmelCase:					Optional[int]		= name[1:]
    # figure out how many levels deep the name is
    __UpperCAmelCase:					Tuple		= 0
    for _name in name:
      if _name.startswith("""layer_with_weights"""     ):
        depth += 1
      else:
        break
    layer_depth.append(_lowercase     )
    # read data
    __UpperCAmelCase:					Union[str, Any]		= 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     )     )})'''     )
  __UpperCAmelCase:					Any		= 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     ):
    __UpperCAmelCase:					Optional[int]		= full_name.split("""/"""     )
    __UpperCAmelCase:					Tuple		= model
    __UpperCAmelCase:					Union[str, Any]		= []
    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"""     ):
        __UpperCAmelCase:					Tuple		= 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"""]     )
          __UpperCAmelCase:					int		= getattr(_lowercase   ,    """embeddings"""     )
          __UpperCAmelCase:					int		= 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     )]     )
          __UpperCAmelCase:					Dict		= getattr(_lowercase   ,    """encoder"""     )
          __UpperCAmelCase:					Optional[int]		= getattr(_lowercase   ,    """layer"""     )
          __UpperCAmelCase:					Optional[Any]		= pointer[layer_num - 4]
        elif layer_num == config.num_hidden_layers + 4:
          # pooler layer
          trace.extend(["""pooler""", """dense"""]     )
          __UpperCAmelCase:					List[str]		= getattr(_lowercase   ,    """pooler"""     )
          __UpperCAmelCase:					List[Any]		= getattr(_lowercase   ,    """dense"""     )
      elif m_name == "embeddings":
        trace.append("""embeddings"""     )
        __UpperCAmelCase:					Union[str, Any]		= getattr(_lowercase   ,    """embeddings"""     )
        if layer_num == 0:
          trace.append("""word_embeddings"""     )
          __UpperCAmelCase:					str		= getattr(_lowercase   ,    """word_embeddings"""     )
        elif layer_num == 1:
          trace.append("""position_embeddings"""     )
          __UpperCAmelCase:					Dict		= getattr(_lowercase   ,    """position_embeddings"""     )
        elif layer_num == 2:
          trace.append("""token_type_embeddings"""     )
          __UpperCAmelCase:					List[str]		= getattr(_lowercase   ,    """token_type_embeddings"""     )
        else:
          raise ValueError(F'''Unknown embedding layer with name {full_name}'''     )
        trace.append("""weight"""     )
        __UpperCAmelCase:					Any		= getattr(_lowercase   ,    """weight"""     )
      elif m_name == "_attention_layer":
        # self-attention layer
        trace.extend(["""attention""", """self"""]     )
        __UpperCAmelCase:					int		= getattr(_lowercase   ,    """attention"""     )
        __UpperCAmelCase:					Optional[Any]		= getattr(_lowercase   ,    """self"""     )
      elif m_name == "_attention_layer_norm":
        # output attention norm
        trace.extend(["""attention""", """output""", """LayerNorm"""]     )
        __UpperCAmelCase:					Optional[Any]		= getattr(_lowercase   ,    """attention"""     )
        __UpperCAmelCase:					str		= getattr(_lowercase   ,    """output"""     )
        __UpperCAmelCase:					List[Any]		= getattr(_lowercase   ,    """LayerNorm"""     )
      elif m_name == "_attention_output_dense":
        # output attention dense
        trace.extend(["""attention""", """output""", """dense"""]     )
        __UpperCAmelCase:					str		= getattr(_lowercase   ,    """attention"""     )
        __UpperCAmelCase:					Optional[Any]		= getattr(_lowercase   ,    """output"""     )
        __UpperCAmelCase:					List[Any]		= getattr(_lowercase   ,    """dense"""     )
      elif m_name == "_output_dense":
        # output dense
        trace.extend(["""output""", """dense"""]     )
        __UpperCAmelCase:					Tuple		= getattr(_lowercase   ,    """output"""     )
        __UpperCAmelCase:					int		= getattr(_lowercase   ,    """dense"""     )
      elif m_name == "_output_layer_norm":
        # output dense
        trace.extend(["""output""", """LayerNorm"""]     )
        __UpperCAmelCase:					Optional[Any]		= getattr(_lowercase   ,    """output"""     )
        __UpperCAmelCase:					int		= getattr(_lowercase   ,    """LayerNorm"""     )
      elif m_name == "_key_dense":
        # attention key
        trace.append("""key"""     )
        __UpperCAmelCase:					Dict		= getattr(_lowercase   ,    """key"""     )
      elif m_name == "_query_dense":
        # attention query
        trace.append("""query"""     )
        __UpperCAmelCase:					Union[str, Any]		= getattr(_lowercase   ,    """query"""     )
      elif m_name == "_value_dense":
        # attention value
        trace.append("""value"""     )
        __UpperCAmelCase:					Optional[Any]		= getattr(_lowercase   ,    """value"""     )
      elif m_name == "_intermediate_dense":
        # attention intermediate dense
        trace.extend(["""intermediate""", """dense"""]     )
        __UpperCAmelCase:					Optional[int]		= getattr(_lowercase   ,    """intermediate"""     )
        __UpperCAmelCase:					int		= getattr(_lowercase   ,    """dense"""     )
      elif m_name == "_output_layer_norm":
        # output layer norm
        trace.append("""output"""     )
        __UpperCAmelCase:					str		= getattr(_lowercase   ,    """output"""     )
      # weights & biases
      elif m_name in ["bias", "beta"]:
        trace.append("""bias"""     )
        __UpperCAmelCase:					Any		= getattr(_lowercase   ,    """bias"""     )
      elif m_name in ["kernel", "gamma"]:
        trace.append("""weight"""     )
        __UpperCAmelCase:					int		= getattr(_lowercase   ,    """weight"""     )
      else:
        logger.warning(F'''Ignored {m_name}'''     )
        # for certain layers reshape is necessary
    __UpperCAmelCase:					Optional[int]		= """.""".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     ):
      __UpperCAmelCase:					Tuple		= array.reshape(pointer.data.shape     )
    if "kernel" in full_name:
      __UpperCAmelCase:					str		= array.transpose()
    if pointer.shape == array.shape:
      __UpperCAmelCase:					List[str]		= 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  UpperCamelCase__					(					_lowercase    :				int   ,    _lowercase    :				str   ,    _lowercase    :				Tuple     )							->	Any:
  # Instantiate model
  logger.info(F'''Loading model based on config from {config_path}...'''     )
  __UpperCAmelCase:					str		= BertConfig.from_json_file(_lowercase     )
  __UpperCAmelCase:					str		= 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 __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_ = None					):

    '''simple docstring'''







    if components is None:
      __UpperCAmelCase:					List[Any]		= []
    __UpperCAmelCase:					Tuple		= list(snake_case_					)







  def __len__(							self					):

    '''simple docstring'''







    return len(self.__components					)







  def __str__(							self					):

    '''simple docstring'''







    return "(" + ",".join(map(snake_case_   ,    self.__components					)					) + ")"







  def __add__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= len(self					)
    if size == len(snake_case_					):
      __UpperCAmelCase:					str		= [self.__components[i] + other.component(snake_case_					) for i in range(snake_case_					)]
      return Vector(snake_case_					)
    else:
      raise Exception("""must have the same size"""					)







  def __sub__(							self   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= len(self					)
    if size == len(snake_case_					):
      __UpperCAmelCase:					Dict		= [self.__components[i] - other.component(snake_case_					) for i in range(snake_case_					)]
      return Vector(snake_case_					)
    else:  # error case
      raise Exception("""must have the same size"""					)







  @overload
  def __mul__(							self   ,    snake_case_					):

    '''simple docstring'''







    ...







  @overload
  def __mul__(							self   ,    snake_case_					):

    '''simple docstring'''







    ...







  def __mul__(							self   ,    snake_case_					):

    '''simple docstring'''







    if isinstance(snake_case_   ,    (float, int)					):
      __UpperCAmelCase:					str		= [c * other for c in self.__components]
      return Vector(snake_case_					)
    elif isinstance(snake_case_   ,    snake_case_					) and len(self					) == len(snake_case_					):
      __UpperCAmelCase:					Dict		= len(self					)
      __UpperCAmelCase:					List[str]		= [self.__components[i] * other.component(snake_case_					) for i in range(snake_case_					)]
      return sum(snake_case_					)
    else:  # error case
      raise Exception("""invalid operand!"""					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    return Vector(self.__components					)







  def    lowercase_  (							self   ,    snake_case_					):

    '''simple docstring'''







    if isinstance(snake_case_   ,    snake_case_					) and -len(self.__components					) <= i < len(self.__components					):
      return self.__components[i]
    else:
      raise Exception("""index out of range"""					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    assert -len(self.__components					) <= pos < len(self.__components					)
    __UpperCAmelCase:					List[str]		= value







  def    lowercase_  (							self					):

    '''simple docstring'''







    if len(self.__components					) == 0:
      raise Exception("""Vector is empty"""					)
    __UpperCAmelCase:					Optional[Any]		= [c**2 for c in self.__components]
    return math.sqrt(sum(snake_case_					)					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_ = False					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= self * other
    __UpperCAmelCase:					Any		= self.euclidean_length() * other.euclidean_length()
    if deg:
      return math.degrees(math.acos(num / den					)					)
    else:
      return math.acos(num / den					)






def  UpperCamelCase__					(					_lowercase    :				int     )							->	Vector:
  assert isinstance(_lowercase   ,    _lowercase     )
  return Vector([0] * dimension     )






def  UpperCamelCase__					(					_lowercase    :				int   ,    _lowercase    :				int     )							->	Vector:
  assert isinstance(_lowercase   ,    _lowercase     ) and (isinstance(_lowercase   ,    _lowercase     ))
  __UpperCAmelCase:					int		= [0] * dimension
  __UpperCAmelCase:					int		= 1
  return Vector(_lowercase     )






def  UpperCamelCase__					(					_lowercase    :				float   ,    _lowercase    :				Vector   ,    _lowercase    :				Vector     )							->	Vector:
  assert (
      isinstance(_lowercase   ,    _lowercase     )
      and isinstance(_lowercase   ,    _lowercase     )
      and (isinstance(_lowercase   ,    (int, float)     ))
  )
  return x * scalar + y






def  UpperCamelCase__					(					_lowercase    :				int   ,    _lowercase    :				int   ,    _lowercase    :				int     )							->	Vector:
  random.seed(_lowercase     )
  __UpperCAmelCase:					str		= [random.randint(_lowercase   ,    _lowercase     ) for _ in range(_lowercase     )]
  return Vector(_lowercase     )




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= matrix
    __UpperCAmelCase:					Dict		= w
    __UpperCAmelCase:					Tuple		= h







  def __str__(							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= """"""
    for i in range(self.__height					):
      ans += "|"
      for j in range(self.__width					):
        if j < self.__width - 1:
          ans += str(self.__matrix[i][j]					) + ","
        else:
          ans += str(self.__matrix[i][j]					) + "|\n"
    return ans







  def __add__(							self   ,    snake_case_					):

    '''simple docstring'''







    if self.__width == other.width() and self.__height == other.height():
      __UpperCAmelCase:					List[str]		= []
      for i in range(self.__height					):
        __UpperCAmelCase:					List[Any]		= [
            self.__matrix[i][j] + other.component(snake_case_   ,    snake_case_					)
            for j in range(self.__width					)
        ]
        matrix.append(snake_case_					)
      return Matrix(snake_case_   ,    self.__width   ,    self.__height					)
    else:
      raise Exception("""matrix must have the same dimension!"""					)







  def __sub__(							self   ,    snake_case_					):

    '''simple docstring'''







    if self.__width == other.width() and self.__height == other.height():
      __UpperCAmelCase:					int		= []
      for i in range(self.__height					):
        __UpperCAmelCase:					Any		= [
            self.__matrix[i][j] - other.component(snake_case_   ,    snake_case_					)
            for j in range(self.__width					)
        ]
        matrix.append(snake_case_					)
      return Matrix(snake_case_   ,    self.__width   ,    self.__height					)
    else:
      raise Exception("""matrices must have the same dimension!"""					)







  @overload
  def __mul__(							self   ,    snake_case_					):

    '''simple docstring'''







    ...







  @overload
  def __mul__(							self   ,    snake_case_					):

    '''simple docstring'''







    ...







  def __mul__(							self   ,    snake_case_					):

    '''simple docstring'''







    if isinstance(snake_case_   ,    snake_case_					):  # matrix-vector
      if len(snake_case_					) == self.__width:
        __UpperCAmelCase:					Optional[Any]		= zero_vector(self.__height					)
        for i in range(self.__height					):
          __UpperCAmelCase:					Dict		= [
              self.__matrix[i][j] * other.component(snake_case_					)
              for j in range(self.__width					)
          ]
          ans.change_component(snake_case_   ,    sum(snake_case_					)					)
        return ans
      else:
        raise Exception(
            """vector must have the same size as the """
            """number of columns of the matrix!"""					)
    elif isinstance(snake_case_   ,    (int, float)					):  # matrix-scalar
      __UpperCAmelCase:					Dict		= [
          [self.__matrix[i][j] * other for j in range(self.__width					)]
          for i in range(self.__height					)
      ]
      return Matrix(snake_case_   ,    self.__width   ,    self.__height					)
    return None







  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.__height







  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.__width







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    if 0 <= x < self.__height and 0 <= y < self.__width:
      return self.__matrix[x][y]
    else:
      raise Exception("""change_component: indices out of bounds"""					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    if 0 <= x < self.__height and 0 <= y < self.__width:
      __UpperCAmelCase:					int		= value
    else:
      raise Exception("""change_component: indices out of bounds"""					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    if self.__height != self.__width:
      raise Exception("""Matrix is not square"""					)
    __UpperCAmelCase:					int		= self.__matrix[:x] + self.__matrix[x + 1 :]
    for i in range(len(snake_case_					)					):
      __UpperCAmelCase:					Optional[Any]		= minor[i][:y] + minor[i][y + 1 :]
    return Matrix(snake_case_   ,    self.__width - 1   ,    self.__height - 1					).determinant()







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    if self.__height != self.__width:
      raise Exception("""Matrix is not square"""					)
    if 0 <= x < self.__height and 0 <= y < self.__width:
      return (-1) ** (x + y) * self.minor(snake_case_   ,    snake_case_					)
    else:
      raise Exception("""Indices out of bounds"""					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    if self.__height != self.__width:
      raise Exception("""Matrix is not square"""					)
    if self.__height < 1:
      raise Exception("""Matrix has no element"""					)
    elif self.__height == 1:
      return self.__matrix[0][0]
    elif self.__height == 2:
      return (
          self.__matrix[0][0] * self.__matrix[1][1]
          - self.__matrix[0][1] * self.__matrix[1][0]
      )
    else:
      __UpperCAmelCase:					Any		= [
          self.__matrix[0][y] * self.cofactor(0   ,    snake_case_					) for y in range(self.__width					)
      ]
      return sum(snake_case_					)






def  UpperCamelCase__					(					_lowercase    :				int     )							->	Matrix:
  __UpperCAmelCase:					list[list[float]]		= [[0] * n for _ in range(_lowercase     )]
  return Matrix(_lowercase   ,    _lowercase   ,    _lowercase     )







def  UpperCamelCase__					(					_lowercase    :				int   ,    _lowercase    :				int   ,    _lowercase    :				int   ,    _lowercase    :				int     )							->	Matrix:
  random.seed(_lowercase     )
  __UpperCAmelCase:					list[list[float]]		= [
      [random.randint(_lowercase   ,    _lowercase     ) for _ in range(_lowercase     )] for _ in range(_lowercase     )
  ]
  return Matrix(_lowercase   ,    _lowercase   ,    _lowercase     )