


"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

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


UpperCAmelCase				=						logging.get_logger(__name__)


UpperCAmelCase				=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

UpperCAmelCase				=						{
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

UpperCAmelCase				=						{"""facebook/blenderbot_small-90M""": 512}

def 				_snake_case      (   __snake_case							:      Tuple   ):




   """simple docstring"""







   _lowerCamelCase	:					Optional[Any]   =		set()
   _lowerCamelCase	:					Any   =		word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)   )
      _lowerCamelCase	:					Tuple   =		char

   _lowerCamelCase	:					Tuple   =		set(__snake_case   )
   return pairs





class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					VOCAB_FILES_NAMES
   __UpperCAmelCase							       =					PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase							       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase							       =					['''input_ids''', '''attention_mask''']





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE="__start__"      ,   SCREAMING_SNAKE_CASE="__end__"      ,   SCREAMING_SNAKE_CASE="__unk__"      ,   SCREAMING_SNAKE_CASE="__null__"      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  List[str]:
      super().__init__(unk_token=SCREAMING_SNAKE_CASE      ,   bos_token=SCREAMING_SNAKE_CASE      ,   eos_token=SCREAMING_SNAKE_CASE      ,   pad_token=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

      with open(SCREAMING_SNAKE_CASE      ,   encoding="""utf-8""") as vocab_handle:
         _lowerCamelCase	:					List[str]   =		json.load(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		{v: k for k, v in self.encoder.items()}
      with open(SCREAMING_SNAKE_CASE      ,   encoding="""utf-8""") as merges_handle:
         _lowerCamelCase	:					Dict   =		merges_handle.read().split("""\n""")[1:-1]
      _lowerCamelCase	:					Optional[int]   =		[tuple(merge.split()) for merge in merges]
      _lowerCamelCase	:					int   =		dict(zip(SCREAMING_SNAKE_CASE      ,   range(len(SCREAMING_SNAKE_CASE))))
      _lowerCamelCase	:					Dict   =		{}





   @property
   def 		UpperCamelCase_  (							self)  ->  int:
      return len(self.encoder)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      return dict(self.encoder      ,   **self.added_tokens_encoder)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  str:
      if token in self.cache:
         return self.cache[token]
      _lowerCamelCase	:					List[Any]   =		re.sub("""([.,!?()])"""      ,   r""" \1"""      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		re.sub("""(')"""      ,   r""" \1 """      ,   SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		re.sub(r"""\s{2,}"""      ,   """ """      ,   SCREAMING_SNAKE_CASE)
      if "\n" in token:
         _lowerCamelCase	:					Tuple   =		token.replace("""\n"""      ,   """ __newln__""")

      _lowerCamelCase	:					Any   =		token.split(""" """)
      _lowerCamelCase	:					str   =		[]
      for token in tokens:
         if not len(SCREAMING_SNAKE_CASE):
            continue

         _lowerCamelCase	:					str   =		token.lower()
         _lowerCamelCase	:					Any   =		tuple(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					Any   =		tuple(list(word[:-1]) + [word[-1] + """</w>"""])
         _lowerCamelCase	:					Tuple   =		get_pairs(SCREAMING_SNAKE_CASE)

         if not pairs:
            words.append(SCREAMING_SNAKE_CASE)
            continue

         while True:
            _lowerCamelCase	:					Optional[Any]   =		min(SCREAMING_SNAKE_CASE      ,   key=lambda SCREAMING_SNAKE_CASE: self.bpe_ranks.get(SCREAMING_SNAKE_CASE      ,   float("""inf""")))
            if bigram not in self.bpe_ranks:
               break
            _lowerCamelCase   ,    _lowerCamelCase	:					Tuple   =		bigram
            _lowerCamelCase	:					Dict   =		[]
            _lowerCamelCase	:					Union[str, Any]   =		0

            while i < len(SCREAMING_SNAKE_CASE):
               try:
                  _lowerCamelCase	:					List[Any]   =		word.index(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
                  new_word.extend(word[i:j])
                  _lowerCamelCase	:					List[str]   =		j
               except ValueError:
                  new_word.extend(word[i:])
                  break

               if word[i] == first and i < len(SCREAMING_SNAKE_CASE) - 1 and word[i + 1] == second:
                  new_word.append(first + second)
                  i += 2
               else:
                  new_word.append(word[i])
                  i += 1
            _lowerCamelCase	:					Any   =		tuple(SCREAMING_SNAKE_CASE)
            _lowerCamelCase	:					Any   =		new_word
            if len(SCREAMING_SNAKE_CASE) == 1:
               break
            else:
               _lowerCamelCase	:					Optional[int]   =		get_pairs(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					int   =		"""@@ """.join(SCREAMING_SNAKE_CASE)
         _lowerCamelCase	:					List[Any]   =		word[:-4]

         _lowerCamelCase	:					Dict   =		word
         words.append(SCREAMING_SNAKE_CASE)
      return " ".join(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Any   =		[]

      _lowerCamelCase	:					Any   =		re.findall(r"""\S+\n?"""      ,   SCREAMING_SNAKE_CASE)

      for token in words:
         split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE).split(""" """)))
      return split_tokens





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					Tuple   =		token.lower()
      return self.encoder.get(SCREAMING_SNAKE_CASE      ,   self.encoder.get(self.unk_token))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  str:
      return self.decoder.get(SCREAMING_SNAKE_CASE      ,   self.unk_token)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  str:
      _lowerCamelCase	:					str   =		""" """.join(SCREAMING_SNAKE_CASE).replace("""@@ """      ,   """""").strip()
      return out_string





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None)  ->  Tuple[str]:
      if not os.path.isdir(SCREAMING_SNAKE_CASE):
         logger.error(F'Vocabulary path ({save_directory}) should be a directory')
         return
      _lowerCamelCase	:					int   =		os.path.join(
          SCREAMING_SNAKE_CASE      ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
      _lowerCamelCase	:					Tuple   =		os.path.join(
          SCREAMING_SNAKE_CASE      ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""])

      with open(SCREAMING_SNAKE_CASE      ,   """w"""      ,   encoding="""utf-8""") as f:
         f.write(json.dumps(self.encoder      ,   indent=2      ,   sort_keys=SCREAMING_SNAKE_CASE      ,   ensure_ascii=SCREAMING_SNAKE_CASE) + """\n""")

      _lowerCamelCase	:					List[Any]   =		0
      with open(SCREAMING_SNAKE_CASE      ,   """w"""      ,   encoding="""utf-8""") as writer:
         writer.write("""#version: 0.2\n""")
         for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,   key=lambda SCREAMING_SNAKE_CASE: kv[1]):
            if index != token_index:
               logger.warning(
                   F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                   """ Please check that the tokenizer is not corrupted!""")
               _lowerCamelCase	:					int   =		token_index
            writer.write(""" """.join(SCREAMING_SNAKE_CASE) + """\n""")
            index += 1

      return vocab_file, merge_file





"""simple docstring"""
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					StableDiffusionSAGPipeline
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_BATCH_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_IMAGE_PARAMS
   __UpperCAmelCase							       =					TEXT_TO_IMAGE_IMAGE_PARAMS
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      torch.manual_seed(0)
      _lowerCamelCase	:					Dict   =		UNetaDConditionModel(
          block_out_channels=(32, 64)      ,   layers_per_block=2      ,   sample_size=32      ,   in_channels=4      ,   out_channels=4      ,   down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,   up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,   cross_attention_dim=32      ,   )
      _lowerCamelCase	:					int   =		DDIMScheduler(
          beta_start=0.0_00_85      ,   beta_end=0.0_12      ,   beta_schedule="""scaled_linear"""      ,   clip_sample=SCREAMING_SNAKE_CASE      ,   set_alpha_to_one=SCREAMING_SNAKE_CASE      ,   )
      torch.manual_seed(0)
      _lowerCamelCase	:					Tuple   =		AutoencoderKL(
          block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,   up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,   latent_channels=4      ,   )
      torch.manual_seed(0)
      _lowerCamelCase	:					Tuple   =		CLIPTextConfig(
          bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-0_5      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1000      ,   )
      _lowerCamelCase	:					List[Any]   =		CLIPTextModel(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

      _lowerCamelCase	:					List[Any]   =		{
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=0)  ->  List[Any]:
      if str(SCREAMING_SNAKE_CASE).startswith("""mps"""):
         _lowerCamelCase	:					List[str]   =		torch.manual_seed(SCREAMING_SNAKE_CASE)
      else:
         _lowerCamelCase	:					List[str]   =		torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		{
          """prompt""": """.""",
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 1.0,
          """sag_scale""": 1.0,
          """output_type""": """numpy""",
      }
      return inputs





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3)







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





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Any   =		StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""")
      _lowerCamelCase	:					Union[str, Any]   =		sag_pipe.to(SCREAMING_SNAKE_CASE)
      sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[int]   =		"""."""
      _lowerCamelCase	:					int   =		torch.manual_seed(0)
      _lowerCamelCase	:					Tuple   =		sag_pipe(
          [prompt]      ,   generator=SCREAMING_SNAKE_CASE      ,   guidance_scale=7.5      ,   sag_scale=1.0      ,   num_inference_steps=20      ,   output_type="""np""")

      _lowerCamelCase	:					Dict   =		output.images

      _lowerCamelCase	:					Tuple   =		image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      _lowerCamelCase	:					Optional[Any]   =		np.array([0.15_68, 0.17_38, 0.16_95, 0.16_93, 0.15_07, 0.17_05, 0.15_47, 0.17_51, 0.19_49])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					Optional[Any]   =		StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
      _lowerCamelCase	:					Dict   =		sag_pipe.to(SCREAMING_SNAKE_CASE)
      sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Union[str, Any]   =		"""."""
      _lowerCamelCase	:					List[str]   =		torch.manual_seed(0)
      _lowerCamelCase	:					int   =		sag_pipe(
          [prompt]      ,   generator=SCREAMING_SNAKE_CASE      ,   guidance_scale=7.5      ,   sag_scale=1.0      ,   num_inference_steps=20      ,   output_type="""np""")

      _lowerCamelCase	:					Any   =		output.images

      _lowerCamelCase	:					List[str]   =		image[0, -3:, -3:, -1]

      assert image.shape == (1, 512, 512, 3)
      _lowerCamelCase	:					Any   =		np.array([0.34_59, 0.28_76, 0.25_37, 0.30_02, 0.26_71, 0.21_60, 0.30_26, 0.22_62, 0.23_71])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					int   =		StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""")
      _lowerCamelCase	:					Optional[Any]   =		sag_pipe.to(SCREAMING_SNAKE_CASE)
      sag_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		"""."""
      _lowerCamelCase	:					Union[str, Any]   =		torch.manual_seed(0)
      _lowerCamelCase	:					Optional[int]   =		sag_pipe(
          [prompt]      ,   width=768      ,   height=512      ,   generator=SCREAMING_SNAKE_CASE      ,   guidance_scale=7.5      ,   sag_scale=1.0      ,   num_inference_steps=20      ,   output_type="""np"""      ,   )

      _lowerCamelCase	:					Union[str, Any]   =		output.images

      assert image.shape == (1, 512, 768, 3)

