
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()
lowercase__							:							str													=     logging.get_logger(__name__)

lowercase__							:							Union[str, Any]													=     "Hello world! cécé herlolip"




def 		A_	(							snake_case							:				str	, snake_case							:				str	, snake_case							:				bool	) ->      Optional[int]:



						'''simple docstring'''

						__UpperCamelCase					,							__UpperCamelCase       					=     os.path.split(snake_case	)
						__UpperCamelCase       					=     DataaVecTextModel.from_pretrained(
						    snake_case	, checkpoint_file=snake_case	)
						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:'''	, snake_case	)

						__UpperCamelCase       					=     DataaVecTextForSequenceClassification(snake_case	) if classification_head else DataaVecTextForMaskedLM(snake_case	)
						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(snake_case	).unsqueeze(0	)  # batch of size 1

						__UpperCamelCase       					=     model(snake_case	)[0]
						if classification_head:
												__UpperCamelCase       					=     dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(snake_case	)	)
						else:
												__UpperCamelCase       					=     dataavec_model(snake_case	)[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(snake_case	, snake_case	, atol=1e-3	)
						print('''Do both models output the same tensors?'''	, '''🔥''' if success else '''💩'''	)
						if not success:
												raise Exception('''Something went wRoNg'''	)

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


if __name__ == "__main__":
				lowercase__							:							str													=     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."
				)
				lowercase__							:							Dict													=     parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)






def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				Tuple	, snake_case							:				Optional[Any]	) ->      Union[str, Any]:



						'''simple docstring'''

						assert b != 0, "This cannot accept modulo that is == 0"
						if n == 0:
												return 1

						if n % 2 == 1:
												return (bin_exp_mod(snake_case	, n - 1	, snake_case	) * a) % b

						__UpperCamelCase       					=     bin_exp_mod(snake_case	, n / 2	, snake_case	)
						return (r * r) % b


if __name__ == "__main__":
				try:
								lowercase__							:							Dict													=     int(input("Enter Base : ").strip())
								lowercase__							:							Dict													=     int(input("Enter Power : ").strip())
								lowercase__							:							Dict													=     int(input("Enter Modulo : ").strip())
				except ValueError:
								print("Invalid literal for integer")

				print(bin_exp_mod(BASE, POWER, MODULO))




