
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        lowercase      (	unittest.TestCase     ):



					def __UpperCamelCase				(				self )						->      List[str]:
							"""simple docstring"""
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



					@property
					def __UpperCamelCase				(				self )						->      Tuple:
							"""simple docstring"""
							UpperCamelCase						=  1
							UpperCamelCase						=  3
							UpperCamelCase						=  (32, 32)

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



					@property
					def __UpperCamelCase				(				self )						->      List[Any]:
							"""simple docstring"""
							torch.manual_seed(0 )
							UpperCamelCase						=  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 )						->      List[Any]:
							"""simple docstring"""
							torch.manual_seed(0 )
							UpperCamelCase						=  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 )						->      Dict:
							"""simple docstring"""
							torch.manual_seed(0 )
							UpperCamelCase						=  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=5_006       ,   )
							return RobertaSeriesModelWithTransformation(UpperCamelCase__ )



					@property
					def __UpperCamelCase				(				self )						->      Union[str, Any]:
							"""simple docstring"""





							def extract(*A_       ,   **A_ ):


									class        lowercase      :



														def __init__(				self )						->      List[Any]:
																"""simple docstring"""
																UpperCamelCase						=  torch.ones([0] )



														def __UpperCamelCase				(				self       ,   A_ )						->      Dict:
																"""simple docstring"""
																self.pixel_values.to(UpperCamelCase__ )
																return self






									return Out()

							return extract



					def __UpperCamelCase				(				self )						->      Optional[Any]:
							"""simple docstring"""
							UpperCamelCase						=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase						=  self.dummy_cond_unet
							UpperCamelCase						=  PNDMScheduler(skip_prk_steps=UpperCamelCase__ )
							UpperCamelCase						=  self.dummy_vae
							UpperCamelCase						=  self.dummy_text_encoder
							UpperCamelCase						=  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta' )
							UpperCamelCase						=  77

							UpperCamelCase						=  self.dummy_image.to(UpperCamelCase__ )
							UpperCamelCase						=  init_image / 2 + 0.5

							# make sure here that pndm scheduler skips prk
							UpperCamelCase						=  AltDiffusionImgaImgPipeline(
							    unet=UpperCamelCase__       ,   scheduler=UpperCamelCase__       ,   vae=UpperCamelCase__       ,   text_encoder=UpperCamelCase__       ,   tokenizer=UpperCamelCase__       ,   safety_checker=UpperCamelCase__       ,   feature_extractor=self.dummy_extractor       ,   )
							UpperCamelCase						=  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,   do_normalize=UpperCamelCase__ )
							UpperCamelCase						=  alt_pipe.to(UpperCamelCase__ )
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase__ )

							UpperCamelCase						=  '''A painting of a squirrel eating a burger'''
							UpperCamelCase						=  torch.Generator(device=UpperCamelCase__ ).manual_seed(0 )
							UpperCamelCase						=  alt_pipe(
							    [prompt]       ,   generator=UpperCamelCase__       ,   guidance_scale=6.0       ,   num_inference_steps=2       ,   output_type='np'       ,   image=UpperCamelCase__       ,   )

							UpperCamelCase						=  output.images

							UpperCamelCase						=  torch.Generator(device=UpperCamelCase__ ).manual_seed(0 )
							UpperCamelCase						=  alt_pipe(
							    [prompt]       ,   generator=UpperCamelCase__       ,   guidance_scale=6.0       ,   num_inference_steps=2       ,   output_type='np'       ,   image=UpperCamelCase__       ,   return_dict=UpperCamelCase__       ,   )[0]

							UpperCamelCase						=  image[0, -3:, -3:, -1]
							UpperCamelCase						=  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase						=  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 )						->      int:
							"""simple docstring"""
							UpperCamelCase						=  self.dummy_cond_unet
							UpperCamelCase						=  PNDMScheduler(skip_prk_steps=UpperCamelCase__ )
							UpperCamelCase						=  self.dummy_vae
							UpperCamelCase						=  self.dummy_text_encoder
							UpperCamelCase						=  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta' )
							UpperCamelCase						=  77

							UpperCamelCase						=  self.dummy_image.to(UpperCamelCase__ )

							# put models in fp16
							UpperCamelCase						=  unet.half()
							UpperCamelCase						=  vae.half()
							UpperCamelCase						=  bert.half()

							# make sure here that pndm scheduler skips prk
							UpperCamelCase						=  AltDiffusionImgaImgPipeline(
							    unet=UpperCamelCase__       ,   scheduler=UpperCamelCase__       ,   vae=UpperCamelCase__       ,   text_encoder=UpperCamelCase__       ,   tokenizer=UpperCamelCase__       ,   safety_checker=UpperCamelCase__       ,   feature_extractor=self.dummy_extractor       ,   )
							UpperCamelCase						=  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,   do_normalize=UpperCamelCase__ )
							UpperCamelCase						=  alt_pipe.to(UpperCamelCase__ )
							alt_pipe.set_progress_bar_config(disable=UpperCamelCase__ )

							UpperCamelCase						=  '''A painting of a squirrel eating a burger'''
							UpperCamelCase						=  torch.manual_seed(0 )
							UpperCamelCase						=  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 )						->      Union[str, Any]:
							"""simple docstring"""
							UpperCamelCase						=  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
							UpperCamelCase						=  init_image.resize((760, 504) )

							UpperCamelCase						=  '''BAAI/AltDiffusion'''
							UpperCamelCase						=  AltDiffusionImgaImgPipeline.from_pretrained(
							    UpperCamelCase__       ,   safety_checker=UpperCamelCase__       ,   )
							pipe.to(UpperCamelCase__ )
							pipe.set_progress_bar_config(disable=UpperCamelCase__ )
							pipe.enable_attention_slicing()

							UpperCamelCase						=  '''A fantasy landscape, trending on artstation'''

							UpperCamelCase						=  torch.manual_seed(0 )
							UpperCamelCase						=  pipe(
							    prompt=UpperCamelCase__       ,   image=UpperCamelCase__       ,   strength=0.75       ,   guidance_scale=7.5       ,   generator=UpperCamelCase__       ,   output_type='np'       ,   )
							UpperCamelCase						=  output.images[0]

							UpperCamelCase						=  image[255:258, 383:386, -1]

							assert image.shape == (504, 760, 3)
							UpperCamelCase						=  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        lowercase      (	unittest.TestCase     ):



					def __UpperCamelCase				(				self )						->      Optional[int]:
							"""simple docstring"""
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



					def __UpperCamelCase				(				self )						->      Dict:
							"""simple docstring"""
							UpperCamelCase						=  load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg' )
							UpperCamelCase						=  init_image.resize((768, 512) )
							UpperCamelCase						=  load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy' )

							UpperCamelCase						=  '''BAAI/AltDiffusion'''
							UpperCamelCase						=  AltDiffusionImgaImgPipeline.from_pretrained(
							    UpperCamelCase__       ,   safety_checker=UpperCamelCase__       ,   )
							pipe.to(UpperCamelCase__ )
							pipe.set_progress_bar_config(disable=UpperCamelCase__ )
							pipe.enable_attention_slicing()

							UpperCamelCase						=  '''A fantasy landscape, trending on artstation'''

							UpperCamelCase						=  torch.manual_seed(0 )
							UpperCamelCase						=  pipe(
							    prompt=UpperCamelCase__       ,   image=UpperCamelCase__       ,   strength=0.75       ,   guidance_scale=7.5       ,   generator=UpperCamelCase__       ,   output_type='np'       ,   )
							UpperCamelCase						=  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


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      :				Dict          = logging.get_logger(__name__)


_UpperCAmelCase      :				Optional[Any]          = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_UpperCAmelCase      :				str          = {
    "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      :				List[str]          = {"facebook/blenderbot_small-90M": 512}


def    A (      lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  set()
		UpperCamelCase						=  word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)				)
				UpperCamelCase						=  char

		UpperCamelCase						=  set(lowercase				)
		return pairs


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Optional[Any]			  =       VOCAB_FILES_NAMES
		__lowercase : Tuple			  =       PRETRAINED_VOCAB_FILES_MAP
		__lowercase : List[Any]			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase : Any			  =       ["input_ids", "attention_mask"]



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

				with open(A_       ,   encoding='utf-8' ) as vocab_handle:
						UpperCamelCase						=  json.load(A_ )
				UpperCamelCase						=  {v: k for k, v in self.encoder.items()}
				with open(A_       ,   encoding='utf-8' ) as merges_handle:
						UpperCamelCase						=  merges_handle.read().split('\n' )[1:-1]
				UpperCamelCase						=  [tuple(merge.split() ) for merge in merges]
				UpperCamelCase						=  dict(zip(A_       ,   range(len(A_ ) ) ) )
				UpperCamelCase						=  {}



		@property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return len(self.encoder )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				return dict(self.encoder       ,   **self.added_tokens_encoder )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				if token in self.cache:
						return self.cache[token]
				UpperCamelCase						=  re.sub('([.,!?()])'       ,   r' \1'       ,   A_ )
				UpperCamelCase						=  re.sub('(\')'       ,   r' \1 '       ,   A_ )
				UpperCamelCase						=  re.sub(r'\s{2,}'       ,   ' '       ,   A_ )
				if "\n" in token:
						UpperCamelCase						=  token.replace('\n'       ,   ' __newln__' )

				UpperCamelCase						=  token.split(' ' )
				UpperCamelCase						=  []
				for token in tokens:
						if not len(A_ ):
								continue

						UpperCamelCase						=  token.lower()
						UpperCamelCase						=  tuple(A_ )
						UpperCamelCase						=  tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
						UpperCamelCase						=  get_pairs(A_ )

						if not pairs:
								words.append(A_ )
								continue

						while True:
								UpperCamelCase						=  min(A_       ,   key=lambda A_ : self.bpe_ranks.get(A_       ,   float('inf' ) ) )
								if bigram not in self.bpe_ranks:
										break
								UpperCamelCase			,						UpperCamelCase						=  bigram
								UpperCamelCase						=  []
								UpperCamelCase						=  0

								while i < len(A_ ):
										try:
												UpperCamelCase						=  word.index(A_       ,   A_ )
												new_word.extend(word[i:j] )
												UpperCamelCase						=  j
										except ValueError:
												new_word.extend(word[i:] )
												break

										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
								UpperCamelCase						=  tuple(A_ )
								UpperCamelCase						=  new_word
								if len(A_ ) == 1:
										break
								else:
										UpperCamelCase						=  get_pairs(A_ )
						UpperCamelCase						=  '@@ '.join(A_ )
						UpperCamelCase						=  word[:-4]

						UpperCamelCase						=  word
						words.append(A_ )
				return " ".join(A_ )



		def __UpperCamelCase				(				self       ,   A_ )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  []

				UpperCamelCase						=  re.findall(r'\S+\n?'       ,   A_ )

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



		def __UpperCamelCase				(				self       ,   A_ )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  token.lower()
				return self.encoder.get(A_       ,   self.encoder.get(self.unk_token ) )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				return self.decoder.get(A_       ,   self.unk_token )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  ' '.join(A_ ).replace('@@ '       ,   '' ).strip()
				return out_string



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

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

				UpperCamelCase						=  0
				with open(A_       ,   '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 A_ : 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!' )
										UpperCamelCase						=  token_index
								writer.write(' '.join(A_ ) + '\n' )
								index += 1

				return vocab_file, merge_file
