
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					return params[f'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__="attention"	):
					'''simple docstring'''
					lowercase					=		lowercase					=		np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]	)
					lowercase					=		k_tmp.reshape(k_tmp.shape[0]				,    k_tmp.shape[1] * k_tmp.shape[2]	)
					lowercase					=		np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]	)
					lowercase					=		o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]				,    o_tmp.shape[2]	)
					lowercase					=		np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]	)
					lowercase					=		q_tmp.reshape(q_tmp.shape[0]				,    q_tmp.shape[1] * q_tmp.shape[2]	)
					lowercase					=		np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]	)
					lowercase					=		v_tmp.reshape(v_tmp.shape[0]				,    v_tmp.shape[1] * v_tmp.shape[2]	)
					return k, o, q, v



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=False	):
					'''simple docstring'''
					if split_mlp_wi:
										lowercase					=		params[f'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
										lowercase					=		params[f'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
										lowercase					=		(wi_a, wi_a)
					else:
										lowercase					=		params[f'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

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



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					return params[f'{prefix}/{prefix}/{layer_name}/scale'][:, i]



def  UpperCamelCase					(							lowerCAmelCase__				,    *, lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = False	):
					'''simple docstring'''
					lowercase					=		traverse_util.flatten_dict(variables['''target''']	)
					lowercase					=		{'''/'''.join(lowerCAmelCase__	): v for k, v in old.items()}

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

					lowercase					=		collections.OrderedDict()

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

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

										# Block i, layer 1 (MLP).
										lowercase					=		tax_layer_norm_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''encoder'''				,    '''pre_mlp_layer_norm'''	)
										lowercase			,		lowercase					=		tax_mlp_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''encoder'''				,    lowerCAmelCase__	)
										lowercase					=		layer_norm
										if split_mlp_wi:
															lowercase					=		wi[0].T
															lowercase					=		wi[1].T
										else:
															lowercase					=		wi.T
										lowercase					=		wo.T
										if scalable_attention:
															# convert the rel_embedding of each layer
															lowercase					=		tax_relpos_bias_lookup(
															    lowerCAmelCase__				,    lowerCAmelCase__				,    '''encoder'''	).T

					lowercase					=		old['''encoder/encoder_norm/scale''']

					if not scalable_attention:
										lowercase					=		tax_relpos_bias_lookup(
										    lowerCAmelCase__				,    0				,    '''encoder'''	).T
										lowercase					=		tax_relpos_bias_lookup(
										    lowerCAmelCase__				,    0				,    '''decoder'''	).T

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

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

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

															if scalable_attention:
																				# convert the rel_embedding of each layer
																				lowercase					=		tax_relpos_bias_lookup(lowerCAmelCase__				,    lowerCAmelCase__				,    '''decoder'''	).T

										lowercase					=		old['''decoder/decoder_norm/scale''']

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

					return new



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		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:
										lowercase					=		state_dict['''shared.weight''']

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

					return state_dict



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		checkpoints.load_tax_checkpoint(lowerCAmelCase__	)
					lowercase					=		convert_tax_to_pytorch(
					    lowerCAmelCase__				,    num_layers=config.num_layers				,    is_encoder_only=lowerCAmelCase__				,    scalable_attention=lowerCAmelCase__	)
					lowercase					=		make_state_dict(lowerCAmelCase__				,    lowerCAmelCase__	)
					model.load_state_dict(lowerCAmelCase__				,    strict=lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = False				,    lowerCAmelCase__ = False				,    ):
					'''simple docstring'''
					lowercase					=		MTaConfig.from_json_file(lowerCAmelCase__	)
					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:
										lowercase					=		UMTaEncoderModel(lowerCAmelCase__	)
					else:
										lowercase					=		UMTaForConditionalGeneration(lowerCAmelCase__	)

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

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

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


if __name__ == "__main__":
							lowercase__	:Tuple										=					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
							)
							parser.add_argument(
							    "--scalable_attention",
							    action="store_true",
							    help="Whether the model uses scaled attention (umt5 model)",
							    default=False,
							)
							lowercase__	:List[Any]										=					parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path,
							    args.config_file,
							    args.pytorch_dump_path,
							    args.is_encoder_only,
							    args.scalable_attention,
							)






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


lowercase__	:Optional[Any]										=					re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase__	:List[str]										=					10
lowercase__	:Tuple										=					256



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < MIN_NUM_TOKENS:
										return None
					lowercase					=		MinHash(num_perm=lowerCAmelCase__	)
					for token in set(lowerCAmelCase__	):
										min_hash.update(token.encode()	)
					return min_hash



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return {t for t in NON_ALPHA.split(lowerCAmelCase__	) if len(t.strip()	) > 0}







class        lowercase	:




							def __init__(       self     ,*,
							    A__ = 0.85     ,):
												lowercase					=		duplication_jaccard_threshold
												lowercase					=		NUM_PERM
												lowercase					=		MinHashLSH(threshold=self._duplication_jaccard_threshold     ,num_perm=self._num_perm)

												lowercase					=		defaultdict(A__)




							def 				A__   (       self     ,A__     ,A__):
												lowercase					=		self._index.query(A__)
												if code_key in self._index.keys:
																	print(f'Duplicate key {code_key}')
																	return

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




							def 				A__   (       self):
												lowercase					=		[]
												for base, duplicates in self._duplicate_clusters.items():
																	lowercase					=		[base] + list(A__)
																	# reformat the cluster to be a list of dict
																	lowercase					=		[{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
																	duplicate_clusters.append(A__)
												return duplicate_clusters





							def 				A__   (       self     ,A__):
												lowercase					=		self.get_duplicate_clusters()
												with open(A__     ,'''w''') as f:
																	json.dump(A__     ,A__)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		element
					lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash				,    ThreadedIterator(lowerCAmelCase__				,    max_queue_size=1_0000	)				,    chunksize=100				,    ):
															if data is not None:
																				yield data



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		get_tokens(lowerCAmelCase__	)
					lowercase					=		get_tokens(lowerCAmelCase__	)
					return len(tokensa & tokensa	) / len(tokensa | tokensa	)


lowercase__	:List[Any]										=					None



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[]
					for elementa in cluster:
										lowercase					=		_shared_dataset[elementa['''base_index''']]['''content''']
										for elementa in extremes:
															lowercase					=		_shared_dataset[elementa['''base_index''']]['''content''']
															if jaccard_similarity(lowerCAmelCase__				,    lowerCAmelCase__	) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															lowercase					=		1
															extremes.append(lowerCAmelCase__	)
					return extremes



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					global _shared_dataset
					lowercase					=		dataset
					lowercase					=		[]
					lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.85	):
					'''simple docstring'''
					lowercase					=		make_duplicate_clusters(lowerCAmelCase__				,    lowerCAmelCase__	)
					lowercase					=		{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
					lowercase					=		{}
					lowercase					=		find_extremes(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)
					for extremes in extremes_clusters:
										for element in extremes:
															lowercase					=		element
					lowercase					=		duplicate_indices - set(extreme_dict.keys()	)
					lowercase					=		dataset.filter(lambda lowerCAmelCase__				,    lowerCAmelCase__	: idx not in remove_indices				,    with_indices=lowerCAmelCase__	)

					# update duplicate_clusters
					for cluster in duplicate_clusters:
										for element in cluster:
															lowercase					=		element['''base_index'''] in extreme_dict
															if element["is_extreme"]:
																				lowercase					=		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




