
"""simple docstring"""




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()
snake_case    =		logging.get_logger(__name__)

snake_case    =		'Hello world! cécé herlolip'

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
					roberta.eval()  # disable dropout
					SCREAMING_SNAKE_CASE				=	roberta.model.encoder.sentence_encoder
					SCREAMING_SNAKE_CASE				=	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=5_1_4,     type_vocab_size=1,     layer_norm_eps=1E-5,     )
					if classification_head:
										SCREAMING_SNAKE_CASE				=	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
					print('Our BERT config:',     SCREAMING_SNAKE_CASE_			)

					SCREAMING_SNAKE_CASE				=	RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_			) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_			)
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					SCREAMING_SNAKE_CASE				=	roberta_sent_encoder.embed_tokens.weight
					SCREAMING_SNAKE_CASE				=	roberta_sent_encoder.embed_positions.weight
					SCREAMING_SNAKE_CASE				=	torch.zeros_like(
					    model.roberta.embeddings.token_type_embeddings.weight			)  # just zero them out b/c RoBERTa doesn't use them.
					SCREAMING_SNAKE_CASE				=	roberta_sent_encoder.emb_layer_norm.weight
					SCREAMING_SNAKE_CASE				=	roberta_sent_encoder.emb_layer_norm.bias

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

										# self attention
										SCREAMING_SNAKE_CASE				=	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)			)
										)

										SCREAMING_SNAKE_CASE				=	roberta_layer.self_attn.q_proj.weight
										SCREAMING_SNAKE_CASE				=	roberta_layer.self_attn.q_proj.bias
										SCREAMING_SNAKE_CASE				=	roberta_layer.self_attn.k_proj.weight
										SCREAMING_SNAKE_CASE				=	roberta_layer.self_attn.k_proj.bias
										SCREAMING_SNAKE_CASE				=	roberta_layer.self_attn.v_proj.weight
										SCREAMING_SNAKE_CASE				=	roberta_layer.self_attn.v_proj.bias

										# self-attention output
										SCREAMING_SNAKE_CASE				=	layer.attention.output
										assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
										SCREAMING_SNAKE_CASE				=	roberta_layer.self_attn.out_proj.weight
										SCREAMING_SNAKE_CASE				=	roberta_layer.self_attn.out_proj.bias
										SCREAMING_SNAKE_CASE				=	roberta_layer.self_attn_layer_norm.weight
										SCREAMING_SNAKE_CASE				=	roberta_layer.self_attn_layer_norm.bias

										# intermediate
										SCREAMING_SNAKE_CASE				=	layer.intermediate
										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
										SCREAMING_SNAKE_CASE				=	roberta_layer.fca.weight
										SCREAMING_SNAKE_CASE				=	roberta_layer.fca.bias

										# output
										SCREAMING_SNAKE_CASE				=	layer.output
										assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
										SCREAMING_SNAKE_CASE				=	roberta_layer.fca.weight
										SCREAMING_SNAKE_CASE				=	roberta_layer.fca.bias
										SCREAMING_SNAKE_CASE				=	roberta_layer.final_layer_norm.weight
										SCREAMING_SNAKE_CASE				=	roberta_layer.final_layer_norm.bias
										# end of layer

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

					# Let's check that we get the same results.
					SCREAMING_SNAKE_CASE				=	roberta.encode(SCREAMING_SNAKE_CASE_			).unsqueeze(0			)  # batch of size 1

					SCREAMING_SNAKE_CASE				=	model(SCREAMING_SNAKE_CASE_			)[0]
					if classification_head:
										SCREAMING_SNAKE_CASE				=	roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE_			)			)
					else:
										SCREAMING_SNAKE_CASE				=	roberta.model(SCREAMING_SNAKE_CASE_			)[0]
					print(our_output.shape,     their_output.shape			)
					SCREAMING_SNAKE_CASE				=	torch.max(torch.abs(our_output - their_output			)			).item()
					print(f'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
					SCREAMING_SNAKE_CASE				=	torch.allclose(SCREAMING_SNAKE_CASE_,     SCREAMING_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(SCREAMING_SNAKE_CASE_			).mkdir(parents=SCREAMING_SNAKE_CASE_,     exist_ok=SCREAMING_SNAKE_CASE_			)
					print(f'''Saving model to {pytorch_dump_folder_path}'''			)
					model.save_pretrained(SCREAMING_SNAKE_CASE_			)


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


"""simple docstring"""




from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	from ..models.auto.modeling_auto import (
	    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
	    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
	    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
	    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
	)


snake_case    =		logging.get_logger(__name__)


snake_case    =		Dict[str, Any]
snake_case    =		List[Prediction]




@add_end_docstrings(__magic_name__      )
class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	*lowercase__		,	**lowercase__			)    ->    Dict:
									"""simple docstring"""







									super().__init__(*lowercase__		,	**lowercase__			)

									if self.framework == "tf":
														raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''			)

									requires_backends(self		,	'vision'			)
									self.check_model_type(
									    dict(
									        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()			)			)


				def 							A					(				self		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	{}
									SCREAMING_SNAKE_CASE				=	{}
									if "subtask" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['subtask']
														SCREAMING_SNAKE_CASE				=	kwargs['subtask']
									if "threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['threshold']
									if "mask_threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['mask_threshold']
									if "overlap_mask_area_threshold" in kwargs:
														SCREAMING_SNAKE_CASE				=	kwargs['overlap_mask_area_threshold']

									return preprocess_kwargs, {}, postprocess_kwargs


				def __call__(				self		,	lowercase__		,	**lowercase__			)    ->    Union[Predictions, List[Prediction]]:
									"""simple docstring"""







									return super().__call__(lowercase__		,	**lowercase__			)


				def 							A					(				self		,	lowercase__		,	lowercase__=None			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	load_image(lowercase__			)
									SCREAMING_SNAKE_CASE				=	[(image.height, image.width)]
									if self.model.config.__class__.__name__ == "OneFormerConfig":
														if subtask is None:
																			SCREAMING_SNAKE_CASE				=	{}
														else:
																			SCREAMING_SNAKE_CASE				=	{'task_inputs': [subtask]}
														SCREAMING_SNAKE_CASE				=	self.image_processor(images=[image]		,	return_tensors='pt'		,	**lowercase__			)
														SCREAMING_SNAKE_CASE				=	self.tokenizer(
														    inputs['task_inputs']		,	padding='max_length'		,	max_length=self.model.config.task_seq_len		,	return_tensors=self.framework		,	)['input_ids']
									else:
														SCREAMING_SNAKE_CASE				=	self.image_processor(images=[image]		,	return_tensors='pt'			)
									SCREAMING_SNAKE_CASE				=	target_size
									return inputs


				def 							A					(				self		,	lowercase__			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	model_inputs.pop('target_size'			)
									SCREAMING_SNAKE_CASE				=	self.model(**lowercase__			)
									SCREAMING_SNAKE_CASE				=	target_size
									return model_outputs




				def 							A					(				self		,	lowercase__		,	lowercase__=None		,	lowercase__=0.9		,	lowercase__=0.5		,	lowercase__=0.5			)    ->    Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	None
									if subtask in {"panoptic", None} and hasattr(self.image_processor		,	'post_process_panoptic_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_panoptic_segmentation
									elif subtask in {"instance", None} and hasattr(self.image_processor		,	'post_process_instance_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_instance_segmentation

									if fn is not None:
														SCREAMING_SNAKE_CASE				=	fn(
														    lowercase__		,	threshold=lowercase__		,	mask_threshold=lowercase__		,	overlap_mask_area_threshold=lowercase__		,	target_sizes=model_outputs['target_size']		,	)[0]

														SCREAMING_SNAKE_CASE				=	[]
														SCREAMING_SNAKE_CASE				=	outputs['segmentation']

														for segment in outputs["segments_info"]:
																			SCREAMING_SNAKE_CASE				=	(segmentation == segment['id']) * 255
																			SCREAMING_SNAKE_CASE				=	Image.fromarray(mask.numpy().astype(np.uinta			)		,	mode='L'			)
																			SCREAMING_SNAKE_CASE				=	self.model.config.idalabel[segment['label_id']]
																			SCREAMING_SNAKE_CASE				=	segment['score']
																			annotation.append({'score': score, 'label': label, 'mask': mask}			)

									elif subtask in {"semantic", None} and hasattr(self.image_processor		,	'post_process_semantic_segmentation'			):
														SCREAMING_SNAKE_CASE				=	self.image_processor.post_process_semantic_segmentation(
														    lowercase__		,	target_sizes=model_outputs['target_size']			)[0]

														SCREAMING_SNAKE_CASE				=	[]
														SCREAMING_SNAKE_CASE				=	outputs.numpy()
														SCREAMING_SNAKE_CASE				=	np.unique(lowercase__			)

														for label in labels:
																			SCREAMING_SNAKE_CASE				=	(segmentation == label) * 255
																			SCREAMING_SNAKE_CASE				=	Image.fromarray(mask.astype(np.uinta			)		,	mode='L'			)
																			SCREAMING_SNAKE_CASE				=	self.model.config.idalabel[label]
																			annotation.append({'score': None, 'label': label, 'mask': mask}			)
									else:
														raise ValueError(f'''Subtask {subtask} is not supported for model {type(self.model			)}'''			)
									return annotation
