import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


__SCREAMING_SNAKE_CASE		:       Optional[int]      =					re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
__SCREAMING_SNAKE_CASE		:       Tuple      =					1_0
__SCREAMING_SNAKE_CASE		:       Any      =					2_5_6
def    snake_case		(__lowercase		)			->			Optional[MinHash]:

						'''simple docstring'''



						if len(lowerCAmelCase_		) < MIN_NUM_TOKENS:
												return None
						_snake_case		:	List[Any]							=       MinHash(num_perm=lowerCAmelCase_		)
						for token in set(lowerCAmelCase_		):
												min_hash.update(token.encode()		)
						return min_hash
def    snake_case		(__lowercase		)			->			Set[str]:

						'''simple docstring'''



						return {t for t in NON_ALPHA.split(lowerCAmelCase_		) if len(t.strip()		) > 0}



class   lowercase_       :



							def __init__(		self ,							*,
							    lowercase_ = 0.85 ,							):
													_snake_case		:	Union[str, Any]							=       duplication_jaccard_threshold
													_snake_case		:	Dict							=       NUM_PERM
													_snake_case		:	Dict							=       MinHashLSH(threshold=self._duplication_jaccard_threshold ,							num_perm=self._num_perm							)

													_snake_case		:	Tuple							=       defaultdict(_lowerCAmelCase							)



							def 			UpperCamelCase			(		self ,							lowercase_ ,							lowercase_							):
													_snake_case		:	List[Any]							=       self._index.query(_lowerCAmelCase							)
													if code_key in self._index.keys:
																			print(f"""Duplicate key {code_key}"""							)
																			return

													self._index.insert(_lowerCAmelCase ,							_lowerCAmelCase							)
													if len(_lowerCAmelCase							) > 0:
																			for base_duplicate in close_duplicates:
																									if base_duplicate in self._duplicate_clusters:
																															self._duplicate_clusters[base_duplicate].add(_lowerCAmelCase							)
																															break
																			else:
																									self._duplicate_clusters[close_duplicates[0]].add(_lowerCAmelCase							)



							def 			UpperCamelCase			(		self							):
													_snake_case		:	Optional[Any]							=       []
													for base, duplicates in self._duplicate_clusters.items():
																			_snake_case		:	Optional[Any]							=       [base] + list(_lowerCAmelCase							)
																			# reformat the cluster to be a list of dict
																			_snake_case		:	List[str]							=       [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
																			duplicate_clusters.append(_lowerCAmelCase							)
													return duplicate_clusters



							def 			UpperCamelCase			(		self ,							lowercase_							):
													_snake_case		:	List[str]							=       self.get_duplicate_clusters()
													with open(_lowerCAmelCase ,							"w"							) as f:
																			json.dump(_lowerCAmelCase ,							_lowerCAmelCase							)




def    snake_case		(__lowercase		)			->			int:

						'''simple docstring'''



						_snake_case							,_snake_case		:	List[Any]							=       element
						_snake_case		:	str							=       get_min_hash([t for t in NON_ALPHA.split(data["content"]		) if len(t.strip()		) > 0]		)
						if min_hash is not None:
												return (index, data["repo_name"], data["path"]), min_hash
def    snake_case		(__lowercase		)			->			Union[str, Any]:

						'''simple docstring'''



						with mp.Pool() as pool:
												for data in pool.imap_unordered(
												    _compute_min_hash   ,     ThreadedIterator(lowerCAmelCase_   ,     max_queue_size=10_000		)   ,     chunksize=100   ,     ):
																		if data is not None:
																								yield data
def    snake_case		(__lowercase   ,     __lowercase		)			->			Dict:

						'''simple docstring'''



						_snake_case		:	Tuple							=       DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase_		)

						for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCAmelCase_		)		)   ,     max_queue_size=100		)		):
												di.add(lowerCAmelCase_   ,     lowerCAmelCase_		)

						# Returns a List[Cluster] where Cluster is List[str] with the filenames.
						return di.get_duplicate_clusters()
def    snake_case		(__lowercase   ,     __lowercase		)			->			float:

						'''simple docstring'''



						_snake_case		:	str							=       get_tokens(lowerCAmelCase_		)
						_snake_case		:	Union[str, Any]							=       get_tokens(lowerCAmelCase_		)
						return len(tokensa & tokensa		) / len(tokensa | tokensa		)


__SCREAMING_SNAKE_CASE		:       int      =					None
def    snake_case		(__lowercase   ,     __lowercase		)			->			Tuple:

						'''simple docstring'''



						_snake_case		:	Optional[int]							=       []
						for elementa in cluster:
												_snake_case		:	Optional[int]							=       _shared_dataset[elementa["base_index"]]["content"]
												for elementa in extremes:
																		_snake_case		:	int							=       _shared_dataset[elementa["base_index"]]["content"]
																		if jaccard_similarity(lowerCAmelCase_   ,     lowerCAmelCase_		) >= jaccard_threshold:
																								elementa["copies"] += 1
																								break
												else:
																		_snake_case		:	int							=       1
																		extremes.append(lowerCAmelCase_		)
						return extremes
def    snake_case		(__lowercase   ,     __lowercase   ,     __lowercase		)			->			Tuple:

						'''simple docstring'''



						global _shared_dataset
						_snake_case		:	str							=       dataset
						_snake_case		:	str							=       []
						_snake_case		:	Any							=       partial(_find_cluster_extremes_shared   ,     jaccard_threshold=lowerCAmelCase_		)
						with mp.Pool() as pool:
												for extremes in tqdm(
												    pool.imap_unordered(
												        lowerCAmelCase_   ,     lowerCAmelCase_   ,     )   ,     total=len(lowerCAmelCase_		)   ,     ):
																		extremes_list.append(lowerCAmelCase_		)
						return extremes_list

def    snake_case		(__lowercase   ,     __lowercase = 0.85		)			->			Tuple[Type[Dataset], List[List[Dict]]]:

						'''simple docstring'''



						_snake_case		:	Union[str, Any]							=       make_duplicate_clusters(lowerCAmelCase_   ,     lowerCAmelCase_		)
						_snake_case		:	Optional[Any]							=       {x["base_index"] for cluster in duplicate_clusters for x in cluster}
						_snake_case		:	Union[str, Any]							=       {}
						_snake_case		:	str							=       find_extremes(lowerCAmelCase_   ,     lowerCAmelCase_   ,     lowerCAmelCase_		)
						for extremes in extremes_clusters:
												for element in extremes:
																		_snake_case		:	Dict							=       element
						_snake_case		:	List[Any]							=       duplicate_indices - set(extreme_dict.keys()		)
						_snake_case		:	Optional[Any]							=       dataset.filter(lambda __lowercase   ,     __lowercase		: idx not in remove_indices   ,     with_indices=lowerCAmelCase_		)

						# update duplicate_clusters
						for cluster in duplicate_clusters:
												for element in cluster:
																		_snake_case		:	List[str]							=       element["base_index"] in extreme_dict
																		if element["is_extreme"]:
																								_snake_case		:	Optional[Any]							=       extreme_dict[element["base_index"]]["copies"]

						print(F"""Original dataset size: {len(lowerCAmelCase_		)}"""		)
						print(F"""Number of duplicate clusters: {len(lowerCAmelCase_		)}"""		)
						print(F"""Files in duplicate cluster: {len(lowerCAmelCase_		)}"""		)
						print(F"""Unique files in duplicate cluster: {len(lowerCAmelCase_		)}"""		)
						print(F"""Filtered dataset size: {len(lowerCAmelCase_		)}"""		)

						return ds_filter, duplicate_clusters



import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
SCREAMING_SNAKE_CASE						:Tuple			=  {
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}



def       _lowerCAmelCase    (				lowerCAmelCase_				:Any				)->Any:
   '''simple docstring'''






   snake_case_            =       list(s_dict.keys()				)
   for key in keys:
      snake_case_            =       r".*/layers_(\d+)"
      snake_case_            =       key
      if re.match(lowerCAmelCase_		,    lowerCAmelCase_				):
         snake_case_            =       re.sub(r"layers_(\d+)"		,    r"block/\1/layer"		,    lowerCAmelCase_				)

      snake_case_            =       r"(encoder|decoder)\/"

      if re.match(lowerCAmelCase_		,    lowerCAmelCase_				):
         snake_case_            =       re.match(lowerCAmelCase_		,    lowerCAmelCase_				).groups()
         if groups[0] == "encoder":
            snake_case_            =       re.sub(r"/mlp/"		,    r"/1/mlp/"		,    lowerCAmelCase_				)
            snake_case_            =       re.sub(r"/pre_mlp_layer_norm/"		,    r"/1/layer_norm/"		,    lowerCAmelCase_				)

         elif groups[0] == "decoder":
            snake_case_            =       re.sub(r"/mlp/"		,    r"/2/mlp/"		,    lowerCAmelCase_				)
            snake_case_            =       re.sub(r"/pre_mlp_layer_norm/"		,    r"/2/layer_norm/"		,    lowerCAmelCase_				)

        # 2. Convert other classic mappings
      for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
         if old_key in new_key:
            snake_case_            =       new_key.replace(lowerCAmelCase_		,    lowerCAmelCase_				)

      print(F'''{key} -> {new_key}'''				)
      snake_case_            =       s_dict.pop(lowerCAmelCase_				)

   if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      snake_case_            =       s_dict[
          "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
      ].T
   if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      snake_case_            =       s_dict[
          "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
      ].T

   # 3. Take extra care of the EXPERTS layer
   for key in list(s_dict.keys()				):
      if "expert" in key:
         snake_case_            =       s_dict[key].shape[0]
         snake_case_            =       s_dict[key]
         for idx in range(lowerCAmelCase_				):
            snake_case_            =       expert_weihts[idx]
            print(F'''{key} -> {key.replace('expert/'		,    'nested fstring'				)}'''				)

         s_dict.pop(lowerCAmelCase_				)

   return s_dict


SCREAMING_SNAKE_CASE						:Optional[Any]			=  {
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]		,    lowerCAmelCase_				:Any				)->Optional[int]:
   '''simple docstring'''






   import regex as re

   with open(lowerCAmelCase_		,    "r"				) as f:
      snake_case_            =       f.read()

   snake_case_            =       re.findall(r"(.*) = ([0-9.]*)"		,    lowerCAmelCase_				)
   snake_case_            =       {}
   for param, value in regex_match:
      if param in GIN_TO_CONFIG_MAPPING and value != "":
         snake_case_            =       float(lowerCAmelCase_				) if "." in value else int(lowerCAmelCase_				)

   snake_case_            =       re.findall(r"(.*activations) = \(\'(.*)\',\)"		,    lowerCAmelCase_				)[0]
   snake_case_            =       str(activation[1]				)

   snake_case_            =       num_experts
   snake_case_            =       SwitchTransformersConfig(**lowerCAmelCase_				)
   return config



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:Dict=None		,    lowerCAmelCase_				:str="./"		,    lowerCAmelCase_				:Optional[int]=8				)->List[str]:
   '''simple docstring'''






   print(F'''Loading flax weights from : {flax_checkpoint_path}'''				)
   snake_case_            =       checkpoints.load_tax_checkpoint(lowerCAmelCase_				)

   if gin_file is not None:
      snake_case_            =       convert_gin_to_config(lowerCAmelCase_		,    lowerCAmelCase_				)
   else:
      snake_case_            =       SwitchTransformersConfig.from_pretrained(lowerCAmelCase_				)

   snake_case_            =       SwitchTransformersForConditionalGeneration(lowerCAmelCase_				)

   snake_case_            =       flax_params["target"]
   snake_case_            =       flatten_dict(lowerCAmelCase_		,    sep="/"				)
   snake_case_            =       rename_keys(lowerCAmelCase_				)
   snake_case_            =       unflatten_dict(lowerCAmelCase_		,    sep="/"				)

   # Load the flax params in the PT model
   load_flax_weights_in_pytorch_model(lowerCAmelCase_		,    lowerCAmelCase_				)

   print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
   pt_model.save_pretrained(lowerCAmelCase_				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:int			=  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--switch_t5x_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
           ''' model architecture. If not provided, a `gin_file` has to be provided.'''
       ),
   )
   parser.add_argument(
       '''--gin_file''',
       default=None,
       type=str,
       required=False,
       help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
   )
   parser.add_argument(
       '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
   )
   parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
   SCREAMING_SNAKE_CASE						:int			=  parser.parse_args()
   convert_flax_checkpoint_to_pytorch(
       args.switch_tax_checkpoint_path,
       args.config_name,
       args.gin_file,
       args.pytorch_dump_folder_path,
       args.num_experts,
   )




