






"""simple docstring"""




import copy
import random

from transformers import CLIPTokenizer


class 	__UpperCAmelCase		(				_a	):


	'''simple docstring'''





	def __init__(     self      ,      *_A      ,      **_A					):



		'''simple docstring'''

		super().__init__(*__lowerCAmelCase      ,      **__lowerCAmelCase					)
		_SCREAMING_SNAKE_CASE						={}


	def 				UpperCamelCase_  (     self      ,      _A      ,      *_A      ,      **_A					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=super().add_tokens(__lowerCAmelCase      ,      *__lowerCAmelCase      ,      **__lowerCAmelCase					)
		if num_added_tokens == 0:
			raise ValueError(
			    f"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
			    ''' `placeholder_token` that is not already in the tokenizer.'''					)


	def 				UpperCamelCase_  (     self      ,      _A      ,      *_A      ,      _A=1      ,      **_A					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=[]
		if num_vec_per_token == 1:
			self.try_adding_tokens(__lowerCAmelCase      ,      *__lowerCAmelCase      ,      **__lowerCAmelCase					)
			output.append(__lowerCAmelCase					)
		else:
			_SCREAMING_SNAKE_CASE						=[]
			for i in range(__lowerCAmelCase					):
				_SCREAMING_SNAKE_CASE						=placeholder_token + f"""_{i}"""
				self.try_adding_tokens(__lowerCAmelCase      ,      *__lowerCAmelCase      ,      **__lowerCAmelCase					)
				output.append(__lowerCAmelCase					)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
		for token in self.token_map:
			if token in placeholder_token:
				raise ValueError(
				    f"""The tokenizer already has placeholder token {token} that can get confused with"""
				    f""" {placeholder_token}keep placeholder tokens independent"""					)
		_SCREAMING_SNAKE_CASE						=output


	def 				UpperCamelCase_  (     self      ,      _A      ,      _A=False      ,      _A=1.0					):



		'''simple docstring'''

		if isinstance(__lowerCAmelCase      ,      __lowerCAmelCase					):
			_SCREAMING_SNAKE_CASE						=[]
			for i in range(len(__lowerCAmelCase					)					):
				output.append(self.replace_placeholder_tokens_in_text(text[i]      ,      vector_shuffle=__lowerCAmelCase					)					)
			return output
		for placeholder_token in self.token_map:
			if placeholder_token in text:
				_SCREAMING_SNAKE_CASE						=self.token_map[placeholder_token]
				_SCREAMING_SNAKE_CASE						=tokens[: 1 + int(len(__lowerCAmelCase					) * prop_tokens_to_load					)]
				if vector_shuffle:
					_SCREAMING_SNAKE_CASE						=copy.copy(__lowerCAmelCase					)
					random.shuffle(__lowerCAmelCase					)
				_SCREAMING_SNAKE_CASE						=text.replace(__lowerCAmelCase      ,      ''' '''.join(__lowerCAmelCase					)					)
		return text


	def __call__(     self      ,      _A      ,      *_A      ,      _A=False      ,      _A=1.0      ,      **_A					):



		'''simple docstring'''

		return super().__call__(
		    self.replace_placeholder_tokens_in_text(
		        __lowerCAmelCase      ,      vector_shuffle=__lowerCAmelCase      ,      prop_tokens_to_load=__lowerCAmelCase					)      ,      *__lowerCAmelCase      ,      **__lowerCAmelCase      ,      )







	def 				UpperCamelCase_  (     self      ,      _A      ,      *_A      ,      _A=False      ,      _A=1.0      ,      **_A					):



		'''simple docstring'''

		return super().encode(
		    self.replace_placeholder_tokens_in_text(
		        __lowerCAmelCase      ,      vector_shuffle=__lowerCAmelCase      ,      prop_tokens_to_load=__lowerCAmelCase					)      ,      *__lowerCAmelCase      ,      **__lowerCAmelCase      ,      )











import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a ,		unittest.TestCase							):
							snake_case  :   List[str]      =							VQModel
							snake_case  :   Dict      =							"""sample"""






							@property
							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase=(32, 32)	):
														UpperCamelCase__					  =      4
														UpperCamelCase__					  =      3

														UpperCamelCase__					  =      floats_tensor((batch_size, num_channels) + sizes	).to(__lowerCAmelCase	)

														return {"sample": image}






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      {
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														UpperCamelCase__					  =      self.dummy_input
														return init_dict, inputs_dict






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__					  =      VQModel.from_pretrained("""fusing/vqgan-dummy"""	,			output_loading_info=__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)
														self.assertEqual(len(loading_info["""missing_keys"""]	)	,			0	)

														model.to(__lowerCAmelCase	)
														UpperCamelCase__					  =      model(**self.dummy_input	)

														assert image is not None, "Make sure output is not None"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VQModel.from_pretrained("""fusing/vqgan-dummy"""	)
														model.to(__lowerCAmelCase	).eval()

														torch.manual_seed(0	)
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0	)

														UpperCamelCase__					  =      torch.randn(1	,			model.config.in_channels	,			model.config.sample_size	,			model.config.sample_size	)
														UpperCamelCase__					  =      image.to(__lowerCAmelCase	)
														with torch.no_grad():
																					UpperCamelCase__					  =      model(__lowerCAmelCase	).sample

														UpperCamelCase__					  =      output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														UpperCamelCase__					  =      torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]	)
														# fmt: on
														self.assertTrue(torch.allclose(__lowerCAmelCase	,			__lowerCAmelCase	,			atol=1E-3	)	)




