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()
UpperCamelCase      			=     logging.get_logger(__name__)

UpperCamelCase      			=     'Hello world! cécé herlolip'



def    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase    )							->	Optional[int]:
		__A							:   Dict	  =				FairseqRobertaModel.from_pretrained(_lowercase    )
		roberta.eval()  # disable dropout
		__A							:   Optional[int]	  =				roberta.model.encoder.sentence_encoder
		__A							:   Tuple	  =				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=514 ,     type_vocab_size=1 ,     layer_norm_eps=1E-5 ,     )
		if classification_head:
				__A							:   Any	  =				roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
		print("Our BERT config:" ,     _lowercase    )

		__A							:   Tuple	  =				RobertaForSequenceClassification(_lowercase    ) if classification_head else RobertaForMaskedLM(_lowercase    )
		model.eval()

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

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

				# self attention
				__A							:   BertSelfAttention	  =				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							:   List[str]	  =				roberta_layer.self_attn.q_proj.weight
				__A							:   Any	  =				roberta_layer.self_attn.q_proj.bias
				__A							:   int	  =				roberta_layer.self_attn.k_proj.weight
				__A							:   int	  =				roberta_layer.self_attn.k_proj.bias
				__A							:   List[Any]	  =				roberta_layer.self_attn.v_proj.weight
				__A							:   List[Any]	  =				roberta_layer.self_attn.v_proj.bias

				# self-attention output
				__A							:   BertSelfOutput	  =				layer.attention.output
				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
				__A							:   int	  =				roberta_layer.self_attn.out_proj.weight
				__A							:   List[Any]	  =				roberta_layer.self_attn.out_proj.bias
				__A							:   Dict	  =				roberta_layer.self_attn_layer_norm.weight
				__A							:   Union[str, Any]	  =				roberta_layer.self_attn_layer_norm.bias

				# intermediate
				__A							:   BertIntermediate	  =				layer.intermediate
				assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
				__A							:   Any	  =				roberta_layer.fca.weight
				__A							:   Tuple	  =				roberta_layer.fca.bias

				# output
				__A							:   BertOutput	  =				layer.output
				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
				__A							:   Optional[Any]	  =				roberta_layer.fca.weight
				__A							:   str	  =				roberta_layer.fca.bias
				__A							:   Optional[int]	  =				roberta_layer.final_layer_norm.weight
				__A							:   int	  =				roberta_layer.final_layer_norm.bias
				# end of layer

		if classification_head:
				__A							:   Any	  =				roberta.model.classification_heads["mnli"].dense.weight
				__A							:   Tuple	  =				roberta.model.classification_heads["mnli"].dense.bias
				__A							:   Optional[int]	  =				roberta.model.classification_heads["mnli"].out_proj.weight
				__A							:   Any	  =				roberta.model.classification_heads["mnli"].out_proj.bias
		else:
				# LM Head
				__A							:   int	  =				roberta.model.encoder.lm_head.dense.weight
				__A							:   Tuple	  =				roberta.model.encoder.lm_head.dense.bias
				__A							:   Optional[int]	  =				roberta.model.encoder.lm_head.layer_norm.weight
				__A							:   Any	  =				roberta.model.encoder.lm_head.layer_norm.bias
				__A							:   Any	  =				roberta.model.encoder.lm_head.weight
				__A							:   Union[str, Any]	  =				roberta.model.encoder.lm_head.bias

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

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

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


if __name__ == "__main__":
					UpperCamelCase      			=     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.'
					)
					UpperCamelCase      			=     parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .image_encoder import PaintByExampleImageEncoder
					from .pipeline_paint_by_example import PaintByExamplePipeline





