



import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       """Hello world! cécé herlolip"""

def       A_   (						__a  :					str			,		__a  :					str			,		__a  :					bool		):

				"""simple docstring"""
				a__   ,      a__					   =		os.path.split(__a		)
				a__					   =		DataaVecTextModel.from_pretrained(
				    __a			,		checkpoint_file=__a		)
				dataavec.eval()  # disable dropout
				a__					   =		dataavec.models[0]
				a__					   =		dataavec_model.encoder.sentence_encoder
				a__					   =		DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings			,		hidden_size=dataavec_model.args.encoder_embed_dim			,		num_hidden_layers=dataavec_model.args.encoder_layers			,		num_attention_heads=dataavec_model.args.encoder_attention_heads			,		intermediate_size=dataavec_model.args.encoder_ffn_embed_dim			,		max_position_embeddings=514			,		type_vocab_size=1			,		layer_norm_eps=1e-5			,		)
				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
				print("""Our BERT config:"""			,		__a		)

				a__					   =		DataaVecTextForSequenceClassification(__a		) if classification_head else DataaVecTextForMaskedLM(__a		)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				a__					   =		dataavec_sent_encoder.embed_tokens.weight
				a__					   =		dataavec_sent_encoder.embed_positions.weight
				a__					   =		torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
				a__					   =		dataavec_sent_encoder.layernorm_embedding.weight
				a__					   =		dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers		):
								# Encoder: start of layer
								a__					   =		model.dataavec_text.encoder.layer[i]
								a__					   =		dataavec_sent_encoder.layers[i]

								# self attention
								a__					   =		layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)		), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
								)

								a__					   =		dataavec_layer.self_attn.q_proj.weight
								a__					   =		dataavec_layer.self_attn.q_proj.bias
								a__					   =		dataavec_layer.self_attn.k_proj.weight
								a__					   =		dataavec_layer.self_attn.k_proj.bias
								a__					   =		dataavec_layer.self_attn.v_proj.weight
								a__					   =		dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								a__					   =		layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
								a__					   =		dataavec_layer.self_attn.out_proj.weight
								a__					   =		dataavec_layer.self_attn.out_proj.bias
								a__					   =		dataavec_layer.self_attn_layer_norm.weight
								a__					   =		dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								a__					   =		layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								a__					   =		dataavec_layer.fca.weight
								a__					   =		dataavec_layer.fca.bias

								# output
								a__					   =		layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								a__					   =		dataavec_layer.fca.weight
								a__					   =		dataavec_layer.fca.bias
								a__					   =		dataavec_layer.final_layer_norm.weight
								a__					   =		dataavec_layer.final_layer_norm.bias
								# end of layer

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

				# Let's check that we get the same results.
				a__					   =		dataavec.encode(__a		).unsqueeze(0		)  # batch of size 1

				a__					   =		model(__a		)[0]
				if classification_head:
								a__					   =		dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__a		)		)
				else:
								a__					   =		dataavec_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__":
	UpperCAmelCase							=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--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."""
	)
	UpperCAmelCase							=       parser.parse_args()
	convert_dataavec_checkpoint_to_pytorch(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)






from typing import Any

import numpy as np

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_		):
											# 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 _a   (				*a_		):
											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 _a   (				self	,	a_		):
											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 _a   (				self	,	a_	,	a_	,	a_		):
											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 _a   (				self	,	a_		):
											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 _a   (				self		):
											# Stop simplex algorithm from cycling.
											for _ in range(100		):
															# 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 _a   (				self	,	a_	,	a_		):
											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()

