
from random import shuffle

import tensorflow as tf
from numpy import array



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						int(lowerCAmelCase__		)
					assert noofclusters < len(lowerCAmelCase__		)

					# Find out the dimensionality
					lowercase							=						len(vectors[0]		)

					# Will help select random centroids from among the available vectors
					lowercase							=						list(range(len(lowerCAmelCase__		)		)		)
					shuffle(lowerCAmelCase__		)

					# GRAPH OF COMPUTATION
					# We initialize a new graph and set it as the default during each run
					# of this algorithm. This ensures that as this function is called
					# multiple times, the default graph doesn't keep getting crowded with
					# unused ops and Variables from previous function calls.

					lowercase							=						tf.Graph()

					with graph.as_default():
										# SESSION OF COMPUTATION

										lowercase							=						tf.Session()

										##CONSTRUCTING THE ELEMENTS OF COMPUTATION

										##First lets ensure we have a Variable vector for each centroid,
										##initialized to one of the vectors from the available data points
										lowercase							=						[
										    tf.Variable(vectors[vector_indices[i]]		) for i in range(lowerCAmelCase__		)
										]
										##These nodes will assign the centroid Variables the appropriate
										##values
										lowercase							=						tf.placeholder('''float64''' ,			[dim]		)
										lowercase							=						[]
										for centroid in centroids:
															cent_assigns.append(tf.assign(lowerCAmelCase__ ,			lowerCAmelCase__		)		)

										##Variables for cluster assignments of individual vectors(initialized
										##to 0 at first)
										lowercase							=						[tf.Variable(0		) for i in range(len(lowerCAmelCase__		)		)]
										##These nodes will assign an assignment Variable the appropriate
										##value
										lowercase							=						tf.placeholder('''int32'''		)
										lowercase							=						[]
										for assignment in assignments:
															cluster_assigns.append(tf.assign(lowerCAmelCase__ ,			lowerCAmelCase__		)		)

										##Now lets construct the node that will compute the mean
										# The placeholder for the input
										lowercase							=						tf.placeholder('''float''' ,			[None, dim]		)
										# The Node/op takes the input and computes a mean along the 0th
										# dimension, i.e. the list of input vectors
										lowercase							=						tf.reduce_mean(lowerCAmelCase__ ,			0		)

										##Node for computing Euclidean distances
										# Placeholders for input
										lowercase							=						tf.placeholder('''float''' ,			[dim]		)
										lowercase							=						tf.placeholder('''float''' ,			[dim]		)
										lowercase							=						tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowerCAmelCase__ ,			lowerCAmelCase__		) ,			2		)		)		)

										##This node will figure out which cluster to assign a vector to,
										##based on Euclidean distances of the vector from the centroids.
										# Placeholder for input
										lowercase							=						tf.placeholder('''float''' ,			[noofclusters]		)
										lowercase							=						tf.argmin(lowerCAmelCase__ ,			0		)

										##INITIALIZING STATE VARIABLES

										##This will help initialization of all Variables defined with respect
										##to the graph. The Variable-initializer should be defined after
										##all the Variables have been constructed, so that each of them
										##will be included in the initialization.
										lowercase							=						tf.initialize_all_variables()

										# Initialize all variables
										sess.run(lowerCAmelCase__		)

										##CLUSTERING ITERATIONS

										# Now perform the Expectation-Maximization steps of K-Means clustering
										# iterations. To keep things simple, we will only do a set number of
										# iterations, instead of using a Stopping Criterion.
										lowercase							=						100
										for _ in range(lowerCAmelCase__		):
															##EXPECTATION STEP
															##Based on the centroid locations till last iteration, compute
															##the _expected_ centroid assignments.
															# Iterate over each vector
															for vector_n in range(len(lowerCAmelCase__		)		):
																				lowercase							=						vectors[vector_n]
																				# Compute Euclidean distance between this vector and each
																				# centroid. Remember that this list cannot be named
																				#'centroid_distances', since that is the input to the
																				# cluster assignment node.
																				lowercase							=						[
																				    sess.run(lowerCAmelCase__ ,			feed_dict={va: vect, va: sess.run(lowerCAmelCase__		)}		)
																				    for centroid in centroids
																				]
																				# Now use the cluster assignment node, with the distances
																				# as the input
																				lowercase							=						sess.run(
																				    lowerCAmelCase__ ,			feed_dict={centroid_distances: distances}		)
																				# Now assign the value to the appropriate state variable
																				sess.run(
																				    cluster_assigns[vector_n] ,			feed_dict={assignment_value: assignment}		)

															##MAXIMIZATION STEP
															# Based on the expected state computed from the Expectation Step,
															# compute the locations of the centroids so as to maximize the
															# overall objective of minimizing within-cluster Sum-of-Squares
															for cluster_n in range(lowerCAmelCase__		):
																				# Collect all the vectors assigned to this cluster
																				lowercase							=						[
																				    vectors[i]
																				    for i in range(len(lowerCAmelCase__		)		)
																				    if sess.run(assignments[i]		) == cluster_n
																				]
																				# Compute new centroid location
																				lowercase							=						sess.run(
																				    lowerCAmelCase__ ,			feed_dict={mean_input: array(lowerCAmelCase__		)}		)
																				# Assign value to appropriate variable
																				sess.run(
																				    cent_assigns[cluster_n] ,			feed_dict={centroid_value: new_location}		)

        # Return centroids and assignments
										lowercase							=						sess.run(lowerCAmelCase__		)
										lowercase							=						sess.run(lowerCAmelCase__		)
										return centroids, assignments






import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowercase__		:str   =    2







class 				lowercase		:




							def __init__(				self   ,*,  # begin keyword-only arguments
							    A__="<s>"   ,A__="<pad>"   ,A__="</s>"   ,A__="<unk>"   ,A__=None   ,):
												lowercase	,						lowercase	,						lowercase	,						lowercase							=						bos, unk, pad, eos
												lowercase							=						[]
												lowercase							=						[]
												lowercase							=						{}
												lowercase							=						self.add_symbol(A__)
												lowercase							=						self.add_symbol(A__)
												lowercase							=						self.add_symbol(A__)
												lowercase							=						self.add_symbol(A__)
												if extra_special_symbols:
																	for s in extra_special_symbols:
																						self.add_symbol(A__)
												lowercase							=						len(self.symbols)




							def __eq__(				self   ,A__):
												return self.indices == other.indices




							def __getitem__(				self   ,A__):
												if idx < len(self.symbols):
																	return self.symbols[idx]
												return self.unk_word




							def __len__(				self):
												return len(self.symbols)




							def __contains__(				self   ,A__):
												return sym in self.indices




							@classmethod
							def  A__      (				cls   ,A__):
												lowercase							=						cls()
												d.add_from_file(A__)
												return d




							def  A__      (				self   ,A__   ,A__=1   ,A__=False):
												if word in self.indices and not overwrite:
																	lowercase							=						self.indices[word]
																	lowercase							=						self.count[idx] + n
																	return idx
												else:
																	lowercase							=						len(self.symbols)
																	lowercase							=						idx
																	self.symbols.append(A__)
																	self.count.append(A__)
																	return idx




							def  A__      (				self   ,A__):
												return 0





							def  A__      (				self   ,A__):
												if isinstance(A__   ,A__):
																	try:
																						with open(A__   ,'''r'''   ,encoding='''utf-8''') as fd:
																											self.add_from_file(A__)
																	except FileNotFoundError as fnfe:
																						raise fnfe
																	except UnicodeError:
																						raise Exception('''Incorrect encoding detected in {}, please rebuild the dataset'''.format(A__))
																	return

												lowercase							=						f.readlines()
												lowercase							=						self._load_meta(A__)

												for line in lines[indices_start_line:]:
																	try:
																						lowercase	,						lowercase							=						line.rstrip().rsplit(''' '''   ,1)
																						if field == "#fairseq:overwrite":
																											lowercase							=						True
																											lowercase	,						lowercase							=						line.rsplit(''' '''   ,1)
																						else:
																											lowercase							=						False
																						lowercase							=						int(A__)
																						lowercase							=						line
																						if word in self and not overwrite:
																											raise RuntimeError(
																											    '''Duplicate word found when loading Dictionary: \'{}\'. '''
																											    '''Duplicate words can overwrite earlier ones by adding the '''
																											    '''#fairseq:overwrite flag at the end of the corresponding row '''
																											    '''in the dictionary file. If using the Camembert model, please '''
																											    '''download an updated copy of the model file.'''.format(A__))
																						self.add_symbol(A__   ,n=A__   ,overwrite=A__)
																	except ValueError:
																						raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt> [flags]\'''')



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
					# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
					lowercase							=						dict((re.sub(R'''@@$''' ,			'''''' ,			lowerCAmelCase__		), v) if k.endswith('''@@'''		) else (re.sub(R'''$''' ,			'''</w>''' ,			lowerCAmelCase__		), v) for k, v in d.items()		)
					lowercase							=						'''<s> <pad> </s> <unk>'''.split()
					# restore the special tokens
					for k in keep_keys:
										del da[f'{k}</w>']
										lowercase							=						d[k]  # restore
					return da



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					# prep
					if not os.path.exists(lowerCAmelCase__		):
										raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'		)
					os.makedirs(lowerCAmelCase__ ,			exist_ok=lowerCAmelCase__		)
					print(f'Writing results to {pytorch_dump_folder_path}'		)

					# handle various types of models

					lowercase							=						os.path.join(lowerCAmelCase__ ,			'''checkpoint.pt'''		)
					if not os.path.isfile(lowerCAmelCase__		):
										raise ValueError(f'path to the file {checkpoint_file} does not exist!'		)
					lowercase							=						torch.load(lowerCAmelCase__ ,			map_location='''cpu'''		)

					lowercase							=						chkpt['''cfg''']['''model''']

					# dicts
					lowercase							=						os.path.join(lowerCAmelCase__ ,			'''dict.txt'''		)
					if not os.path.isfile(lowerCAmelCase__		):
										raise ValueError(f'path to the file {dict_file} does not exist!'		)
					lowercase							=						Dictionary.load(lowerCAmelCase__		)
					lowercase							=						rewrite_dict_keys(src_dict.indices		)
					lowercase							=						len(lowerCAmelCase__		)
					lowercase							=						os.path.join(lowerCAmelCase__ ,			VOCAB_FILES_NAMES['''vocab_file''']		)
					print(f'Generating {src_vocab_file} of {src_vocab_size} records'		)
					with open(lowerCAmelCase__ ,			'''w''' ,			encoding='''utf-8'''		) as f:
										f.write(json.dumps(lowerCAmelCase__ ,			ensure_ascii=lowerCAmelCase__ ,			indent=lowerCAmelCase__		)		)

					# merges_file (bpecodes)
					lowercase							=						os.path.join(lowerCAmelCase__ ,			'''bpecodes'''		)
					if not os.path.isfile(lowerCAmelCase__		):
										raise ValueError(f'path to the file {bpecodes_file} does not exist!'		)

					lowercase							=						os.path.join(lowerCAmelCase__ ,			VOCAB_FILES_NAMES['''merges_file''']		)
					shutil.copyfile(lowerCAmelCase__ ,			lowerCAmelCase__		)

					# model config
					lowercase							=						os.path.join(lowerCAmelCase__ ,			'''config.json'''		)

					lowercase							=						{
					    '''activation_dropout''': args['''activation_dropout'''],
					    '''architectures''': ['''BioGptForCausalLM'''],
					    '''attention_probs_dropout_prob''': args['''attention_dropout'''],
					    '''bos_token_id''': 0,
					    '''eos_token_id''': 2,
					    '''hidden_act''': args['''activation_fn'''],
					    '''hidden_dropout_prob''': args['''dropout'''],
					    '''hidden_size''': args['''decoder_embed_dim'''],
					    '''initializer_range''': 0.02,
					    '''intermediate_size''': args['''decoder_ffn_embed_dim'''],
					    '''layer_norm_eps''': 1E-12,
					    '''layerdrop''': args['''decoder_layerdrop'''],
					    '''max_position_embeddings''': args['''max_target_positions'''],
					    '''model_type''': '''biogpt''',
					    '''num_attention_heads''': args['''decoder_attention_heads'''],
					    '''num_hidden_layers''': args['''decoder_layers'''],
					    '''pad_token_id''': 1,
					    '''scale_embedding''': not args['''no_scale_embedding'''],
					    '''tie_word_embeddings''': args['''share_decoder_input_output_embed'''],
					    '''vocab_size''': src_vocab_size,
					}

					# good hparam defaults to start with

					print(f'Generating {biogpt_model_config_file}'		)
					with open(lowerCAmelCase__ ,			'''w''' ,			encoding='''utf-8'''		) as f:
										f.write(json.dumps(lowerCAmelCase__ ,			ensure_ascii=lowerCAmelCase__ ,			indent=lowerCAmelCase__		)		)

					# tokenizer config
					lowercase							=						os.path.join(lowerCAmelCase__ ,			lowerCAmelCase__		)

					lowercase							=						{
					    '''bos_token''': '''<s>''',
					    '''eos_token''': '''</s>''',
					    '''model_max_length''': 1024,
					    '''pad_token''': '''<pad>''',
					    '''special_tokens_map_file''': None,
					    '''tokenizer_class''': '''BioGptTokenizer''',
					    '''unk_token''': '''<unk>''',
					}

					print(f'Generating {biogpt_tokenizer_config_file}'		)
					with open(lowerCAmelCase__ ,			'''w''' ,			encoding='''utf-8'''		) as f:
										f.write(json.dumps(lowerCAmelCase__ ,			ensure_ascii=lowerCAmelCase__ ,			indent=lowerCAmelCase__		)		)

					# model
					lowercase							=						chkpt['''model''']

					# remove unneeded keys
					lowercase							=						[
					    '''decoder.version''',
					]
					for k in ignore_keys:
										model_state_dict.pop(lowerCAmelCase__ ,			lowerCAmelCase__		)

					lowercase							=						list(model_state_dict.keys()		)
					for layer_name in layer_names:
										if layer_name.endswith('''output_projection.weight'''		):
															lowercase							=						model_state_dict.pop(lowerCAmelCase__		)
										else:
															lowercase							=						model_state_dict.pop(lowerCAmelCase__		)

					lowercase							=						BioGptConfig.from_pretrained(lowerCAmelCase__		)
					lowercase							=						BioGptForCausalLM(lowerCAmelCase__		)

					# check that it loads ok
					model_new.load_state_dict(lowerCAmelCase__		)

					# save
					lowercase							=						os.path.join(lowerCAmelCase__ ,			lowerCAmelCase__		)
					print(f'Generating {pytorch_weights_dump_path}'		)
					torch.save(lowerCAmelCase__ ,			lowerCAmelCase__		)

					print('''Conversion is done!'''		)


if __name__ == "__main__":
							lowercase__		:Union[str, Any]   =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--biogpt_checkpoint_path",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
							        " bpecodes, etc."
							    ),
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							lowercase__		:Any   =    parser.parse_args()
							convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)




