
"""simple docstring"""

import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def 							A						(    snake_case				:List[Any]    , snake_case				:List[Any]    , snake_case				:Tuple    , snake_case				:List[Any]="attention"		)				->     Dict:
 __UpperCamelCase      =	params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
 __UpperCamelCase      =	params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
 __UpperCamelCase      =	params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
 __UpperCamelCase      =	params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
 return k, o, q, v






def 							A						(    snake_case				:Optional[Any]    , snake_case				:Union[str, Any]    , snake_case				:str    , snake_case				:List[Any]=False		)				->     Union[str, Any]:
 if split_mlp_wi:
  __UpperCamelCase      =	params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
  __UpperCamelCase      =	params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
  __UpperCamelCase      =	(wi_a, wi_a)
 else:
  __UpperCamelCase      =	params[f'{prefix}/layers_{i}/mlp/wi/kernel']

 __UpperCamelCase      =	params[f'{prefix}/layers_{i}/mlp/wo/kernel']
 return wi, wo






def 							A						(    snake_case				:List[str]    , snake_case				:Dict    , snake_case				:List[Any]    , snake_case				:int		)				->     int:
 return params[f'{prefix}/layers_{i}/{layer_name}/scale']






def 							A						(    snake_case				:dict    , *, snake_case				:int    , snake_case				:bool		)				->     Any:
 __UpperCamelCase      =	traverse_util.flatten_dict(variables['target']		)
 __UpperCamelCase      =	{'/'.join(snake_case		): v for k, v in old.items()}

 # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
 __UpperCamelCase      =	'encoder/layers_0/mlp/wi_0/kernel' in old
 print('Split MLP:'    , snake_case		)

 __UpperCamelCase      =	collections.OrderedDict()

 # Shared embeddings.
 __UpperCamelCase      =	old['token_embedder/embedding']

 # Encoder.
 for i in range(snake_case		):
  # Block i, layer 0 (Self Attention).
  __UpperCamelCase      =	tax_layer_norm_lookup(snake_case    , snake_case    , 'encoder'    , 'pre_attention_layer_norm'		)
  __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	tax_attention_lookup(snake_case    , snake_case    , 'encoder'    , 'attention'		)
  __UpperCamelCase      =	layer_norm
  __UpperCamelCase      =	k.T
  __UpperCamelCase      =	o.T
  __UpperCamelCase      =	q.T
  __UpperCamelCase      =	v.T

  # Block i, layer 1 (MLP).
  __UpperCamelCase      =	tax_layer_norm_lookup(snake_case    , snake_case    , 'encoder'    , 'pre_mlp_layer_norm'		)
  __UpperCamelCase  ,     __UpperCamelCase      =	tax_mlp_lookup(snake_case    , snake_case    , 'encoder'    , snake_case		)
  __UpperCamelCase      =	layer_norm
  if split_mlp_wi:
   __UpperCamelCase      =	wi[0].T
   __UpperCamelCase      =	wi[1].T
  else:
   __UpperCamelCase      =	wi.T
  __UpperCamelCase      =	wo.T

 __UpperCamelCase      =	old[
     'encoder/relpos_bias/rel_embedding'
 ].T
 __UpperCamelCase      =	old['encoder/encoder_norm/scale']

 if not is_encoder_only:
  # Decoder.
  for i in range(snake_case		):
   # Block i, layer 0 (Self Attention).
   __UpperCamelCase      =	tax_layer_norm_lookup(snake_case    , snake_case    , 'decoder'    , 'pre_self_attention_layer_norm'		)
   __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	tax_attention_lookup(snake_case    , snake_case    , 'decoder'    , 'self_attention'		)
   __UpperCamelCase      =	layer_norm
   __UpperCamelCase      =	k.T
   __UpperCamelCase      =	o.T
   __UpperCamelCase      =	q.T
   __UpperCamelCase      =	v.T

   # Block i, layer 1 (Cross Attention).
   __UpperCamelCase      =	tax_layer_norm_lookup(snake_case    , snake_case    , 'decoder'    , 'pre_cross_attention_layer_norm'		)
   __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	tax_attention_lookup(snake_case    , snake_case    , 'decoder'    , 'encoder_decoder_attention'		)
   __UpperCamelCase      =	layer_norm
   __UpperCamelCase      =	k.T
   __UpperCamelCase      =	o.T
   __UpperCamelCase      =	q.T
   __UpperCamelCase      =	v.T

   # Block i, layer 2 (MLP).
   __UpperCamelCase      =	tax_layer_norm_lookup(snake_case    , snake_case    , 'decoder'    , 'pre_mlp_layer_norm'		)
   __UpperCamelCase  ,     __UpperCamelCase      =	tax_mlp_lookup(snake_case    , snake_case    , 'decoder'    , snake_case		)
   __UpperCamelCase      =	layer_norm
   if split_mlp_wi:
    __UpperCamelCase      =	wi[0].T
    __UpperCamelCase      =	wi[1].T
   else:
    __UpperCamelCase      =	wi.T
   __UpperCamelCase      =	wo.T

  __UpperCamelCase      =	old['decoder/decoder_norm/scale']
  __UpperCamelCase      =	old[
      'decoder/relpos_bias/rel_embedding'
  ].T

  # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
  if "decoder/logits_dense/kernel" in old:
   __UpperCamelCase      =	old['decoder/logits_dense/kernel'].T

 return new






def 							A						(    snake_case				:str    , snake_case				:bool		)				->     Dict:
 __UpperCamelCase      =	collections.OrderedDict([(k, torch.from_numpy(v.copy()		)) for (k, v) in converted_params.items()]		)

 # Add what is missing.
 if "encoder.embed_tokens.weight" not in state_dict:
  __UpperCamelCase      =	state_dict['shared.weight']

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   __UpperCamelCase      =	state_dict['shared.weight']

  if "lm_head.weight" not in state_dict:  # For old 1.0 models.
   print('Using shared word embeddings as lm_head.'		)
   __UpperCamelCase      =	state_dict['shared.weight']

 return state_dict






def 							A						(    snake_case				:Optional[Any]    , snake_case				:Any    , snake_case				:List[Any]    , snake_case				:List[str]		)				->     Tuple:
 __UpperCamelCase      =	checkpoints.load_tax_checkpoint(snake_case		)
 __UpperCamelCase      =	convert_tax_to_pytorch(snake_case    , num_layers=config.num_layers    , is_encoder_only=snake_case		)
 __UpperCamelCase      =	make_state_dict(snake_case    , snake_case		)
 model.load_state_dict(snake_case    , strict=snake_case		)






def 							A						(    snake_case				:Union[str, Any]    , snake_case				:List[Any]    , snake_case				:str    , snake_case				:bool = False		)				->     int:
 __UpperCamelCase      =	TaConfig.from_json_file(snake_case		)
 print(f'Building PyTorch model from configuration: {config}'		)
 # Non-v1.1 checkpoints could also use T5Model, but this works for all.
 # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
 if is_encoder_only:
  __UpperCamelCase      =	TaEncoderModel(snake_case		)
 else:
  __UpperCamelCase      =	TaForConditionalGeneration(snake_case		)

 # Load weights from tf checkpoint
 load_tax_weights_in_ta(snake_case    , snake_case    , snake_case    , snake_case		)

 # Save pytorch-model
 print(f'Save PyTorch model to {pytorch_dump_path}'		)
 model.save_pretrained(snake_case		)

 # Verify that we can load the checkpoint.
 model.from_pretrained(snake_case		)
 print('Done'		)


if __name__ == "__main__":
      UpperCamelCase     :    Optional[Any]       =			argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
      # Required parameters
      parser.add_argument(
          "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
      )
      UpperCamelCase     :    Union[str, Any]       =			parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
      )





"""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				)



