






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()
_a       =    logging.get_logger(__name__)

_a       =    '''Hello world! cécé herlolip'''


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    List[str]:





						"""simple docstring"""



						_UpperCAmelCase   ,					_UpperCAmelCase							   =     os.path.split(__lowerCAmelCase      )
						_UpperCAmelCase							   =     DataaVecTextModel.from_pretrained(
						    __lowerCAmelCase					,						checkpoint_file=__lowerCAmelCase      )
						dataavec.eval()  # disable dropout
						_UpperCAmelCase							   =     dataavec.models[0]
						_UpperCAmelCase							   =     dataavec_model.encoder.sentence_encoder
						_UpperCAmelCase							   =     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:
												_UpperCAmelCase							   =     dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our BERT config:'					,						__lowerCAmelCase      )

						_UpperCAmelCase							   =     DataaVecTextForSequenceClassification(__lowerCAmelCase      ) if classification_head else DataaVecTextForMaskedLM(__lowerCAmelCase      )
						model.eval()

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

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

												# self attention
												_UpperCAmelCase							   =     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)      )}"""
												)

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

												# self-attention output
												_UpperCAmelCase							   =     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}"""
												_UpperCAmelCase							   =     dataavec_layer.self_attn.out_proj.weight
												_UpperCAmelCase							   =     dataavec_layer.self_attn.out_proj.bias
												_UpperCAmelCase							   =     dataavec_layer.self_attn_layer_norm.weight
												_UpperCAmelCase							   =     dataavec_layer.self_attn_layer_norm.bias

												# intermediate
												_UpperCAmelCase							   =     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}"""
												_UpperCAmelCase							   =     dataavec_layer.fca.weight
												_UpperCAmelCase							   =     dataavec_layer.fca.bias

												# output
												_UpperCAmelCase							   =     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}"""
												_UpperCAmelCase							   =     dataavec_layer.fca.weight
												_UpperCAmelCase							   =     dataavec_layer.fca.bias
												_UpperCAmelCase							   =     dataavec_layer.final_layer_norm.weight
												_UpperCAmelCase							   =     dataavec_layer.final_layer_norm.bias
												# end of layer

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

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

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

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


if __name__ == "__main__":
		_a       =    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.'''
		)
		_a       =    parser.parse_args()
		convert_dataavec_checkpoint_to_pytorch(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)








from __future__ import annotations


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						)->    list[float | int]:





						"""simple docstring"""



						if not all((nth_term, start_term_a, common_ratio_r)      ):
												return []
						_UpperCAmelCase							   =     []
						_UpperCAmelCase							   =     1
						_UpperCAmelCase							   =     common_ratio_r
						for _ in range(int(__lowerCAmelCase      )      ):
												if not series:
																		series.append(__lowerCAmelCase      )
												else:
																		power += 1
																		series.append(float(start_term_a * multiple      )      )
																		_UpperCAmelCase							   =     pow(float(__lowerCAmelCase      )					,						__lowerCAmelCase      )
						return series


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_a       =    float(input('''Enter the last number (n term) of the Geometric Series'''))
		_a       =    float(input('''Enter the starting term (a) of the Geometric Series'''))
		_a       =    float(
		    input('''Enter the common ratio between two terms (r) of the Geometric Series''')
		)
		print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
		print(geometric_series(nth_term, start_term_a, common_ratio_r))
