

'''simple docstring'''

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       :      Dict  	=			logging.get_logger(__name__)

a       :      Optional[Any]  	=			"Hello world! cécé herlolip"





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Tuple        =       os.path.split(__magic_name__							)
					UpperCAmelCase		:							List[str]        =       DataaVecTextModel.from_pretrained(
					    __magic_name__			,   checkpoint_file=__magic_name__							)
					dataavec.eval()  # disable dropout
					UpperCAmelCase		:							List[Any]        =       dataavec.models[0]
					UpperCAmelCase		:							str        =       dataavec_model.encoder.sentence_encoder
					UpperCAmelCase		:							List[str]        =       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		:							Dict        =       dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
					print("Our BERT config:"			,   __magic_name__							)

					UpperCAmelCase		:							Dict        =       DataaVecTextForSequenceClassification(__magic_name__							) if classification_head else DataaVecTextForMaskedLM(__magic_name__							)
					model.eval()

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

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

										# self attention
										UpperCAmelCase		:							BertSelfAttention        =       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		:							Union[str, Any]        =       dataavec_layer.self_attn.q_proj.weight
										UpperCAmelCase		:							Any        =       dataavec_layer.self_attn.q_proj.bias
										UpperCAmelCase		:							Any        =       dataavec_layer.self_attn.k_proj.weight
										UpperCAmelCase		:							Optional[int]        =       dataavec_layer.self_attn.k_proj.bias
										UpperCAmelCase		:							Union[str, Any]        =       dataavec_layer.self_attn.v_proj.weight
										UpperCAmelCase		:							Dict        =       dataavec_layer.self_attn.v_proj.bias

										# self-attention output
										UpperCAmelCase		:							BertSelfOutput        =       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		:							Dict        =       dataavec_layer.self_attn.out_proj.weight
										UpperCAmelCase		:							int        =       dataavec_layer.self_attn.out_proj.bias
										UpperCAmelCase		:							Optional[Any]        =       dataavec_layer.self_attn_layer_norm.weight
										UpperCAmelCase		:							Tuple        =       dataavec_layer.self_attn_layer_norm.bias

										# intermediate
										UpperCAmelCase		:							BertIntermediate        =       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		:							Tuple        =       dataavec_layer.fca.weight
										UpperCAmelCase		:							Optional[Any]        =       dataavec_layer.fca.bias

										# output
										UpperCAmelCase		:							BertOutput        =       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		:							List[str]        =       dataavec_layer.fca.weight
										UpperCAmelCase		:							Union[str, Any]        =       dataavec_layer.fca.bias
										UpperCAmelCase		:							List[str]        =       dataavec_layer.final_layer_norm.weight
										UpperCAmelCase		:							int        =       dataavec_layer.final_layer_norm.bias
										# end of layer

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

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

					UpperCAmelCase		:							Dict        =       model(__magic_name__							)[0]
					if classification_head:
										UpperCAmelCase		:							Any        =       dataavec.model.classification_heads["mnli"](dataavec.extract_features(__magic_name__							)							)
					else:
										UpperCAmelCase		:							List[Any]        =       dataavec_model(__magic_name__							)[0]
					print(our_output.shape			,   their_output.shape							)
					UpperCAmelCase		:							Optional[int]        =       torch.max(torch.abs(our_output - their_output							)							).item()
					print(F"max_absolute_diff = {max_absolute_diff}"							)  # ~ 1e-7
					UpperCAmelCase		:							Dict        =       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__":
						a       :      int  	=			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       :      Dict  	=			parser.parse_args()
						convert_dataavec_checkpoint_to_pytorch(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)



'''simple docstring'''

from ..utils import DummyObject, requires_backends







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







class   UpperCamelCase__   (       metaclass=lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     int       =							["torch", "transformers", "invisible_watermark"]







		def __init__(			self   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(self   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )







		@classmethod
		def 				A_	(			cls   ,      *snake_case   ,      **snake_case      ):


							'''simple docstring'''

							requires_backends(cls   ,      ["torch", "transformers", "invisible_watermark"]      )
