




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    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case				):
							lowerCAmelCase =		FairseqRobertaModel.from_pretrained(_lowerCAmelCase				)
							roberta.eval()  # disable dropout
							lowerCAmelCase =		roberta.model.encoder.sentence_encoder
							lowerCAmelCase =		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:
														lowerCAmelCase =		roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
							print('''Our BERT config:'''    ,			_lowerCAmelCase				)

							lowerCAmelCase =		RobertaForSequenceClassification(_lowerCAmelCase				) if classification_head else RobertaForMaskedLM(_lowerCAmelCase				)
							model.eval()

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

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

														# self attention
														lowerCAmelCase =		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)				)
														)

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

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

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

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

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

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

							lowerCAmelCase =		model(_lowerCAmelCase				)[0]
							if classification_head:
														lowerCAmelCase =		roberta.model.classification_heads['''mnli'''](roberta.extract_features(_lowerCAmelCase				)				)
							else:
														lowerCAmelCase =		roberta.model(_lowerCAmelCase				)[0]
							print(our_output.shape    ,			their_output.shape				)
							lowerCAmelCase =		torch.max(torch.abs(our_output - their_output				)				).item()
							print(F"""max_absolute_diff = {max_absolute_diff}"""				)  # ~ 1e-7
							lowerCAmelCase =		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__":
		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
		)







"""simple docstring"""






import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


__snake_case												=							logging.get_logger(__name__)
def    A_ ( _lowerCAmelCase  :       Optional[int],  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					nn.functional.normalize(_lowerCAmelCase							)
     _a     						=					nn.functional.normalize(_lowerCAmelCase							)
     return torch.mm(_lowerCAmelCase,  normalized_text_embeds.t()							)


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						str     =		CLIPConfig

     A_							:						List[str]     =		['CLIPEncoderLayer']


     def __init__(   self     ,					__UpperCAmelCase				)							->				List[Any]:
          super().__init__(__UpperCAmelCase				)

          _a     						=					CLIPVisionModel(config.vision_config				)
          _a     						=					nn.Linear(config.vision_config.hidden_size     ,					config.projection_dim     ,					bias=__UpperCAmelCase				)

          _a     						=					nn.Parameter(torch.ones(17     ,					config.projection_dim				)     ,					requires_grad=__UpperCAmelCase				)
          _a     						=					nn.Parameter(torch.ones(3     ,					config.projection_dim				)     ,					requires_grad=__UpperCAmelCase				)

          _a     						=					nn.Parameter(torch.ones(17				)     ,					requires_grad=__UpperCAmelCase				)
          _a     						=					nn.Parameter(torch.ones(3				)     ,					requires_grad=__UpperCAmelCase				)


     @torch.no_grad()
     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Optional[Any]:
          _a     						=					self.vision_model(__UpperCAmelCase				)[1]  # pooled_output
          _a     						=					self.visual_projection(__UpperCAmelCase				)

          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          _a     						=					cosine_distance(__UpperCAmelCase     ,					self.special_care_embeds				).cpu().float().numpy()
          _a     						=					cosine_distance(__UpperCAmelCase     ,					self.concept_embeds				).cpu().float().numpy()

          _a     						=					[]
          _a     						=					image_embeds.shape[0]
          for i in range(__UpperCAmelCase				):
               _a     						=					{'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}

               # increase this value to create a stronger `nfsw` filter
               # at the cost of increasing the possibility of filtering benign images
               _a     						=					0.0

               for concept_idx in range(len(special_cos_dist[0]				)				):
                    _a     						=					special_cos_dist[i][concept_idx]
                    _a     						=					self.special_care_embeds_weights[concept_idx].item()
                    _a     						=					round(concept_cos - concept_threshold + adjustment     ,					3				)
                    if result_img["special_scores"][concept_idx] > 0:
                         result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]}				)
                         _a     						=					0.01

               for concept_idx in range(len(cos_dist[0]				)				):
                    _a     						=					cos_dist[i][concept_idx]
                    _a     						=					self.concept_embeds_weights[concept_idx].item()
                    _a     						=					round(concept_cos - concept_threshold + adjustment     ,					3				)
                    if result_img["concept_scores"][concept_idx] > 0:
                         result_img["bad_concepts"].append(__UpperCAmelCase				)

               result.append(__UpperCAmelCase				)

          _a     						=					[len(res['''bad_concepts''']				) > 0 for res in result]

          for idx, has_nsfw_concept in enumerate(__UpperCAmelCase				):
               if has_nsfw_concept:
                    if torch.is_tensor(__UpperCAmelCase				) or torch.is_tensor(images[0]				):
                         _a     						=					torch.zeros_like(images[idx]				)  # black image
                    else:
                         _a     						=					np.zeros(images[idx].shape				)  # black image

          if any(__UpperCAmelCase				):
               logger.warning(
                   '''Potential NSFW content was detected in one or more images. A black image will be returned instead.'''
                   ''' Try again with a different prompt and/or seed.'''				)

          return images, has_nsfw_concepts





     @torch.no_grad()
     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Any:
          _a     						=					self.vision_model(__UpperCAmelCase				)[1]  # pooled_output
          _a     						=					self.visual_projection(__UpperCAmelCase				)

          _a     						=					cosine_distance(__UpperCAmelCase     ,					self.special_care_embeds				)
          _a     						=					cosine_distance(__UpperCAmelCase     ,					self.concept_embeds				)

          # increase this value to create a stronger `nsfw` filter
          # at the cost of increasing the possibility of filtering benign images
          _a     						=					0.0

          _a     						=					special_cos_dist - self.special_care_embeds_weights + adjustment
          # special_scores = special_scores.round(decimals=3)
          _a     						=					torch.any(special_scores > 0     ,					dim=1				)
          _a     						=					special_care * 0.01
          _a     						=					special_adjustment.unsqueeze(1				).expand(-1     ,					cos_dist.shape[1]				)

          _a     						=					(cos_dist - self.concept_embeds_weights) + special_adjustment
          # concept_scores = concept_scores.round(decimals=3)
          _a     						=					torch.any(concept_scores > 0     ,					dim=1				)

          _a     						=					0.0  # black image

          return images, has_nsfw_concepts