


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()
lowerCamelCase__							:						Optional[int]							     =   logging.get_logger(__name__)







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

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

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

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


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






import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
							# Create universe of discourse in Python using linspace ()
							SCREAMING_SNAKE_CASE__   			=   np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

							# Create two fuzzy sets by defining any membership function
							# (trapmf(), gbellmf(), gaussmf(), etc).
							SCREAMING_SNAKE_CASE__   			=   [0, 2_5, 5_0]
							SCREAMING_SNAKE_CASE__   			=   [2_5, 5_0, 7_5]
							SCREAMING_SNAKE_CASE__   			=   fuzz.membership.trimf(X, abca)
							SCREAMING_SNAKE_CASE__   			=   fuzz.membership.trimf(X, abca)

							# Compute the different operations using inbuilt functions.
							SCREAMING_SNAKE_CASE__   			=   np.ones(7_5)
							SCREAMING_SNAKE_CASE__   			=   np.zeros((7_5,))
							# 1. Union = max(µA(x), µB(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_or(X, young, X, middle_aged)[1]
							# 2. Intersection = min(µA(x), µB(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, young, X, middle_aged)[1]
							# 3. Complement (A) = (1- min(µA(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_not(young)
							# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
							# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
							SCREAMING_SNAKE_CASE__   			=   young + middle_aged - (young * middle_aged)
							# 6. Algebraic Product = (µA(x) * µB(x))
							SCREAMING_SNAKE_CASE__   			=   young * middle_aged
							# 7. Bounded Sum = min[1,(µA(x), µB(x))]
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
							# 8. Bounded difference = min[0,(µA(x), µB(x))]
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

							# max-min composition
							# max-product composition

							# Plot each set A, set B and each operation result using plot() and subplot().
							from matplotlib import pyplot as plt

							plt.figure()

							plt.subplot(4, 3, 1)
							plt.plot(X, young)
							plt.title('''Young''')
							plt.grid(True)

							plt.subplot(4, 3, 2)
							plt.plot(X, middle_aged)
							plt.title('''Middle aged''')
							plt.grid(True)

							plt.subplot(4, 3, 3)
							plt.plot(X, union)
							plt.title('''union''')
							plt.grid(True)

							plt.subplot(4, 3, 4)
							plt.plot(X, intersection)
							plt.title('''intersection''')
							plt.grid(True)

							plt.subplot(4, 3, 5)
							plt.plot(X, complement_a)
							plt.title('''complement_a''')
							plt.grid(True)

							plt.subplot(4, 3, 6)
							plt.plot(X, difference)
							plt.title('''difference a/b''')
							plt.grid(True)

							plt.subplot(4, 3, 7)
							plt.plot(X, alg_sum)
							plt.title('''alg_sum''')
							plt.grid(True)

							plt.subplot(4, 3, 8)
							plt.plot(X, alg_product)
							plt.title('''alg_product''')
							plt.grid(True)

							plt.subplot(4, 3, 9)
							plt.plot(X, bdd_sum)
							plt.title('''bdd_sum''')
							plt.grid(True)

							plt.subplot(4, 3, 1_0)
							plt.plot(X, bdd_difference)
							plt.title('''bdd_difference''')
							plt.grid(True)

							plt.subplots_adjust(hspace=0.5)
							plt.show()




