
'''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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
						raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
lowerCAmelCase					:    Any           = logging.get_logger(__name__)

lowerCAmelCase					:    Union[str, Any]           = 'Hello world! cécé herlolip'







def 		A_(     A						:		str		,     A						:		str		,     A						:		bool):
							UpperCamelCase   			=     FairseqRobertaModel.from_pretrained(A)
							roberta.eval()  # disable dropout
							UpperCamelCase   			=     roberta.model.encoder.sentence_encoder
							UpperCamelCase   			=     XLMRobertaConfig(
							    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings		,     hidden_size=roberta.cfg.model.encoder_embed_dim		,     num_hidden_layers=roberta.cfg.model.encoder_layers		,     num_attention_heads=roberta.cfg.model.encoder_attention_heads		,     intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim		,     max_position_embeddings=514		,     type_vocab_size=1		,     layer_norm_eps=1E-5		,     )
							if classification_head:
														UpperCamelCase   			=     roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

							print('Our RoBERTa config:'		,     A)

							UpperCamelCase   			=     XLMRobertaXLForSequenceClassification(A) if classification_head else XLMRobertaXLForMaskedLM(A)
							model.eval()

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

							UpperCamelCase   			=     roberta_sent_encoder.layer_norm.weight
							UpperCamelCase   			=     roberta_sent_encoder.layer_norm.bias

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

														UpperCamelCase   			=     layer.attention
														UpperCamelCase   			=     roberta_layer.self_attn_layer_norm.weight
														UpperCamelCase   			=     roberta_layer.self_attn_layer_norm.bias

														# self attention
														UpperCamelCase   			=     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   			=     roberta_layer.self_attn.q_proj.weight
														UpperCamelCase   			=     roberta_layer.self_attn.q_proj.bias
														UpperCamelCase   			=     roberta_layer.self_attn.k_proj.weight
														UpperCamelCase   			=     roberta_layer.self_attn.k_proj.bias
														UpperCamelCase   			=     roberta_layer.self_attn.v_proj.weight
														UpperCamelCase   			=     roberta_layer.self_attn.v_proj.bias

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

														# this one is final layer norm
														UpperCamelCase   			=     roberta_layer.final_layer_norm.weight
														UpperCamelCase   			=     roberta_layer.final_layer_norm.bias

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

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

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

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

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

							pathlib.Path(A).mkdir(parents=A		,     exist_ok=A)
							print(f'''Saving model to {pytorch_dump_folder_path}''')
							model.save_pretrained(A)


if __name__ == "__main__":
						lowerCAmelCase					:    Union[str, 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.'
						)
						lowerCAmelCase					:    List[str]           = parser.parse_args()
						convert_xlm_roberta_xl_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)






'''simple docstring'''
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DonutImageProcessor


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def __init__(       self						,				A_						,				A_=7						,				A_=3						,				A_=18						,				A_=30						,				A_=400						,				A_=True						,				A_=None						,				A_=True						,				A_=False						,				A_=True						,				A_=True						,				A_=[0.5, 0.5, 0.5]						,				A_=[0.5, 0.5, 0.5]						,				)->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     num_channels
											UpperCamelCase   			=     image_size
											UpperCamelCase   			=     min_resolution
											UpperCamelCase   			=     max_resolution
											UpperCamelCase   			=     do_resize
											UpperCamelCase   			=     size if size is not None else {'height': 18, 'width': 20}
											UpperCamelCase   			=     do_thumbnail
											UpperCamelCase   			=     do_align_axis
											UpperCamelCase   			=     do_pad
											UpperCamelCase   			=     do_normalize
											UpperCamelCase   			=     image_mean
											UpperCamelCase   			=     image_std







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_thumbnail": self.do_thumbnail,
											    "do_align_long_axis": self.do_align_axis,
											    "do_pad": self.do_pad,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							DonutImageProcessor if is_vision_available() else None







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     DonutImageProcessingTester(self     )







				@property
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(A_						,				'do_resize'     )     )
											self.assertTrue(hasattr(A_						,				'size'     )     )
											self.assertTrue(hasattr(A_						,				'do_thumbnail'     )     )
											self.assertTrue(hasattr(A_						,				'do_align_long_axis'     )     )
											self.assertTrue(hasattr(A_						,				'do_pad'     )     )
											self.assertTrue(hasattr(A_						,				'do_normalize'     )     )
											self.assertTrue(hasattr(A_						,				'image_mean'     )     )
											self.assertTrue(hasattr(A_						,				'image_std'     )     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size						,				{'height': 18, 'width': 20}     )

											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=42     )
											self.assertEqual(image_processor.size						,				{'height': 42, 'width': 42}     )

											# Previous config had dimensions in (width, height) order
											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=(42, 84)     )
											self.assertEqual(image_processor.size						,				{'height': 84, 'width': 42}     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											pass







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				Image.Image     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random numpy tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				numpify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				np.ndarray     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PyTorch tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				torchify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				torch.Tensor     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)




