






'''simple docstring'''




from random import shuffle

import tensorflow as tf
from numpy import array

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       int(__UpperCamelCase						)
			assert noofclusters < len(__UpperCamelCase						)

			# Find out the dimensionality
			snake_case_				:					Optional[int]   =       len(vectors[0]						)

			# Will help select random centroids from among the available vectors
			snake_case_				:					int   =       list(range(len(__UpperCamelCase						)						)						)
			shuffle(__UpperCamelCase						)

			# 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.

			snake_case_				:					str   =       tf.Graph()

			with graph.as_default():
						# SESSION OF COMPUTATION

						snake_case_				:					Dict   =       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
						snake_case_				:					str   =       [
						    tf.Variable(vectors[vector_indices[i]]						) for i in range(__UpperCamelCase						)
						]
						##These nodes will assign the centroid Variables the appropriate
						##values
						snake_case_				:					Dict   =       tf.placeholder("float64"     ,[dim]						)
						snake_case_				:					List[str]   =       []
						for centroid in centroids:
									cent_assigns.append(tf.assign(__UpperCamelCase     ,__UpperCamelCase						)						)

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

						##Now lets construct the node that will compute the mean
						# The placeholder for the input
						snake_case_				:					str   =       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
						snake_case_				:					List[str]   =       tf.reduce_mean(__UpperCamelCase     ,0						)

						##Node for computing Euclidean distances
						# Placeholders for input
						snake_case_				:					Union[str, Any]   =       tf.placeholder("float"     ,[dim]						)
						snake_case_				:					List[str]   =       tf.placeholder("float"     ,[dim]						)
						snake_case_				:					int   =       tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__UpperCamelCase     ,__UpperCamelCase						)     ,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
						snake_case_				:					List[str]   =       tf.placeholder("float"     ,[noofclusters]						)
						snake_case_				:					Optional[int]   =       tf.argmin(__UpperCamelCase     ,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.
						snake_case_				:					List[Any]   =       tf.initialize_all_variables()

						# Initialize all variables
						sess.run(__UpperCamelCase						)

						##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.
						snake_case_				:					Optional[Any]   =       100
						for _ in range(__UpperCamelCase						):
									##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(__UpperCamelCase						)						):
												snake_case_				:					Any   =       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.
												snake_case_				:					str   =       [
												    sess.run(__UpperCamelCase     ,feed_dict={va: vect, va: sess.run(__UpperCamelCase						)}						)
												    for centroid in centroids
												]
												# Now use the cluster assignment node, with the distances
												# as the input
												snake_case_				:					Any   =       sess.run(
												    __UpperCamelCase     ,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(__UpperCamelCase						):
												# Collect all the vectors assigned to this cluster
												snake_case_				:					List[str]   =       [
												    vectors[i]
												    for i in range(len(__UpperCamelCase						)						)
												    if sess.run(assignments[i]						) == cluster_n
												]
												# Compute new centroid location
												snake_case_				:					Dict   =       sess.run(
												    __UpperCamelCase     ,feed_dict={mean_input: array(__UpperCamelCase						)}						)
												# Assign value to appropriate variable
												sess.run(
												    cent_assigns[cluster_n]     ,feed_dict={centroid_value: new_location}						)

        # Return centroids and assignments
						snake_case_				:					Optional[Any]   =       sess.run(__UpperCamelCase						)
						snake_case_				:					Any   =       sess.run(__UpperCamelCase						)
						return centroids, assignments












'''simple docstring'''




import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
		raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
__lowerCamelCase   :					Union[str, Any]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					Union[str, Any]   							=					'''Hello world! cécé herlolip'''

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       FairseqRobertaModel.from_pretrained(__magic_name__						)
			roberta.eval()  # disable dropout
			snake_case_				:					Dict   =       roberta.model.encoder.sentence_encoder
			snake_case_				:					List[str]   =       XLMRobertaConfig(
			    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings     ,hidden_size=roberta.cfg.model.encoder_embed_dim     ,num_hidden_layers=roberta.cfg.model.encoder_layers     ,num_attention_heads=roberta.cfg.model.encoder_attention_heads     ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim     ,max_position_embeddings=514     ,type_vocab_size=1     ,layer_norm_eps=1E-5     ,)
			if classification_head:
						snake_case_				:					List[str]   =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("Our RoBERTa config:"     ,__magic_name__						)

			snake_case_				:					List[str]   =       XLMRobertaXLForSequenceClassification(__magic_name__						) if classification_head else XLMRobertaXLForMaskedLM(__magic_name__						)
			model.eval()

			# Now let's copy all the weights.
			# Embeddings
			snake_case_				:					List[Any]   =       roberta_sent_encoder.embed_tokens.weight
			snake_case_				:					int   =       roberta_sent_encoder.embed_positions.weight
			snake_case_				:					Union[str, Any]   =       torch.zeros_like(
			    model.roberta.embeddings.token_type_embeddings.weight						)  # just zero them out b/c RoBERTa doesn't use them.

			snake_case_				:					Union[str, Any]   =       roberta_sent_encoder.layer_norm.weight
			snake_case_				:					str   =       roberta_sent_encoder.layer_norm.bias

			for i in range(config.num_hidden_layers						):
						# Encoder: start of layer
						snake_case_				:					BertLayer   =       model.roberta.encoder.layer[i]
						snake_case_				:					TransformerSentenceEncoderLayer   =       roberta_sent_encoder.layers[i]

						snake_case_				:					RobertaAttention   =       layer.attention
						snake_case_				:					Dict   =       roberta_layer.self_attn_layer_norm.weight
						snake_case_				:					Dict   =       roberta_layer.self_attn_layer_norm.bias

						# self attention
						snake_case_				:					BertSelfAttention   =       layer.attention.self
						assert (
						    roberta_layer.self_attn.k_proj.weight.data.shape
						    == roberta_layer.self_attn.q_proj.weight.data.shape
						    == roberta_layer.self_attn.v_proj.weight.data.shape
						    == torch.Size((config.hidden_size, config.hidden_size)						)
						)

						snake_case_				:					Dict   =       roberta_layer.self_attn.q_proj.weight
						snake_case_				:					Any   =       roberta_layer.self_attn.q_proj.bias
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.weight
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.bias
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.v_proj.weight
						snake_case_				:					Any   =       roberta_layer.self_attn.v_proj.bias

						# self-attention output
						snake_case_				:					BertSelfOutput   =       layer.attention.output
						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
						snake_case_				:					List[str]   =       roberta_layer.self_attn.out_proj.weight
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						snake_case_				:					int   =       roberta_layer.final_layer_norm.weight
						snake_case_				:					Union[str, Any]   =       roberta_layer.final_layer_norm.bias

						# intermediate
						snake_case_				:					BertIntermediate   =       layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						snake_case_				:					List[str]   =       roberta_layer.fca.weight
						snake_case_				:					List[Any]   =       roberta_layer.fca.bias

						# output
						snake_case_				:					BertOutput   =       layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						snake_case_				:					Any   =       roberta_layer.fca.weight
						snake_case_				:					Any   =       roberta_layer.fca.bias
						# end of layer

			if classification_head:
						snake_case_				:					int   =       roberta.model.classification_heads["mnli"].dense.weight
						snake_case_				:					Union[str, Any]   =       roberta.model.classification_heads["mnli"].dense.bias
						snake_case_				:					Tuple   =       roberta.model.classification_heads["mnli"].out_proj.weight
						snake_case_				:					str   =       roberta.model.classification_heads["mnli"].out_proj.bias
			else:
						# LM Head
						snake_case_				:					Optional[Any]   =       roberta.model.encoder.lm_head.dense.weight
						snake_case_				:					int   =       roberta.model.encoder.lm_head.dense.bias
						snake_case_				:					Optional[Any]   =       roberta.model.encoder.lm_head.layer_norm.weight
						snake_case_				:					Optional[int]   =       roberta.model.encoder.lm_head.layer_norm.bias
						snake_case_				:					int   =       roberta.model.encoder.lm_head.weight
						snake_case_				:					List[str]   =       roberta.model.encoder.lm_head.bias

			# Let's check that we get the same results.
			snake_case_				:					torch.Tensor   =       roberta.encode(__magic_name__						).unsqueeze(0						)  # batch of size 1

			snake_case_				:					Union[str, Any]   =       model(__magic_name__						)[0]
			if classification_head:
						snake_case_				:					Optional[Any]   =       roberta.model.classification_heads["mnli"](roberta.extract_features(__magic_name__						)						)
			else:
						snake_case_				:					List[str]   =       roberta.model(__magic_name__						)[0]
			print(our_output.shape     ,their_output.shape						)
			snake_case_				:					str   =       torch.max(torch.abs(our_output - their_output						)						).item()
			print(F'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
			snake_case_				:					Any   =       torch.allclose(__magic_name__     ,__magic_name__     ,atol=1E-3						)
			print("Do both models output the same tensors?"     ,"🔥" if success else "💩"						)
			if not success:
						raise Exception("Something went wRoNg"						)

			pathlib.Path(__magic_name__						).mkdir(parents=__magic_name__     ,exist_ok=__magic_name__						)
			print(F'''Saving model to {pytorch_dump_folder_path}'''						)
			model.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__lowerCamelCase   :					Any   							=					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
		)
		__lowerCamelCase   :					Tuple   							=					parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)




