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






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





"""simple docstring"""

from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters






def 							A						(    )				->     Any:
 __UpperCamelCase      =	{
     'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
     'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
     'content': ['a ' * 2_0, 'a ' * 3_0, 'b ' * 7],
 }
 __UpperCamelCase      =	Dataset.from_dict(snake_case		)
 return dataset





class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	get_dataset()
      __UpperCamelCase      =	make_duplicate_clusters(__UpperCAmelCase     ,					0.8_5				)
      self.assertEqual(len(duplicate_clusters[0]				)     ,					2				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	get_dataset()
      __UpperCamelCase  ,     __UpperCamelCase      =	deduplicate_dataset(__UpperCAmelCase				)
      self.assertEqual(len(__UpperCAmelCase				)     ,					2				)
      print(__UpperCAmelCase				)
      self.assertEqual(duplicate_clusters[0][0]['copies']     ,					2				)
      self.assertEqual(duplicate_clusters[0][0]['is_extreme']     ,					__UpperCAmelCase				)



