
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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
					raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:Any											=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:int											=       'Hello world! cécé herlolip'


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			str:







				"""simple docstring"""



				__A         =				FairseqRobertaModel.from_pretrained(a_		)
				roberta.eval()  # disable dropout
				__A         =				roberta.model.encoder.sentence_encoder
				__A         =				RobertaConfig(
				    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,     hidden_size=roberta.args.encoder_embed_dim	,     num_hidden_layers=roberta.args.encoder_layers	,     num_attention_heads=roberta.args.encoder_attention_heads	,     intermediate_size=roberta.args.encoder_ffn_embed_dim	,     max_position_embeddings=5_1_4	,     type_vocab_size=1	,     layer_norm_eps=1E-5	,     )
				if classification_head:
								__A         =				roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
				print("Our BERT config:"	,     a_		)

				__A         =				RobertaForSequenceClassification(a_		) if classification_head else RobertaForMaskedLM(a_		)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				__A         =				roberta_sent_encoder.embed_tokens.weight
				__A         =				roberta_sent_encoder.embed_positions.weight
				__A         =				torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c RoBERTa doesn't use them.
				__A         =				roberta_sent_encoder.emb_layer_norm.weight
				__A         =				roberta_sent_encoder.emb_layer_norm.bias

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

								# self attention
								__A         =				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)		)
								)

								__A         =				roberta_layer.self_attn.q_proj.weight
								__A         =				roberta_layer.self_attn.q_proj.bias
								__A         =				roberta_layer.self_attn.k_proj.weight
								__A         =				roberta_layer.self_attn.k_proj.bias
								__A         =				roberta_layer.self_attn.v_proj.weight
								__A         =				roberta_layer.self_attn.v_proj.bias

								# self-attention output
								__A         =				layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								__A         =				roberta_layer.self_attn.out_proj.weight
								__A         =				roberta_layer.self_attn.out_proj.bias
								__A         =				roberta_layer.self_attn_layer_norm.weight
								__A         =				roberta_layer.self_attn_layer_norm.bias

								# intermediate
								__A         =				layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								__A         =				roberta_layer.fca.weight
								__A         =				roberta_layer.fca.bias

								# output
								__A         =				layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								__A         =				roberta_layer.fca.weight
								__A         =				roberta_layer.fca.bias
								__A         =				roberta_layer.final_layer_norm.weight
								__A         =				roberta_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								__A         =				roberta.model.classification_heads["mnli"].dense.weight
								__A         =				roberta.model.classification_heads["mnli"].dense.bias
								__A         =				roberta.model.classification_heads["mnli"].out_proj.weight
								__A         =				roberta.model.classification_heads["mnli"].out_proj.bias
				else:
								# LM Head
								__A         =				roberta.model.encoder.lm_head.dense.weight
								__A         =				roberta.model.encoder.lm_head.dense.bias
								__A         =				roberta.model.encoder.lm_head.layer_norm.weight
								__A         =				roberta.model.encoder.lm_head.layer_norm.bias
								__A         =				roberta.model.encoder.lm_head.weight
								__A         =				roberta.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				__A         =				roberta.encode(a_		).unsqueeze(0		)  # batch of size 1

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

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[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.'
					)
					SCREAMING_SNAKE_CASE							:Dict											=       parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)


from typing import Any

import numpy as np





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Dict							,A      :				np.ndarray							,A      :				int				):
					# Check if RHS is negative
					if np.any(tableau[:, -1]							,where=tableau[:, -1] < 0				):
									raise ValueError("RHS must be > 0"				)

					__A         =				tableau
					__A    ,							__A         =				tableau.shape

					# Number of decision variables x1, x2, x3...
					__A         =				n_vars

					# Number of artificial variables to be minimised
					__A         =				len(np.where(tableau[self.n_vars : -1] == -1				)[0]				)

					# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
					__A         =				(self.n_art_vars > 0) + 1

					# Number of slack variables added to make inequalities into equalities
					__A         =				self.n_rows - self.n_stages

					# Objectives for each stage
					__A         =				["max"]

					# In two stage simplex, first minimise then maximise
					if self.n_art_vars:
									self.objectives.append("min"				)

					__A         =				[""]

					# Index of current pivot row and column
					__A         =				None
					__A         =				None

					# Does objective row only contain (non)-negative values?
					__A         =				False






	@staticmethod
	def        UpperCamelCase_						( *A      :				int				):
					if len(A				) != 3:
									raise ValueError("Must provide n_vars, n_slack, and n_art_vars"				)

					if not all(x >= 0 and isinstance(A							,A				) for x in args				):
									raise ValueError("All arguments must be non-negative integers"				)

					# decision | slack | artificial
					__A         =				["x", "s", "a"]
					__A         =				[]
					for i in range(3				):
									for j in range(args[i]				):
													titles.append(string_starts[i] + str(j + 1				)				)
					titles.append("RHS"				)
					return titles






	def        UpperCamelCase_						( self      :				List[Any]							,A      :				np.ndarray				):
					__A         =				self.objectives[-1]

					# Find entries of highest magnitude in objective rows
					__A         =				(objective == "min") - (objective == "max")
					__A         =				np.argmax(sign * tableau[0, : self.n_vars]				)

					# Choice is only valid if below 0 for maximise, and above for minimise
					if sign * self.tableau[0, col_idx] <= 0:
									__A         =				True
									return 0, 0

					# Pivot row is chosen as having the lowest quotient when elements of
					# the pivot column divide the right-hand side

					# Slice excluding the objective rows
					__A         =				slice(self.n_stages							,self.n_rows				)

					# RHS
					__A         =				tableau[s, -1]

					# Elements of pivot column within slice
					__A         =				tableau[s, col_idx]

					# Array filled with nans
					__A         =				np.full(self.n_rows - self.n_stages							,np.nan				)

					# If element in pivot column is greater than zeron_stages, return
					# quotient or nan otherwise
					__A         =				np.divide(A							,A							,out=A							,where=divisor > 0				)

					# Arg of minimum quotient excluding the nan values. n_stages is added
					# to compensate for earlier exclusion of objective columns
					__A         =				np.nanargmin(A				) + self.n_stages
					return row_idx, col_idx






	def        UpperCamelCase_						( self      :				List[Any]							,A      :				np.ndarray							,A      :				int							,A      :				int				):
					__A         =				tableau[row_idx].copy()

					__A         =				piv_row[col_idx]

					# Entry becomes 1
					piv_row *= 1 / piv_val

					# Variable in pivot column becomes basic, ie the only non-zero entry
					for idx, coeff in enumerate(tableau[:, col_idx]				):
									tableau[idx] += -coeff * piv_row
					__A         =				piv_row
					return tableau






	def        UpperCamelCase_						( self      :				Optional[Any]							,A      :				np.ndarray				):
					self.objectives.pop()

					if not self.objectives:
									return tableau

					# Slice containing ids for artificial columns
					__A         =				slice(-self.n_art_vars - 1							,-1				)

					# Delete the artificial variable columns
					__A         =				np.delete(A							,A							,axis=1				)

					# Delete the objective row of the first stage
					__A         =				np.delete(A							,0							,axis=0				)

					__A         =				1
					self.n_rows -= 1
					__A         =				0
					__A         =				False
					return tableau






	def        UpperCamelCase_						( self      :				Dict				):
					# Stop simplex algorithm from cycling.
					for _ in range(1_00				):
									# Completion of each stage removes an objective. If both stages
									# are complete, then no objectives are left
									if not self.objectives:
													__A         =				self.generate_col_titles(
													    self.n_vars							,self.n_slack							,self.n_art_vars				)

													# Find the values of each variable at optimal solution
													return self.interpret_tableau(self.tableau							,self.col_titles				)

									__A    ,							__A         =				self.find_pivot(self.tableau				)

									# If there are no more negative values in objective row
									if self.stop_iter:
													# Delete artificial variable columns and rows. Update attributes
													__A         =				self.change_stage(self.tableau				)
									else:
													__A         =				self.pivot(self.tableau							,A							,A				)
					return {}







	def        UpperCamelCase_						( self      :				Dict							,A      :				np.ndarray							,A      :				list[str]				):
					__A         =				{"P": abs(tableau[0, -1]				)}

					for i in range(self.n_vars				):
									# Gives ids of nonzero entries in the ith column
									__A         =				np.nonzero(tableau[:, i]				)
									__A         =				len(nonzero[0]				)

									# First entry in the nonzero ids
									__A         =				nonzero[0][0]
									__A         =				tableau[nonzero_rowidx, i]

									# If there is only one nonzero value in column, which is one
									if n_nonzero == nonzero_val == 1:
													__A         =				tableau[nonzero_rowidx, -1]
													__A         =				rhs_val

        # Check for basic variables
					for title in col_titles:
									# Don't add RHS or slack variables to output dict
									if title[0] not in "R-s-a":
													output_dict.setdefault(A							,0				)
					return output_dict


if __name__ == "__main__":
					import doctest

					doctest.testmod()
