
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()
_A  =	logging.get_logger(__name__)

_A  =	"Hello world! cécé herlolip"






def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    bool							):




			"""simple docstring"""


			lowerCAmelCase_         =      FairseqRobertaModel.from_pretrained(__lowerCAmelCase							)
			roberta.eval()  # disable dropout
			lowerCAmelCase_         =      roberta.model.encoder.sentence_encoder
			lowerCAmelCase_         =      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:
						lowerCAmelCase_         =      roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("Our RoBERTa config:"       ,				__lowerCAmelCase							)

			lowerCAmelCase_         =      XLMRobertaXLForSequenceClassification(__lowerCAmelCase							) if classification_head else XLMRobertaXLForMaskedLM(__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.layer_norm.weight
			lowerCAmelCase_         =      roberta_sent_encoder.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]

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

						# 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

						# this one is final layer norm
						lowerCAmelCase_         =      roberta_layer.final_layer_norm.weight
						lowerCAmelCase_         =      roberta_layer.final_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
						# 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__":
			_A  =	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."
			)
			_A  =	parser.parse_args()
			convert_xlm_roberta_xl_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 ChineseCLIPImageProcessor





class       _lowerCAmelCase   (      unittest.TestCase			):




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=7							,							_UpperCamelCase=3							,							_UpperCamelCase=18							,							_UpperCamelCase=30							,							_UpperCamelCase=400							,							_UpperCamelCase=True							,							_UpperCamelCase=None							,							_UpperCamelCase=True							,							_UpperCamelCase=None							,							_UpperCamelCase=True							,							_UpperCamelCase=[0.48145466, 0.4578275, 0.40821073]							,							_UpperCamelCase=[0.26862954, 0.26130258, 0.27577711]							,							_UpperCamelCase=True							,							)							-> Dict:
							lowerCAmelCase_         =      size if size is not None else {"height": 224, "width": 224}
							lowerCAmelCase_         =      crop_size if crop_size is not None else {"height": 18, "width": 18}
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      num_channels
							lowerCAmelCase_         =      image_size
							lowerCAmelCase_         =      min_resolution
							lowerCAmelCase_         =      max_resolution
							lowerCAmelCase_         =      do_resize
							lowerCAmelCase_         =      size
							lowerCAmelCase_         =      do_center_crop
							lowerCAmelCase_         =      crop_size
							lowerCAmelCase_         =      do_normalize
							lowerCAmelCase_         =      image_mean
							lowerCAmelCase_         =      image_std
							lowerCAmelCase_         =      do_convert_rgb




				def 	__a		(   self	)							-> List[Any]:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_convert_rgb": self.do_convert_rgb,
							}




				def 	__a		(   self							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							_UpperCamelCase=False	)							-> Dict:
							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   (      __a   , unittest.TestCase			):
				_lowercase     						=ChineseCLIPImageProcessor if is_vision_available() else None




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      ChineseCLIPImageProcessingTester(self							,							do_center_crop=_UpperCamelCase	)




				@property
				def 	__a		(   self	)							-> Tuple:
							return self.image_processor_tester.prepare_image_processor_dict()




				def 	__a		(   self	)							-> Any:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_resize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"size"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_normalize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_mean"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_std"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_convert_rgb"	)	)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.image_processing_class.from_dict(self.image_processor_dict	)
							self.assertEqual(image_processor.size							,							{"height": 224, "width": 224}	)
							self.assertEqual(image_processor.crop_size							,							{"height": 18, "width": 18}	)

							lowerCAmelCase_         =      self.image_processing_class.from_dict(self.image_processor_dict							,							size=42							,							crop_size=84	)
							self.assertEqual(image_processor.size							,							{"shortest_edge": 42}	)
							self.assertEqual(image_processor.crop_size							,							{"height": 84, "width": 84}	)




				def 	__a		(   self	)							-> str:
							pass




				def 	__a		(   self	)							-> Any:
							# Initialize image_processing
							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_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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)

							# Test batched
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)




				def 	__a		(   self	)							-> str:
							# Initialize image_processing
							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_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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)

							# Test batched
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)




				def 	__a		(   self	)							-> Union[str, Any]:
							# Initialize image_processing
							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_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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)

							# Test batched
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							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.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)





@require_torch
@require_vision
class       _lowerCAmelCase   (      __a   , unittest.TestCase			):
				_lowercase     						=ChineseCLIPImageProcessor if is_vision_available() else None




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      ChineseCLIPImageProcessingTester(self							,							num_channels=4							,							do_center_crop=_UpperCamelCase	)
							lowerCAmelCase_         =      3




				@property
				def 	__a		(   self	)							-> List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()




				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_resize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"size"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"center_crop"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_normalize"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_mean"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"image_std"	)	)
							self.assertTrue(hasattr(_UpperCamelCase							,							"do_convert_rgb"	)	)




				def 	__a		(   self	)							-> int:
							pass




				def 	__a		(   self	)							-> str:
							# Initialize image_processing
							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_processing(image_inputs[0]							,							return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,							(
							        1,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)

							# Test batched
							lowerCAmelCase_         =      image_processing(_UpperCamelCase							,							return_tensors="pt"	).pixel_values
							self.assertEqual(
							    encoded_images.shape							,							(
							        self.image_processor_tester.batch_size,
							        self.expected_encoded_image_num_channels,
							        self.image_processor_tester.crop_size["height"],
							        self.image_processor_tester.crop_size["width"],
							    )							,							)



