


import json
import os
from typing import Optional, Tuple

import regex as re

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


SCREAMING_SNAKE_CASE       :		Optional[Any]         =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :		Optional[int]         =				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

SCREAMING_SNAKE_CASE       :		Optional[int]         =				{
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

SCREAMING_SNAKE_CASE       :		str         =				{
    "ctrl": 256,
}

SCREAMING_SNAKE_CASE       :		Dict         =				{
    "Pregnancy": 168_629,
    "Christianity": 7_675,
    "Explain": 106_423,
    "Fitness": 63_440,
    "Saving": 63_163,
    "Ask": 27_171,
    "Ass": 95_985,
    "Joke": 163_509,
    "Questions": 45_622,
    "Thoughts": 49_605,
    "Retail": 52_342,
    "Feminism": 164_338,
    "Writing": 11_992,
    "Atheism": 192_263,
    "Netflix": 48_616,
    "Computing": 39_639,
    "Opinion": 43_213,
    "Alone": 44_967,
    "Funny": 58_917,
    "Gaming": 40_358,
    "Human": 4_088,
    "India": 1_331,
    "Joker": 77_138,
    "Diet": 36_206,
    "Legal": 11_859,
    "Norman": 4_939,
    "Tip": 72_689,
    "Weight": 52_343,
    "Movies": 46_273,
    "Running": 23_425,
    "Science": 2_090,
    "Horror": 37_793,
    "Confession": 60_572,
    "Finance": 12_250,
    "Politics": 16_360,
    "Scary": 191_985,
    "Support": 12_654,
    "Technologies": 32_516,
    "Teenage": 66_160,
    "Event": 32_769,
    "Learned": 67_460,
    "Notion": 182_770,
    "Wikipedia": 37_583,
    "Books": 6_665,
    "Extract": 76_050,
    "Confessions": 102_701,
    "Conspiracy": 75_932,
    "Links": 63_674,
    "Narcissus": 150_425,
    "Relationship": 54_766,
    "Relationships": 134_796,
    "Reviews": 41_671,
    "News": 4_256,
    "Translation": 26_820,
    "multilingual": 128_406,
}




def     UpperCamelCase    (      _a		)					->							int:
     '''simple docstring'''





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

     lowercase_       :List[str]     =       set(_a		)
     return pairs






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   lowercase					:	Dict									=VOCAB_FILES_NAMES
   lowercase					:	Optional[Any]									=PRETRAINED_VOCAB_FILES_MAP
   lowercase					:	int									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase					:	List[Any]									=CONTROL_CODES
   def __init__(     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_="<unk>"   ,							**UpperCamelCase_		):
        super().__init__(unk_token=UpperCamelCase_   ,							**UpperCamelCase_		)

        with open(UpperCamelCase_   ,							encoding='''utf-8'''		) as vocab_handle:
             lowercase_       :Union[str, Any]     =       json.load(UpperCamelCase_		)
        lowercase_       :int     =       {v: k for k, v in self.encoder.items()}
        with open(UpperCamelCase_   ,							encoding='''utf-8'''		) as merges_handle:
             lowercase_       :int     =       merges_handle.read().split('''\n'''		)[1:-1]
        lowercase_       :Dict     =       [tuple(merge.split()		) for merge in merges]
        lowercase_       :Tuple     =       dict(zip(UpperCamelCase_   ,							range(len(UpperCamelCase_		)		)		)		)
        lowercase_       :Tuple     =       {}
   @property
   def 		UpperCamelCase  (     self		):
        return len(self.encoder		)
   def 		UpperCamelCase  (     self		):
        return dict(self.encoder   ,							**self.added_tokens_encoder		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        if token in self.cache:
             return self.cache[token]
        lowercase_       :Dict     =       tuple(UpperCamelCase_		)
        lowercase_       :Any     =       tuple(list(word[:-1]		) + [word[-1] + '''</w>''']		)
        lowercase_       :Optional[int]     =       get_pairs(UpperCamelCase_		)

        if not pairs:
             return token

        while True:
             lowercase_       :str     =       min(UpperCamelCase_   ,							key=lambda UpperCamelCase_		: self.bpe_ranks.get(UpperCamelCase_   ,							float('''inf'''		)		)		)
             if bigram not in self.bpe_ranks:
                  break
             lowercase_    ,      lowercase_       :int     =       bigram
             lowercase_       :Tuple     =       []
             lowercase_       :Optional[Any]     =       0
             while i < len(UpperCamelCase_		):
                  try:
                       lowercase_       :Optional[Any]     =       word.index(UpperCamelCase_   ,							UpperCamelCase_		)
                  except ValueError:
                       new_word.extend(word[i:]		)
                       break
                  else:
                       new_word.extend(word[i:j]		)
                       lowercase_       :Union[str, Any]     =       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
             lowercase_       :Tuple     =       tuple(UpperCamelCase_		)
             lowercase_       :Optional[Any]     =       new_word
             if len(UpperCamelCase_		) == 1:
                  break
             else:
                  lowercase_       :Dict     =       get_pairs(UpperCamelCase_		)
        lowercase_       :List[str]     =       '''@@ '''.join(UpperCamelCase_		)
        lowercase_       :Any     =       word[:-4]
        lowercase_       :Dict     =       word
        return word
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :Optional[int]     =       []

        lowercase_       :Optional[int]     =       re.findall(R'''\S+\n?'''   ,							UpperCamelCase_		)

        for token in words:
             split_tokens.extend(list(self.bpe(UpperCamelCase_		).split(''' '''		)		)		)
        return split_tokens
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        return self.encoder.get(UpperCamelCase_   ,							self.encoder.get(self.unk_token		)		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        return self.decoder.get(UpperCamelCase_   ,							self.unk_token		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :str     =       ''' '''.join(UpperCamelCase_		).replace('''@@ '''   ,							''''''		).strip()
        return out_string




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None		):
        if not os.path.isdir(UpperCamelCase_		):
             logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
             return
        lowercase_       :Union[str, Any]     =       os.path.join(
            UpperCamelCase_   ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
        lowercase_       :Optional[Any]     =       os.path.join(
            UpperCamelCase_   ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']		)

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

        lowercase_       :Tuple     =       0
        with open(UpperCamelCase_   ,							'''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 UpperCamelCase_		: 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!'''		)
                       lowercase_       :List[str]     =       token_index
                  writer.write(''' '''.join(UpperCamelCase_		) + '''\n'''		)
                  index += 1

        return vocab_file, merge_file

   # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
   #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
   #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
   #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
   #     return ''.join(tokens_generated_so_far)





import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   @property
   def 		UpperCamelCase  (     self		):
        lowercase_       :Dict     =       1
        lowercase_       :Optional[Any]     =       3
        lowercase_       :Optional[int]     =       (32, 32)

        lowercase_       :Dict     =       floats_tensor((batch_size, num_channels) + sizes   ,							rng=random.Random(0		)		).to(UpperCamelCase_		)
        return image
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :Optional[int]     =       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   ,							)
        return model
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :Dict     =       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   ,							)
        return model
   @property
   def 		UpperCamelCase  (     self		):
        torch.manual_seed(0		)
        lowercase_       :str     =       RobertaSeriesConfig(
            hidden_size=32   ,							project_dim=32   ,							intermediate_size=37   ,							layer_norm_eps=1E-05   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=5006   ,							)
        return RobertaSeriesModelWithTransformation(UpperCamelCase_		)
   @property
   def 		UpperCamelCase  (     self		):
        def extract(*UpperCamelCase_   ,							**UpperCamelCase_		):






             class      UpperCamelCase					:


                '''simple docstring'''







                def __init__(     self		):
                     lowercase_       :List[str]     =       torch.ones([0]		)




                def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
                     self.pixel_values.to(UpperCamelCase_		)
                     return self
             return Out()

        return extract
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
        lowercase_       :Dict     =       self.dummy_cond_unet
        lowercase_       :Optional[Any]     =       PNDMScheduler(skip_prk_steps=UpperCamelCase_		)
        lowercase_       :Union[str, Any]     =       self.dummy_vae
        lowercase_       :Any     =       self.dummy_text_encoder
        lowercase_       :List[Any]     =       XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
        lowercase_       :List[str]     =       77

        lowercase_       :int     =       self.dummy_image.to(UpperCamelCase_		)
        lowercase_       :Optional[Any]     =       init_image / 2 + 0.5

        # make sure here that pndm scheduler skips prk
        lowercase_       :str     =       AltDiffusionImgaImgPipeline(
            unet=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							vae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							feature_extractor=self.dummy_extractor   ,							)
        lowercase_       :List[str]     =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,							do_normalize=UpperCamelCase_		)
        lowercase_       :int     =       alt_pipe.to(UpperCamelCase_		)
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :Any     =       '''A painting of a squirrel eating a burger'''
        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       alt_pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='''np'''   ,							image=UpperCamelCase_   ,							)

        lowercase_       :Dict     =       output.images

        lowercase_       :Tuple     =       torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
        lowercase_       :Optional[int]     =       alt_pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type='''np'''   ,							image=UpperCamelCase_   ,							return_dict=UpperCamelCase_   ,							)[0]

        lowercase_       :Optional[int]     =       image[0, -3:, -3:, -1]
        lowercase_       :Dict     =       image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        lowercase_       :int     =       np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 5E-3
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 5E-3
   @unittest.skipIf(torch_device != '''cuda'''   ,							'''This test requires a GPU'''		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[str]     =       self.dummy_cond_unet
        lowercase_       :Dict     =       PNDMScheduler(skip_prk_steps=UpperCamelCase_		)
        lowercase_       :Tuple     =       self.dummy_vae
        lowercase_       :Dict     =       self.dummy_text_encoder
        lowercase_       :Tuple     =       XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
        lowercase_       :str     =       77

        lowercase_       :str     =       self.dummy_image.to(UpperCamelCase_		)

        # put models in fp16
        lowercase_       :Union[str, Any]     =       unet.half()
        lowercase_       :Union[str, Any]     =       vae.half()
        lowercase_       :List[str]     =       bert.half()

        # make sure here that pndm scheduler skips prk
        lowercase_       :List[Any]     =       AltDiffusionImgaImgPipeline(
            unet=UpperCamelCase_   ,							scheduler=UpperCamelCase_   ,							vae=UpperCamelCase_   ,							text_encoder=UpperCamelCase_   ,							tokenizer=UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							feature_extractor=self.dummy_extractor   ,							)
        lowercase_       :List[Any]     =       VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,							do_normalize=UpperCamelCase_		)
        lowercase_       :List[str]     =       alt_pipe.to(UpperCamelCase_		)
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

        lowercase_       :str     =       '''A painting of a squirrel eating a burger'''
        lowercase_       :Union[str, Any]     =       torch.manual_seed(0		)
        lowercase_       :Any     =       alt_pipe(
            [prompt]   ,							generator=UpperCamelCase_   ,							num_inference_steps=2   ,							output_type='''np'''   ,							image=UpperCamelCase_   ,							).images

        assert image.shape == (1, 32, 32, 3)




   @unittest.skipIf(torch_device != '''cuda'''   ,							'''This test requires a GPU'''		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Optional[int]     =       load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/img2img/sketch-mountains-input.jpg'''		)
        # resize to resolution that is divisible by 8 but not 16 or 32
        lowercase_       :Optional[Any]     =       init_image.resize((760, 504)		)

        lowercase_       :List[str]     =       '''BAAI/AltDiffusion'''
        lowercase_       :Optional[Any]     =       AltDiffusionImgaImgPipeline.from_pretrained(
            UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							)
        pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)
        pipe.enable_attention_slicing()

        lowercase_       :Optional[Any]     =       '''A fantasy landscape, trending on artstation'''

        lowercase_       :Optional[Any]     =       torch.manual_seed(0		)
        lowercase_       :str     =       pipe(
            prompt=UpperCamelCase_   ,							image=UpperCamelCase_   ,							strength=0.75   ,							guidance_scale=7.5   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							)
        lowercase_       :Optional[Any]     =       output.images[0]

        lowercase_       :Optional[int]     =       image[255:258, 383:386, -1]

        assert image.shape == (504, 760, 3)
        lowercase_       :Any     =       np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]		)

        assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2






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


   '''simple docstring'''







   def 		UpperCamelCase  (     self		):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()




   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/img2img/sketch-mountains-input.jpg'''		)
        lowercase_       :Any     =       init_image.resize((768, 512)		)
        lowercase_       :List[str]     =       load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''		)

        lowercase_       :List[Any]     =       '''BAAI/AltDiffusion'''
        lowercase_       :Tuple     =       AltDiffusionImgaImgPipeline.from_pretrained(
            UpperCamelCase_   ,							safety_checker=UpperCamelCase_   ,							)
        pipe.to(UpperCamelCase_		)
        pipe.set_progress_bar_config(disable=UpperCamelCase_		)
        pipe.enable_attention_slicing()

        lowercase_       :List[str]     =       '''A fantasy landscape, trending on artstation'''

        lowercase_       :Optional[int]     =       torch.manual_seed(0		)
        lowercase_       :Tuple     =       pipe(
            prompt=UpperCamelCase_   ,							image=UpperCamelCase_   ,							strength=0.75   ,							guidance_scale=7.5   ,							generator=UpperCamelCase_   ,							output_type='''np'''   ,							)
        lowercase_       :int     =       output.images[0]

        assert image.shape == (512, 768, 3)
        # img2img is flaky across GPUs even in fp32, so using MAE here
        assert np.abs(expected_image - image		).max() < 1E-2

