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

lowercase__		:Union[str, Any]   =    "Hello world! cécé herlolip"



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						FairseqRobertaModel.from_pretrained(lowerCAmelCase__		)
					roberta.eval()  # disable dropout
					lowercase							=						roberta.model.encoder.sentence_encoder
					lowercase							=						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:
										lowercase							=						roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

					print('''Our RoBERTa config:''' ,			lowerCAmelCase__		)

					lowercase							=						XLMRobertaXLForSequenceClassification(lowerCAmelCase__		) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase__		)
					model.eval()

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

					lowercase							=						roberta_sent_encoder.layer_norm.weight
					lowercase							=						roberta_sent_encoder.layer_norm.bias

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

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

										# self attention
										lowercase							=						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)		)
										)

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

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

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

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

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

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

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

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









import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor



@require_vision
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   tempfile.mkdtemp()

						a					     =   [
						    "[UNK]",
						    "[CLS]",
						    "[SEP]",
						    "[PAD]",
						    "[MASK]",
						    "的",
						    "价",
						    "格",
						    "是",
						    "15",
						    "便",
						    "alex",
						    "##andra",
						    "，",
						    "。",
						    "-",
						    "t",
						    "shirt",
						]
						a					     =   os.path.join(self.tmpdirname		,       VOCAB_FILES_NAMES["vocab_file"]		)
						with open(self.vocab_file		,       "w"		,       encoding="utf-8"		) as vocab_writer:
											vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)

						a					     =   {
						    "do_resize": True,
						    "size": {"height": 224, "width": 224},
						    "do_center_crop": True,
						    "crop_size": {"height": 18, "width": 18},
						    "do_normalize": True,
						    "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
						    "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
						    "do_convert_rgb": True,
						}
						a					     =   os.path.join(self.tmpdirname		,       lowerCamelCase_		)
						with open(self.image_processor_file		,       "w"		,       encoding="utf-8"		) as fp:
											json.dump(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						return BertTokenizer.from_pretrained(self.tmpdirname		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						return BertTokenizerFast.from_pretrained(self.tmpdirname		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						shutil.rmtree(self.tmpdirname		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   [np.random.randint(255		,       size=(3, 30, 400)		,       dtype=np.uinta		)]

						a					     =   [Image.fromarray(np.moveaxis(lowerCamelCase_		,       0		,       -1		)		) for x in image_inputs]

						return image_inputs




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_tokenizer()
						a					     =   self.get_rust_tokenizer()
						a					     =   self.get_image_processor()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)
						processor_slow.save_pretrained(self.tmpdirname		)
						a					     =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,       use_fast=lowerCamelCase_		)

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)
						processor_fast.save_pretrained(self.tmpdirname		)
						a					     =   ChineseCLIPProcessor.from_pretrained(self.tmpdirname		)

						self.assertEqual(processor_slow.tokenizer.get_vocab()		,       tokenizer_slow.get_vocab()		)
						self.assertEqual(processor_fast.tokenizer.get_vocab()		,       tokenizer_fast.get_vocab()		)
						self.assertEqual(tokenizer_slow.get_vocab()		,       tokenizer_fast.get_vocab()		)
						self.assertIsInstance(processor_slow.tokenizer		,       lowerCamelCase_		)
						self.assertIsInstance(processor_fast.tokenizer		,       lowerCamelCase_		)

						self.assertEqual(processor_slow.image_processor.to_json_string()		,       image_processor.to_json_string()		)
						self.assertEqual(processor_fast.image_processor.to_json_string()		,       image_processor.to_json_string()		)
						self.assertIsInstance(processor_slow.image_processor		,       lowerCamelCase_		)
						self.assertIsInstance(processor_fast.image_processor		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,       image_processor=self.get_image_processor()		)
						processor.save_pretrained(self.tmpdirname		)

						a					     =   self.get_tokenizer(cls_token="(CLS)"		,       sep_token="(SEP)"		)
						a					     =   self.get_image_processor(do_normalize=lowerCamelCase_		)

						a					     =   ChineseCLIPProcessor.from_pretrained(
						    self.tmpdirname		,       cls_token="(CLS)"		,       sep_token="(SEP)"		,       do_normalize=lowerCamelCase_		)

						self.assertEqual(processor.tokenizer.get_vocab()		,       tokenizer_add_kwargs.get_vocab()		)
						self.assertIsInstance(processor.tokenizer		,       lowerCamelCase_		)

						self.assertEqual(processor.image_processor.to_json_string()		,       image_processor_add_kwargs.to_json_string()		)
						self.assertIsInstance(processor.image_processor		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   self.prepare_image_inputs()

						a					     =   image_processor(lowerCamelCase_		,       return_tensors="np"		)
						a					     =   processor(images=lowerCamelCase_		,       return_tensors="np"		)

						for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()		,       input_processor[key].sum()		,       delta=1E-2		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   "Alexandra，T-shirt的价格是15便士。"

						a					     =   processor(text=lowerCamelCase_		)

						a					     =   tokenizer(lowerCamelCase_		)

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]		,       encoded_processor[key]		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   "Alexandra，T-shirt的价格是15便士。"
						a					     =   self.prepare_image_inputs()

						a					     =   processor(text=lowerCamelCase_		,       images=lowerCamelCase_		)

						self.assertListEqual(list(inputs.keys()		)		,       ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]		)

						# test if it raises when no input is passed
						with pytest.raises(lowerCamelCase_		):
											processor()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						a					     =   processor.batch_decode(lowerCamelCase_		)
						a					     =   tokenizer.batch_decode(lowerCamelCase_		)

						self.assertListEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.get_image_processor()
						a					     =   self.get_tokenizer()

						a					     =   ChineseCLIPProcessor(tokenizer=lowerCamelCase_		,       image_processor=lowerCamelCase_		)

						a					     =   "Alexandra，T-shirt的价格是15便士。"
						a					     =   self.prepare_image_inputs()

						a					     =   processor(text=lowerCamelCase_		,       images=lowerCamelCase_		)

						self.assertListEqual(list(inputs.keys()		)		,       processor.model_input_names		)
