




"""simple docstring"""




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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViltImageProcessor
class 		UpperCAmelCase       (    unittest.TestCase      ):







						'''simple docstring'''





						def __init__(						self							:					Optional[int]	,     __lowercase							:					int	,     __lowercase							:					Union[str, Any]=7	,     __lowercase							:					List[Any]=3	,     __lowercase							:					Optional[int]=18	,     __lowercase							:					int=30	,     __lowercase							:					List[str]=4_00	,     __lowercase							:					Dict=True	,     __lowercase							:					Optional[int]=None	,     __lowercase							:					str=2	,     __lowercase							:					Dict=True	,     __lowercase							:					Optional[Any]=[0.5, 0.5, 0.5]	,     __lowercase							:					Any=[0.5, 0.5, 0.5]	,     ):






							"""simple docstring"""






							snake_case_		   =					size if size is not None else {"shortest_edge": 30}
							snake_case_		   =					parent
							snake_case_		   =					batch_size
							snake_case_		   =					num_channels
							snake_case_		   =					image_size
							snake_case_		   =					min_resolution
							snake_case_		   =					max_resolution
							snake_case_		   =					do_resize
							snake_case_		   =					size
							snake_case_		   =					size_divisor
							snake_case_		   =					do_normalize
							snake_case_		   =					image_mean
							snake_case_		   =					image_std






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "size_divisor": self.size_divisor,
							}






						def 	snake_case__						(						self							:					int	,     __lowercase							:					List[Any]	,     __lowercase							:					int=False						):






							"""simple docstring"""






							if not batched:
								snake_case_		   =					self.size["shortest_edge"]
								snake_case_		   =					image_inputs[0]
								if isinstance(__UpperCamelCase	,     Image.Image						):
									snake_case_   ,				snake_case_		   =					image.size
								else:
									snake_case_   ,				snake_case_		   =					image.shape[1], image.shape[2]
								snake_case_		   =					size / min(__UpperCamelCase	,     __UpperCamelCase						)
								if h < w:
									snake_case_   ,				snake_case_		   =					size, scale * w
								else:
									snake_case_   ,				snake_case_		   =					scale * h, size

								snake_case_		   =					int((13_33 / 8_00) * size						)
								if max(__UpperCamelCase	,     __UpperCamelCase						) > max_size:
									snake_case_		   =					max_size / max(__UpperCamelCase	,     __UpperCamelCase						)
									snake_case_		   =					newh * scale
									snake_case_		   =					neww * scale

								snake_case_   ,				snake_case_		   =					int(newh + 0.5						), int(neww + 0.5						)
								snake_case_   ,				snake_case_		   =					(
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

							else:
								snake_case_		   =					[]
								for image in image_inputs:
									snake_case_   ,				snake_case_		   =					self.get_expected_values([image]						)
									expected_values.append((expected_height, expected_width)						)
								snake_case_		   =					max(__UpperCamelCase	,     key=lambda __lowercase						: item[0]						)[0]
								snake_case_		   =					max(__UpperCamelCase	,     key=lambda __lowercase						: item[1]						)[1]

							return expected_height, expected_width







@require_torch
@require_vision
class 		UpperCAmelCase       (    UpperCAmelCase_	,       unittest.TestCase      ):







						'''simple docstring'''





						lowerCAmelCase_							=       ViltImageProcessor if is_vision_available() else None






						def 	snake_case__						(						self							:					str						):






							"""simple docstring"""






							snake_case_		   =					ViltImageProcessingTester(self						)






						@property
						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()






						def 	snake_case__						(						self							:					str						):






							"""simple docstring"""






							snake_case_		   =					self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__UpperCamelCase	,     "image_mean"						)						)
							self.assertTrue(hasattr(__UpperCamelCase	,     "image_std"						)						)
							self.assertTrue(hasattr(__UpperCamelCase	,     "do_normalize"						)						)
							self.assertTrue(hasattr(__UpperCamelCase	,     "do_resize"						)						)
							self.assertTrue(hasattr(__UpperCamelCase	,     "size"						)						)
							self.assertTrue(hasattr(__UpperCamelCase	,     "size_divisor"						)						)






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							snake_case_		   =					self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size	,     {"shortest_edge": 30}						)

							snake_case_		   =					self.image_processing_class.from_dict(self.image_processor_dict	,     size=42						)
							self.assertEqual(image_processor.size	,     {"shortest_edge": 42}						)






						def 	snake_case__						(						self							:					Tuple						):






							"""simple docstring"""






							pass






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






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

							# Test not batched input
							snake_case_		   =					image_processing(image_inputs[0]	,     return_tensors="pt"						).pixel_values

							snake_case_   ,				snake_case_		   =					self.image_processor_tester.get_expected_values(__UpperCamelCase						)
							self.assertEqual(
							    encoded_images.shape	,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,     )

							# Test batched
							snake_case_		   =					image_processing(__UpperCamelCase	,     return_tensors="pt"						).pixel_values

							snake_case_   ,				snake_case_		   =					self.image_processor_tester.get_expected_values(__UpperCamelCase	,     batched=__UpperCamelCase						)
							self.assertEqual(
							    encoded_images.shape	,     (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,     )






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






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

							# Test not batched input
							snake_case_		   =					image_processing(image_inputs[0]	,     return_tensors="pt"						).pixel_values

							snake_case_   ,				snake_case_		   =					self.image_processor_tester.get_expected_values(__UpperCamelCase						)
							self.assertEqual(
							    encoded_images.shape	,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,     )

							# Test batched
							snake_case_		   =					image_processing(__UpperCamelCase	,     return_tensors="pt"						).pixel_values

							snake_case_   ,				snake_case_		   =					self.image_processor_tester.get_expected_values(__UpperCamelCase	,     batched=__UpperCamelCase						)
							self.assertEqual(
							    encoded_images.shape	,     (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,     )






						def 	snake_case__						(						self							:					Any						):






							"""simple docstring"""






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

							# Test not batched input
							snake_case_		   =					image_processing(image_inputs[0]	,     return_tensors="pt"						).pixel_values

							snake_case_   ,				snake_case_		   =					self.image_processor_tester.get_expected_values(__UpperCamelCase						)
							self.assertEqual(
							    encoded_images.shape	,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,     )

							# Test batched
							snake_case_		   =					image_processing(__UpperCamelCase	,     return_tensors="pt"						).pixel_values

							snake_case_   ,				snake_case_		   =					self.image_processor_tester.get_expected_values(__UpperCamelCase	,     batched=__UpperCamelCase						)
							self.assertEqual(
							    encoded_images.shape	,     (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )	,     )






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()
lowercase__    :					Dict					=						logging.get_logger(__name__)

lowercase__    :					Any					=						"Hello world! cécé herlolip"


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					FairseqRobertaModel.from_pretrained(_A							)
	roberta.eval()  # disable dropout
	snake_case_		   =					roberta.model.encoder.sentence_encoder
	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=514					,     type_vocab_size=1					,     layer_norm_eps=1E-5					,     )
	if classification_head:
		snake_case_		   =					roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
	print("Our BERT config:"					,     _A							)

	snake_case_		   =					RobertaForSequenceClassification(_A							) if classification_head else RobertaForMaskedLM(_A							)
	model.eval()

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

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

		# self attention
		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)							)
		)

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

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

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

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

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

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

	snake_case_		   =					model(_A							)[0]
	if classification_head:
		snake_case_		   =					roberta.model.classification_heads["mnli"](roberta.extract_features(_A							)							)
	else:
		snake_case_		   =					roberta.model(_A							)[0]
	print(our_output.shape					,     their_output.shape							)
	snake_case_		   =					torch.max(torch.abs(our_output - their_output							)							).item()
	print(f"max_absolute_diff = {max_absolute_diff}"							)  # ~ 1e-7
	snake_case_		   =					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__":
		lowercase__    :					int					=						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."
		)
		lowercase__    :					Optional[Any]					=						parser.parse_args()
		convert_roberta_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)
