





'''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_  :							List[str]            =	logging.get_logger(__name__)

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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      bool )			->						List[str]:
						UpperCAmelCase_      :   Dict      =	FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
						roberta.eval()  # disable dropout
						UpperCAmelCase_      :   Optional[Any]      =	roberta.model.encoder.sentence_encoder
						UpperCAmelCase_      :   Optional[int]      =	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:
												UpperCAmelCase_      :   Optional[Any]      =	roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
						print('''Our BERT config:''',      SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   Optional[Any]      =	RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__ ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__ )
						model.eval()

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

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

												# self attention
												UpperCAmelCase_      :   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) )
												)

												UpperCAmelCase_      :   List[str]      =	roberta_layer.self_attn.q_proj.weight
												UpperCAmelCase_      :   Union[str, Any]      =	roberta_layer.self_attn.q_proj.bias
												UpperCAmelCase_      :   Tuple      =	roberta_layer.self_attn.k_proj.weight
												UpperCAmelCase_      :   Dict      =	roberta_layer.self_attn.k_proj.bias
												UpperCAmelCase_      :   Dict      =	roberta_layer.self_attn.v_proj.weight
												UpperCAmelCase_      :   Optional[Any]      =	roberta_layer.self_attn.v_proj.bias

												# self-attention output
												UpperCAmelCase_      :   BertSelfOutput      =	layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												UpperCAmelCase_      :   Tuple      =	roberta_layer.self_attn.out_proj.weight
												UpperCAmelCase_      :   Dict      =	roberta_layer.self_attn.out_proj.bias
												UpperCAmelCase_      :   Tuple      =	roberta_layer.self_attn_layer_norm.weight
												UpperCAmelCase_      :   List[Any]      =	roberta_layer.self_attn_layer_norm.bias

												# intermediate
												UpperCAmelCase_      :   BertIntermediate      =	layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												UpperCAmelCase_      :   List[Any]      =	roberta_layer.fca.weight
												UpperCAmelCase_      :   List[str]      =	roberta_layer.fca.bias

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

						if classification_head:
												UpperCAmelCase_      :   Tuple      =	roberta.model.classification_heads['''mnli'''].dense.weight
												UpperCAmelCase_      :   Tuple      =	roberta.model.classification_heads['''mnli'''].dense.bias
												UpperCAmelCase_      :   str      =	roberta.model.classification_heads['''mnli'''].out_proj.weight
												UpperCAmelCase_      :   Optional[Any]      =	roberta.model.classification_heads['''mnli'''].out_proj.bias
						else:
												# LM Head
												UpperCAmelCase_      :   Optional[int]      =	roberta.model.encoder.lm_head.dense.weight
												UpperCAmelCase_      :   Any      =	roberta.model.encoder.lm_head.dense.bias
												UpperCAmelCase_      :   str      =	roberta.model.encoder.lm_head.layer_norm.weight
												UpperCAmelCase_      :   List[Any]      =	roberta.model.encoder.lm_head.layer_norm.bias
												UpperCAmelCase_      :   List[Any]      =	roberta.model.encoder.lm_head.weight
												UpperCAmelCase_      :   Tuple      =	roberta.model.encoder.lm_head.bias

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

						UpperCAmelCase_      :   Optional[int]      =	model(SCREAMING_SNAKE_CASE__ )[0]
						if classification_head:
												UpperCAmelCase_      :   List[Any]      =	roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE__ ) )
						else:
												UpperCAmelCase_      :   Tuple      =	roberta.model(SCREAMING_SNAKE_CASE__ )[0]
						print(our_output.shape,      their_output.shape )
						UpperCAmelCase_      :   Tuple      =	torch.max(torch.abs(our_output - their_output ) ).item()
						print(F"""max_absolute_diff = {max_absolute_diff}""" )  # ~ 1e-7
						UpperCAmelCase_      :   List[Any]      =	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_  :							Any            =	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_  :							Dict            =	parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)













import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size


class 		lowerCAmelCase_				(  unittest.TestCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=7							, _UpperCAmelCase=3							, _UpperCAmelCase=18							, _UpperCAmelCase=30							, _UpperCAmelCase=400							, _UpperCAmelCase=True							, _UpperCAmelCase=1 / 255							, _UpperCAmelCase=True							, _UpperCAmelCase=8							, )		->				Tuple:
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=image_size
        lowerCamelCase									=min_resolution
        lowerCamelCase									=max_resolution
        lowerCamelCase									=do_rescale
        lowerCamelCase									=rescale_factor
        lowerCamelCase									=do_pad
        lowerCamelCase									=pad_size





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=False  )		->				int:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              image_inputs.append(
                  np.random.randint(
                      255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta  )  )
        else:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              lowerCamelCase				,       lowerCamelCase									=np.random.choice(np.arange(self.min_resolution							, self.max_resolution  )							, 2  )
              image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta  )  )

        if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           lowerCamelCase									=[Image.fromarray(np.moveaxis(_UpperCAmelCase							, 0							, -1  )  ) for x in image_inputs]

        if torchify:
           lowerCamelCase									=[torch.from_numpy(_UpperCAmelCase  ) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[Any] 	=     SwinaSRImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=SwinaSRImageProcessingTester(self  )





     @property
     def 						lowerCamelCase							(	self  )		->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_rescale"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """rescale_factor"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_pad"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """pad_size"""  )  )





     def 						lowerCamelCase							(	self  )		->				Any:
        pass





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=get_image_size(_UpperCAmelCase  )
        lowerCamelCase									=self.image_processor_tester.pad_size

        lowerCamelCase									=(old_height // size + 1) * size - old_height
        lowerCamelCase									=(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(np.array(image_inputs[0]  )  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Any:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, numpify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, np.ndarray  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, torchify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, torch.Tensor  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )