


import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

lowerCAmelCase       		=    {
    """vocab_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
    },
    """merges_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
    },
}

lowerCAmelCase       		=    {
    """vinai/phobert-base""": 256,
    """vinai/phobert-large""": 256,
}




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Union[str, Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							int	    =	set()
						__UpperCAmelCase			:							Union[str, Any]	    =	word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)      )
												__UpperCAmelCase			:							int	    =	char

						__UpperCAmelCase			:							Any	    =	set(_SCREAMING_SNAKE_CASE      )
						return pairs





class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Any										=						VOCAB_FILES_NAMES
	_lowerCAmelCase     :		Union[str, Any]										=						PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase     :		Optional[Any]										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


	def __init__(    self				,     lowercase__				,     lowercase__				,     lowercase__="<s>"				,     lowercase__="</s>"				,     lowercase__="</s>"				,     lowercase__="<s>"				,     lowercase__="<unk>"				,     lowercase__="<pad>"				,     lowercase__="<mask>"				,     **lowercase__				,     ):
							super().__init__(
							    bos_token=__UpperCAmelCase				,     eos_token=__UpperCAmelCase				,     unk_token=__UpperCAmelCase				,     sep_token=__UpperCAmelCase				,     cls_token=__UpperCAmelCase				,     pad_token=__UpperCAmelCase				,     mask_token=__UpperCAmelCase				,     **__UpperCAmelCase				,     )

							__UpperCAmelCase			:							int	    =	vocab_file
							__UpperCAmelCase			:							Union[str, Any]	    =	merges_file

							__UpperCAmelCase			:							Optional[Any]	    =	{}
							__UpperCAmelCase			:							List[str]	    =	0
							__UpperCAmelCase			:							Any	    =	1
							__UpperCAmelCase			:							List[Any]	    =	2
							__UpperCAmelCase			:							Optional[Any]	    =	3

							self.add_from_file(__UpperCAmelCase)

							__UpperCAmelCase			:							Dict	    =	{v: k for k, v in self.encoder.items()}

							with open(__UpperCAmelCase				,     encoding='''utf-8''') as merges_handle:
													__UpperCAmelCase			:							List[Any]	    =	merges_handle.read().split('''\n''')[:-1]
							__UpperCAmelCase			:							Optional[int]	    =	[tuple(merge.split()[:-1]) for merge in merges]
							__UpperCAmelCase			:							List[str]	    =	dict(zip(__UpperCAmelCase				,     range(len(__UpperCAmelCase))))
							__UpperCAmelCase			:							Union[str, Any]	    =	{}


	def  A(    self				,     lowercase__				,     lowercase__ = None):

							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							__UpperCAmelCase			:							Dict	    =	[self.cls_token_id]
							__UpperCAmelCase			:							List[Any]	    =	[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep


	def  A(    self				,     lowercase__				,     lowercase__ = None				,     lowercase__ = False):

							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=__UpperCAmelCase				,     token_ids_a=__UpperCAmelCase				,     already_has_special_tokens=__UpperCAmelCase)

							if token_ids_a is None:
													return [1] + ([0] * len(__UpperCAmelCase)) + [1]
							return [1] + ([0] * len(__UpperCAmelCase)) + [1, 1] + ([0] * len(__UpperCAmelCase)) + [1]


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							Union[str, Any]	    =	[self.sep_token_id]
							__UpperCAmelCase			:							Optional[int]	    =	[self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


	@property
	def  A(    self):
							return len(self.encoder)


	def  A(    self):
							return dict(self.encoder				,     **self.added_tokens_encoder)


	def  A(    self				,     lowercase__):

							if token in self.cache:
													return self.cache[token]
							__UpperCAmelCase			:							Union[str, Any]	    =	tuple(__UpperCAmelCase)
							__UpperCAmelCase			:							Dict	    =	tuple(list(word[:-1]) + [word[-1] + '''</w>'''])
							__UpperCAmelCase			:							Any	    =	get_pairs(__UpperCAmelCase)

							if not pairs:
													return token

							while True:
													__UpperCAmelCase			:							Optional[Any]	    =	min(__UpperCAmelCase				,     key=lambda lowercase__: self.bpe_ranks.get(__UpperCAmelCase				,     float('''inf''')))
													if bigram not in self.bpe_ranks:
																			break
													__UpperCAmelCase			:							int	    =	bigram
													__UpperCAmelCase			:							Dict	    =	[]
													__UpperCAmelCase			:							Union[str, Any]	    =	0
													while i < len(__UpperCAmelCase):
																			try:
																									__UpperCAmelCase			:							str	    =	word.index(__UpperCAmelCase				,     __UpperCAmelCase)
																			except ValueError:
																									new_word.extend(word[i:])
																									break
																			else:
																									new_word.extend(word[i:j])
																									__UpperCAmelCase			:							List[str]	    =	j

																			if word[i] == first and i < len(__UpperCAmelCase) - 1 and word[i + 1] == second:
																									new_word.append(first + second)
																									i += 2
																			else:
																									new_word.append(word[i])
																									i += 1
													__UpperCAmelCase			:							List[str]	    =	tuple(__UpperCAmelCase)
													__UpperCAmelCase			:							Optional[int]	    =	new_word
													if len(__UpperCAmelCase) == 1:
																			break
													else:
																			__UpperCAmelCase			:							Union[str, Any]	    =	get_pairs(__UpperCAmelCase)
							__UpperCAmelCase			:							int	    =	'@@ '.join(__UpperCAmelCase)
							__UpperCAmelCase			:							Optional[Any]	    =	word[:-4]
							__UpperCAmelCase			:							int	    =	word
							return word


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							str	    =	[]

							__UpperCAmelCase			:							Any	    =	re.findall(r'''\S+\n?'''				,     __UpperCAmelCase)

							for token in words:
													split_tokens.extend(list(self.bpe(__UpperCAmelCase).split(''' ''')))
							return split_tokens


	def  A(    self				,     lowercase__):
							return self.encoder.get(__UpperCAmelCase				,     self.encoder.get(self.unk_token))


	def  A(    self				,     lowercase__):
							return self.decoder.get(__UpperCAmelCase				,     self.unk_token)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							Optional[int]	    =	' '.join(__UpperCAmelCase).replace('''@@ '''				,     '''''').strip()
							return out_string


	def  A(    self				,     lowercase__				,     lowercase__ = None):

							if not os.path.isdir(__UpperCAmelCase):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory")
													return
							__UpperCAmelCase			:							Any	    =	os.path.join(
							    __UpperCAmelCase				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
							__UpperCAmelCase			:							Optional[Any]	    =	os.path.join(
							    __UpperCAmelCase				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''])

							if os.path.abspath(self.vocab_file) != os.path.abspath(__UpperCAmelCase):
													copyfile(self.vocab_file				,     __UpperCAmelCase)

							if os.path.abspath(self.merges_file) != os.path.abspath(__UpperCAmelCase):
													copyfile(self.merges_file				,     __UpperCAmelCase)

							return out_vocab_file, out_merge_file


	def  A(    self				,     lowercase__):

							if isinstance(__UpperCAmelCase				,     __UpperCAmelCase):
													try:
																			with open(__UpperCAmelCase				,     '''r'''				,     encoding='''utf-8''') as fd:
																									self.add_from_file(__UpperCAmelCase)
													except FileNotFoundError as fnfe:
																			raise fnfe
													except UnicodeError:
																			raise Exception(F"Incorrect encoding detected in {f}, please rebuild the dataset")
													return

							__UpperCAmelCase			:							Tuple	    =	f.readlines()
							for lineTmp in lines:
													__UpperCAmelCase			:							Optional[int]	    =	lineTmp.strip()
													__UpperCAmelCase			:							int	    =	line.rfind(''' ''')
													if idx == -1:
																			raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\'''')
													__UpperCAmelCase			:							Optional[int]	    =	line[:idx]
													__UpperCAmelCase			:							List[str]	    =	len(self.encoder)








"""simple docstring"""





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class    _lowerCAmelCase  (	a    ,  a    ,  a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:int						    =   StableUnCLIPPipeline
       __magic_name__	:int						    =   TEXT_TO_IMAGE_PARAMS
       __magic_name__	:List[str]						    =   TEXT_TO_IMAGE_BATCH_PARAMS
       __magic_name__	:Optional[int]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS
       __magic_name__	:Union[str, Any]						    =   TEXT_TO_IMAGE_IMAGE_PARAMS

       # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
       __magic_name__	:List[str]						    =   False






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				3_2
         lowerCAmelCase__     :List[Any]											=				embedder_hidden_size

         # prior components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Any											=				CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=__UpperCAmelCase       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :int											=				PriorTransformer(
             num_attention_heads=2       ,					attention_head_dim=1_2       ,					embedding_dim=__UpperCAmelCase       ,					num_layers=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				DDPMScheduler(
             variance_type='fixed_small_log'       ,					prediction_type='sample'       ,					num_train_timesteps=1_0_0_0       ,					clip_sample=__UpperCAmelCase       ,					clip_sample_range=5.0       ,					beta_schedule='squaredcos_cap_v2'       ,					)

         # regular denoising components

         torch.manual_seed(0 )
         lowerCAmelCase__     :Dict											=				StableUnCLIPImageNormalizer(embedding_dim=__UpperCAmelCase )
         lowerCAmelCase__     :List[Any]											=				DDPMScheduler(beta_schedule='squaredcos_cap_v2' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :List[Any]											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         torch.manual_seed(0 )
         lowerCAmelCase__     :str											=				CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=__UpperCAmelCase       ,					projection_dim=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					) )

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				UNetaDConditionModel(
             sample_size=3_2       ,					in_channels=4       ,					out_channels=4       ,					down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,					up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,					block_out_channels=(3_2, 6_4)       ,					attention_head_dim=(2, 4)       ,					class_embed_type='projection'       ,					projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,					cross_attention_dim=__UpperCAmelCase       ,					layers_per_block=1       ,					upcast_attention=__UpperCAmelCase       ,					use_linear_projection=__UpperCAmelCase       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Tuple											=				DDIMScheduler(
             beta_schedule='scaled_linear'       ,					beta_start=0.0_00_85       ,					beta_end=0.0_12       ,					prediction_type='v_prediction'       ,					set_alpha_to_one=__UpperCAmelCase       ,					steps_offset=1       ,					)

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[int]											=				AutoencoderKL()

         lowerCAmelCase__     :Optional[int]											=				{
             # prior components
             'prior_tokenizer': prior_tokenizer,
             'prior_text_encoder': prior_text_encoder,
             'prior': prior,
             'prior_scheduler': prior_scheduler,
             # image noising components
             'image_normalizer': image_normalizer,
             'image_noising_scheduler': image_noising_scheduler,
             # regular denoising components
             'tokenizer': tokenizer,
             'text_encoder': text_encoder,
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
         }

         return components






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

         if str(__UpperCAmelCase ).startswith('mps' ):
           lowerCAmelCase__     :str											=				torch.manual_seed(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :Optional[int]											=				torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
         lowerCAmelCase__     :Tuple											=				{
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'prior_num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[int]											=				torch_device == 'cpu'

         self._test_attention_slicing_forward_pass(test_max_difference=__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :str											=				torch_device in ['cpu', 'mps']

         self._test_inference_batch_single_identical(test_max_difference=__UpperCAmelCase )






@slow
@require_torch_gpu
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy' )

         lowerCAmelCase__     :List[Any]											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         # stable unclip will oom when integration tests are run on a V100,
         # so turn on memory savings
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :List[str]											=				torch.Generator(device='cpu' ).manual_seed(0 )
         lowerCAmelCase__     :List[str]											=				pipe('anime turle'       ,					generator=__UpperCAmelCase       ,					output_type='np' )

         lowerCAmelCase__     :Union[str, Any]											=				output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         lowerCAmelCase__     :int											=				StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,					torch_dtype=torch.floataa )
         lowerCAmelCase__     :List[str]											=				pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         lowerCAmelCase__     :Tuple											=				pipe(
             'anime turtle'       ,					prior_num_inference_steps=2       ,					num_inference_steps=2       ,					output_type='np'       ,					)

         lowerCAmelCase__     :str											=				torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 1_0**9
