
import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     Tuple			)			->	Tuple:
					lowercase_	    =   tempfile.mkdtemp()

					# fmt: off
					lowercase_	    =   ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
					# fmt: on
					lowercase_	    =   dict(zip(SCREAMING_SNAKE_CASE_			,				range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
					lowercase_	    =   ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
					lowercase_	    =   {'''unk_token''': '''<unk>'''}

					lowercase_	    =   os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']			)
					lowercase_	    =   os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''merges_file''']			)
					with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''			) as fp:
									fp.write(json.dumps(SCREAMING_SNAKE_CASE_			) + '''\n'''			)
					with open(self.merges_file			,				'''w'''			,				encoding='''utf-8'''			) as fp:
									fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_			)			)

					lowercase_	    =   {
					    '''do_resize''': True,
					    '''size''': 2_0,
					    '''do_center_crop''': True,
					    '''crop_size''': 1_8,
					    '''do_normalize''': True,
					    '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
					    '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
					}
					lowercase_	    =   os.path.join(self.tmpdirname			,				SCREAMING_SNAKE_CASE_			)
					with open(self.image_processor_file			,				'''w'''			,				encoding='''utf-8'''			) as fp:
									json.dump(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     int			,				**SCREAMING_SNAKE_CASE_    :     Optional[Any]			)			->	Union[str, Any]:
					return CLIPTokenizer.from_pretrained(self.tmpdirname			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     int			,				**SCREAMING_SNAKE_CASE_    :     Dict			)			->	Dict:
					return CLIPTokenizerFast.from_pretrained(self.tmpdirname			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Any			,				**SCREAMING_SNAKE_CASE_    :     Tuple			)			->	List[str]:
					return ViTImageProcessor.from_pretrained(self.tmpdirname			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Tuple:
					shutil.rmtree(self.tmpdirname			)
	def 		_lowercase    (			self    :     Optional[int]			)			->	int:
					lowercase_	    =   [np.random.randint(2_5_5			,				size=(3, 3_0, 4_0_0)			,				dtype=np.uinta			)]

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

					return image_inputs
	def 		_lowercase    (			self    :     int			)			->	Tuple:
					lowercase_	    =   self.get_tokenizer()
					lowercase_	    =   self.get_rust_tokenizer()
					lowercase_	    =   self.get_image_processor()

					lowercase_	    =   CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,				image_processor=SCREAMING_SNAKE_CASE_			)
					processor_slow.save_pretrained(self.tmpdirname			)
					lowercase_	    =   CLIPSegProcessor.from_pretrained(self.tmpdirname			,				use_fast=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,				image_processor=SCREAMING_SNAKE_CASE_			)
					processor_fast.save_pretrained(self.tmpdirname			)
					lowercase_	    =   CLIPSegProcessor.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			,				SCREAMING_SNAKE_CASE_			)
					self.assertIsInstance(processor_fast.tokenizer			,				SCREAMING_SNAKE_CASE_			)

					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			,				SCREAMING_SNAKE_CASE_			)
					self.assertIsInstance(processor_fast.image_processor			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     int			)			->	Optional[int]:
					lowercase_	    =   CLIPSegProcessor(tokenizer=self.get_tokenizer()			,				image_processor=self.get_image_processor()			)
					processor.save_pretrained(self.tmpdirname			)

					lowercase_	    =   self.get_tokenizer(bos_token='''(BOS)'''			,				eos_token='''(EOS)'''			)
					lowercase_	    =   self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_			,				padding_value=1.0			)

					lowercase_	    =   CLIPSegProcessor.from_pretrained(
					    self.tmpdirname			,				bos_token='''(BOS)'''			,				eos_token='''(EOS)'''			,				do_normalize=SCREAMING_SNAKE_CASE_			,				padding_value=1.0			)

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

					self.assertEqual(processor.image_processor.to_json_string()			,				image_processor_add_kwargs.to_json_string()			)
					self.assertIsInstance(processor.image_processor			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Tuple			)			->	Tuple:
					lowercase_	    =   self.get_image_processor()
					lowercase_	    =   self.get_tokenizer()

					lowercase_	    =   CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,				image_processor=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   self.prepare_image_inputs()

					lowercase_	    =   image_processor(SCREAMING_SNAKE_CASE_			,				return_tensors='''np'''			)
					lowercase_	    =   processor(images=SCREAMING_SNAKE_CASE_			,				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 		_lowercase    (			self    :     Optional[Any]			)			->	Tuple:
					lowercase_	    =   self.get_image_processor()
					lowercase_	    =   self.get_tokenizer()

					lowercase_	    =   CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,				image_processor=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   '''lower newer'''

					lowercase_	    =   processor(text=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   tokenizer(SCREAMING_SNAKE_CASE_			)

					for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]			,				encoded_processor[key]			)
	def 		_lowercase    (			self    :     Any			)			->	Optional[int]:
					lowercase_	    =   self.get_image_processor()
					lowercase_	    =   self.get_tokenizer()

					lowercase_	    =   CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,				image_processor=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   '''lower newer'''
					lowercase_	    =   self.prepare_image_inputs()

					lowercase_	    =   processor(text=SCREAMING_SNAKE_CASE_			,				images=SCREAMING_SNAKE_CASE_			)

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

					# test if it raises when no input is passed
					with pytest.raises(SCREAMING_SNAKE_CASE_			):
									processor()
	def 		_lowercase    (			self    :     List[Any]			)			->	List[Any]:
					lowercase_	    =   self.get_image_processor()
					lowercase_	    =   self.get_tokenizer()

					lowercase_	    =   CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,				image_processor=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   self.prepare_image_inputs()
					lowercase_	    =   self.prepare_image_inputs()

					lowercase_	    =   processor(images=SCREAMING_SNAKE_CASE_			,				visual_prompt=SCREAMING_SNAKE_CASE_			)

					self.assertListEqual(list(inputs.keys()			)			,				['''pixel_values''', '''conditional_pixel_values''']			)

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




	def 		_lowercase    (			self    :     Tuple			)			->	str:
					lowercase_	    =   self.get_image_processor()
					lowercase_	    =   self.get_tokenizer()

					lowercase_	    =   CLIPSegProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,				image_processor=SCREAMING_SNAKE_CASE_			)

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

					lowercase_	    =   processor.batch_decode(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   tokenizer.batch_decode(SCREAMING_SNAKE_CASE_			)

					self.assertListEqual(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				if "cls_token" in name:
								lowercase_	    =   name.replace('''cls_token'''		,  '''vit.embeddings.cls_token'''   )
				if "mask_token" in name:
								lowercase_	    =   name.replace('''mask_token'''		,  '''decoder.mask_token'''   )
				if "decoder_pos_embed" in name:
								lowercase_	    =   name.replace('''decoder_pos_embed'''		,  '''decoder.decoder_pos_embed'''   )
				if "pos_embed" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''pos_embed'''		,  '''vit.embeddings.position_embeddings'''   )
				if "patch_embed.proj" in name:
								lowercase_	    =   name.replace('''patch_embed.proj'''		,  '''vit.embeddings.patch_embeddings.projection'''   )
				if "patch_embed.norm" in name:
								lowercase_	    =   name.replace('''patch_embed.norm'''		,  '''vit.embeddings.norm'''   )
				if "decoder_blocks" in name:
								lowercase_	    =   name.replace('''decoder_blocks'''		,  '''decoder.decoder_layers'''   )
				if "blocks" in name:
								lowercase_	    =   name.replace('''blocks'''		,  '''vit.encoder.layer'''   )
				if "attn.proj" in name:
								lowercase_	    =   name.replace('''attn.proj'''		,  '''attention.output.dense'''   )
				if "attn" in name:
								lowercase_	    =   name.replace('''attn'''		,  '''attention.self'''   )
				if "norm1" in name:
								lowercase_	    =   name.replace('''norm1'''		,  '''layernorm_before'''   )
				if "norm2" in name:
								lowercase_	    =   name.replace('''norm2'''		,  '''layernorm_after'''   )
				if "mlp.fc1" in name:
								lowercase_	    =   name.replace('''mlp.fc1'''		,  '''intermediate.dense'''   )
				if "mlp.fc2" in name:
								lowercase_	    =   name.replace('''mlp.fc2'''		,  '''output.dense'''   )
				if "decoder_embed" in name:
								lowercase_	    =   name.replace('''decoder_embed'''		,  '''decoder.decoder_embed'''   )
				if "decoder_norm" in name:
								lowercase_	    =   name.replace('''decoder_norm'''		,  '''decoder.decoder_norm'''   )
				if "decoder_pred" in name:
								lowercase_	    =   name.replace('''decoder_pred'''		,  '''decoder.decoder_pred'''   )
				if "norm.weight" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''norm.weight'''		,  '''vit.layernorm.weight'''   )
				if "norm.bias" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''norm.bias'''		,  '''vit.layernorm.bias'''   )

				return name




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Any   ):


				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase_	    =   orig_state_dict.pop(snake_case__   )

								if "qkv" in key:
												lowercase_	    =   key.split('''.'''   )
												lowercase_	    =   int(key_split[1]   )
												if "decoder_blocks" in key:
																lowercase_	    =   config.decoder_hidden_size
																lowercase_	    =   '''decoder.decoder_layers.'''
																if "weight" in key:
																				lowercase_	    =   val[:dim, :]
																				lowercase_	    =   val[dim : dim * 2, :]
																				lowercase_	    =   val[-dim:, :]
																elif "bias" in key:
																				lowercase_	    =   val[:dim]
																				lowercase_	    =   val[dim : dim * 2]
																				lowercase_	    =   val[-dim:]
												else:
																lowercase_	    =   config.hidden_size
																lowercase_	    =   '''vit.encoder.layer.'''
																if "weight" in key:
																				lowercase_	    =   val[:dim, :]
																				lowercase_	    =   val[dim : dim * 2, :]
																				lowercase_	    =   val[-dim:, :]
																elif "bias" in key:
																				lowercase_	    =   val[:dim]
																				lowercase_	    =   val[dim : dim * 2]
																				lowercase_	    =   val[-dim:]

								else:
												lowercase_	    =   val

				return orig_state_dict




def 							a		(				snake_case__:   str		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   ViTMAEConfig()
				if "large" in checkpoint_url:
								lowercase_	    =   1_024
								lowercase_	    =   4_096
								lowercase_	    =   24
								lowercase_	    =   16
				elif "huge" in checkpoint_url:
								lowercase_	    =   14
								lowercase_	    =   1_280
								lowercase_	    =   5_120
								lowercase_	    =   32
								lowercase_	    =   16

				lowercase_	    =   ViTMAEForPreTraining(snake_case__   )

				lowercase_	    =   torch.hub.load_state_dict_from_url(snake_case__		,  map_location='''cpu'''   )['''model''']

				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )

				lowercase_	    =   convert_state_dict(snake_case__		,  snake_case__   )

				model.load_state_dict(snake_case__   )
				model.eval()

				lowercase_	    =   '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

				lowercase_	    =   Image.open(requests.get(snake_case__		,  stream=snake_case__   ).raw   )
				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )
				lowercase_	    =   image_processor(images=snake_case__		,  return_tensors='''pt'''   )

				# forward pass
				torch.manual_seed(2   )
				lowercase_	    =   model(**snake_case__   )
				lowercase_	    =   outputs.logits

				if "large" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]   )
				elif "huge" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]   )
				else:
								lowercase_	    =   torch.tensor(
								    [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]   )

				# verify logits
				assert torch.allclose(logits[0, :3, :3]		,  snake_case__		,  atol=1e-4   )

				print(F'''Saving model to {pytorch_dump_folder_path}'''   )
				model.save_pretrained(snake_case__   )

				print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
				image_processor.save_pretrained(snake_case__   )


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_url',
						    default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
						    type=str,
						    help='URL of the checkpoint you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)

						__a         =   parser.parse_args()
						convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




