






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

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


_a     : List[str]   	=      logging.get_logger(__name__)

_a     : Optional[Any]   	=      {
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

_a     : Dict   	=      {
    """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""",
    },
}

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


def 					snake_case__				(				UpperCAmelCase				:	Any							):
    lowerCAmelCase__ :Dict   				=					set()
    lowerCAmelCase__ :Tuple   				=					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)							)
        lowerCAmelCase__ :Optional[int]   				=					char

    lowerCAmelCase__ :Any   				=					set(UpperCamelCase_							)
    return pairs







class 						_UpperCAmelCase	(	A__		):




      """simple docstring"""



      A    =     VOCAB_FILES_NAMES
      A    =     PRETRAINED_VOCAB_FILES_MAP
      A    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      def __init__( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase     ,						_lowerCAmelCase="<s>"     ,						_lowerCAmelCase="</s>"     ,						_lowerCAmelCase="</s>"     ,						_lowerCAmelCase="<s>"     ,						_lowerCAmelCase="<unk>"     ,						_lowerCAmelCase="<pad>"     ,						_lowerCAmelCase="<mask>"     ,						**_lowerCAmelCase     ,						):



          '''simple docstring'''
          super().__init__(
              bos_token=a__     ,						eos_token=a__     ,						unk_token=a__     ,						sep_token=a__     ,						cls_token=a__     ,						pad_token=a__     ,						mask_token=a__     ,						**a__     ,						)

          lowerCAmelCase__ :Optional[int]   				=					vocab_file
          lowerCAmelCase__ :List[Any]   				=					merges_file

          lowerCAmelCase__ :Union[str, Any]   				=					{}
          lowerCAmelCase__ :List[Any]   				=					0
          lowerCAmelCase__ :str   				=					1
          lowerCAmelCase__ :Tuple   				=					2
          lowerCAmelCase__ :str   				=					3

          self.add_from_file(a__		)

          lowerCAmelCase__ :List[Any]   				=					{v: k for k, v in self.encoder.items()}

          with open(a__     ,						encoding="utf-8"		) as merges_handle:
              lowerCAmelCase__ :List[str]   				=					merges_handle.read().split("\n"		)[:-1]
          lowerCAmelCase__ :Any   				=					[tuple(merge.split()[:-1]		) for merge in merges]
          lowerCAmelCase__ :Union[str, Any]   				=					dict(zip(a__     ,						range(len(a__		)		)		)		)
          lowerCAmelCase__ :str   				=					{}
      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase = None		):



          '''simple docstring'''

          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          lowerCAmelCase__ :List[Any]   				=					[self.cls_token_id]
          lowerCAmelCase__ :Optional[Any]   				=					[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep
      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase = None     ,						_lowerCAmelCase = False		):



          '''simple docstring'''

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

          if token_ids_a is None:
              return [1] + ([0] * len(a__		)) + [1]
          return [1] + ([0] * len(a__		)) + [1, 1] + ([0] * len(a__		)) + [1]
      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase = None		):



          '''simple docstring'''
          lowerCAmelCase__ :int   				=					[self.sep_token_id]
          lowerCAmelCase__ :str   				=					[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       snake_case_   ( self		):



          '''simple docstring'''
          return len(self.encoder		)
      def       snake_case_   ( self		):



          '''simple docstring'''
          return dict(self.encoder     ,						**self.added_tokens_encoder		)
      def       snake_case_   ( self     ,						_lowerCAmelCase		):



          '''simple docstring'''
          if token in self.cache:
              return self.cache[token]
          lowerCAmelCase__ :List[Any]   				=					tuple(a__		)
          lowerCAmelCase__ :List[Any]   				=					tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
          lowerCAmelCase__ :List[Any]   				=					get_pairs(a__		)

          if not pairs:
              return token

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

                  if word[i] == first and i < len(a__		) - 1 and word[i + 1] == second:
                      new_word.append(first + second		)
                      i += 2
                  else:
                      new_word.append(word[i]		)
                      i += 1
              lowerCAmelCase__ :str   				=					tuple(a__		)
              lowerCAmelCase__ :int   				=					new_word
              if len(a__		) == 1:
                  break
              else:
                  lowerCAmelCase__ :Union[str, Any]   				=					get_pairs(a__		)
          lowerCAmelCase__ :Union[str, Any]   				=					"@@ ".join(a__		)
          lowerCAmelCase__ :int   				=					word[:-4]
          lowerCAmelCase__ :Dict   				=					word
          return word
      def       snake_case_   ( self     ,						_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__ :List[Any]   				=					[]

          lowerCAmelCase__ :Tuple   				=					re.findall(r"\S+\n?"     ,						a__		)

          for token in words:
              split_tokens.extend(list(self.bpe(a__		).split(" "		)		)		)
          return split_tokens
      def       snake_case_   ( self     ,						_lowerCAmelCase		):



          '''simple docstring'''
          return self.encoder.get(a__     ,						self.encoder.get(self.unk_token		)		)
      def       snake_case_   ( self     ,						_lowerCAmelCase		):



          '''simple docstring'''
          return self.decoder.get(a__     ,						self.unk_token		)
      def       snake_case_   ( self     ,						_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__ :int   				=					" ".join(a__		).replace("@@ "     ,						""		).strip()
          return out_string
      def       snake_case_   ( self     ,						_lowerCAmelCase     ,						_lowerCAmelCase = None		):



          '''simple docstring'''
          if not os.path.isdir(a__		):
              logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
              return
          lowerCAmelCase__ :Tuple   				=					os.path.join(
              a__     ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
          lowerCAmelCase__ :Dict   				=					os.path.join(
              a__     ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

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

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

          return out_vocab_file, out_merge_file



      def       snake_case_   ( self     ,						_lowerCAmelCase		):



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

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








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]						)->	List[str]:
    A__					=       [tensor.shape for tensor in tensor_list]
    return all(shape == shapes[0] for shape in shapes[1:]						)



class      _UpperCAmelCase      (							A__      ,   A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					StableDiffusionLatentUpscalePipeline
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         '''height''',
         '''width''',
         '''cross_attention_kwargs''',
         '''negative_prompt_embeds''',
         '''prompt_embeds''',
     }
     UpperCamelCase__		     =					PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCamelCase__		     =					frozenset(
         []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     UpperCamelCase__		     =					frozenset([]  )

     UpperCamelCase__		     =					True




     @property
     def 		snake_case_		(					self):
         A__					=       1
         A__					=       4
         A__					=       (1_6, 1_6)

         A__					=       floats_tensor((batch_size, num_channels) + sizes			,    rng=random.Random(0)).to(a__)
         return image




     def 		snake_case_		(					self):
         torch.manual_seed(0)
         A__					=       UNetaDConditionModel(
             act_fn='''gelu'''			,    attention_head_dim=8			,    norm_num_groups=a__			,    block_out_channels=[3_2, 3_2, 6_4, 6_4]			,    time_cond_proj_dim=1_6_0			,    conv_in_kernel=1			,    conv_out_kernel=1			,    cross_attention_dim=3_2			,    down_block_types=(
                 '''KDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
             )			,    in_channels=8			,    mid_block_type=a__			,    only_cross_attention=a__			,    out_channels=5			,    resnet_time_scale_shift='''scale_shift'''			,    time_embedding_type='''fourier'''			,    timestep_post_act='''gelu'''			,    up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')			,    )
         A__					=       AutoencoderKL(
             block_out_channels=[3_2, 3_2, 6_4, 6_4]			,    in_channels=3			,    out_channels=3			,    down_block_types=[
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
             ]			,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,    latent_channels=4			,    )
         A__					=       EulerDiscreteScheduler(prediction_type='''sample''')
         A__					=       CLIPTextConfig(
             bos_token_id=0			,    eos_token_id=2			,    hidden_size=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			,    hidden_act='''quick_gelu'''			,    projection_dim=5_1_2			,    )
         A__					=       CLIPTextModel(a__)
         A__					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

         A__					=       {
             '''unet''': model.eval(),
             '''vae''': vae.eval(),
             '''scheduler''': scheduler,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
         }

         return components




     def 		snake_case_		(					self			,    a__			,    a__=0):
         if str(a__).startswith('''mps'''):
             A__					=       torch.manual_seed(a__)
         else:
             A__					=       torch.Generator(device=a__).manual_seed(a__)
         A__					=       {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': self.dummy_image.cpu(),
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs




     def 		snake_case_		(					self):
         A__					=       '''cpu'''

         A__					=       self.get_dummy_components()
         A__					=       self.pipeline_class(**a__)
         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)

         A__					=       self.get_dummy_inputs(a__)
         A__					=       pipe(**a__).images
         A__					=       image[0, -3:, -3:, -1]

         self.assertEqual(image.shape			,    (1, 2_5_6, 2_5_6, 3))
         A__					=       np.array(
             [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5])
         A__					=       np.abs(image_slice.flatten() - expected_slice).max()
         self.assertLessEqual(a__			,    1e-3)




     def 		snake_case_		(					self):
         super().test_attention_slicing_forward_pass(expected_max_diff=7e-3)




     def 		snake_case_		(					self):
         super().test_cpu_offload_forward_pass(expected_max_diff=3e-3)




     def 		snake_case_		(					self):
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         super().test_inference_batch_single_identical(expected_max_diff=7e-3)




     def 		snake_case_		(					self):
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3)




     def 		snake_case_		(					self):
         super().test_save_load_local(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         super().test_save_load_optional_components(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         A__					=       [
             '''DDIMScheduler''',
             '''DDPMScheduler''',
             '''PNDMScheduler''',
             '''HeunDiscreteScheduler''',
             '''EulerAncestralDiscreteScheduler''',
             '''KDPM2DiscreteScheduler''',
             '''KDPM2AncestralDiscreteScheduler''',
             '''DPMSolverSDEScheduler''',
         ]
         A__					=       self.get_dummy_components()
         A__					=       self.pipeline_class(**a__)

         # make sure that PNDM does not need warm-up
         pipe.scheduler.register_to_config(skip_prk_steps=a__)

         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)
         A__					=       self.get_dummy_inputs(a__)
         A__					=       2

         A__					=       []
         for scheduler_enum in KarrasDiffusionSchedulers:
             if scheduler_enum.name in skip_schedulers:
                 # no sigma schedulers are not supported
                 # no schedulers
                 continue

             A__					=       getattr(a__			,    scheduler_enum.name)
             A__					=       scheduler_cls.from_config(pipe.scheduler.config)
             A__					=       pipe(**a__)[0]
             outputs.append(a__)

         assert check_same_shape(a__)







@require_torch_gpu
@slow
class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def 		snake_case_		(					self):
         A__					=       torch.manual_seed(3_3)

         A__					=       StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''			,    torch_dtype=torch.floataa)
         pipe.to('''cuda''')

         A__					=       StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,    torch_dtype=torch.floataa)
         upscaler.to('''cuda''')

         A__					=       '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

         A__					=       pipe(a__			,    generator=a__			,    output_type='''latent''').images

         A__					=       upscaler(
             prompt=a__			,    image=a__			,    num_inference_steps=2_0			,    guidance_scale=0			,    generator=a__			,    output_type='''np'''			,    ).images[0]

         A__					=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy''')
         assert np.abs((expected_image - image).mean()) < 5e-2




     def 		snake_case_		(					self):
         A__					=       torch.manual_seed(3_3)

         A__					=       StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,    torch_dtype=torch.floataa)
         upscaler.to('''cuda''')

         A__					=       '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

         A__					=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png''')

         A__					=       upscaler(
             prompt=a__			,    image=a__			,    num_inference_steps=2_0			,    guidance_scale=0			,    generator=a__			,    output_type='''np'''			,    ).images[0]

         A__					=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy''')
         assert np.abs((expected_image - image).max()) < 5e-2
