

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 					lowercase							(			lowercase_      ,    lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     CycleDiffusionPipeline
				__SCREAMING_SNAKE_CASE   :					int			     =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    '''negative_prompt''',
				    '''height''',
				    '''width''',
				    '''negative_prompt_embeds''',
				}
				__SCREAMING_SNAKE_CASE   :					Any			     =     PipelineTesterMixin.required_optional_params - {'''latents'''}
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}     )
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     IMAGE_TO_IMAGE_IMAGE_PARAMS
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     IMAGE_TO_IMAGE_IMAGE_PARAMS




				def        a				(   self			):
											torch.manual_seed(0			)
											snake_case_     							=       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 ,  )
											snake_case_     							=       DDIMScheduler(
											    beta_start=0.0_00_85 ,  beta_end=0.0_12 ,  beta_schedule='scaled_linear' ,  num_train_timesteps=1000 ,  clip_sample=snake_case ,  set_alpha_to_one=snake_case ,  )
											torch.manual_seed(0			)
											snake_case_     							=       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			)
											snake_case_     							=       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 ,  )
											snake_case_     							=       CLIPTextModel(snake_case			)
											snake_case_     							=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

											snake_case_     							=       {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components




				def        a				(   self ,  snake_case ,  snake_case=0			):
											snake_case_     							=       floats_tensor((1, 3, 32, 32) ,  rng=random.Random(snake_case			)			).to(snake_case			)
											snake_case_     							=       image / 2 + 0.5
											if str(snake_case			).startswith('mps'			):
																		snake_case_     							=       torch.manual_seed(snake_case			)
											else:
																		snake_case_     							=       torch.Generator(device=snake_case			).manual_seed(snake_case			)
											snake_case_     							=       {
											    'prompt': 'An astronaut riding an elephant',
											    'source_prompt': 'An astronaut riding a horse',
											    'image': image,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'eta': 0.1,
											    'strength': 0.8,
											    'guidance_scale': 3,
											    'source_guidance_scale': 1,
											    'output_type': 'numpy',
											}
											return inputs




				def        a				(   self			):
											snake_case_     							=       'cpu'  # ensure determinism for the device-dependent torch.Generator

											snake_case_     							=       self.get_dummy_components()
											snake_case_     							=       CycleDiffusionPipeline(**snake_case			)
											snake_case_     							=       pipe.to(snake_case			)
											pipe.set_progress_bar_config(disable=snake_case			)

											snake_case_     							=       self.get_dummy_inputs(snake_case			)
											snake_case_     							=       pipe(**snake_case			)
											snake_case_     							=       output.images

											snake_case_     							=       images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											snake_case_     							=       np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]			)

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




				@unittest.skipIf(torch_device != 'cuda' ,  'This test requires a GPU'			)
				def        a				(   self			):
											snake_case_     							=       self.get_dummy_components()
											for name, module in components.items():
																		if hasattr(snake_case ,  'half'			):
																									snake_case_     							=       module.half()
											snake_case_     							=       CycleDiffusionPipeline(**snake_case			)
											snake_case_     							=       pipe.to(snake_case			)
											pipe.set_progress_bar_config(disable=snake_case			)

											snake_case_     							=       self.get_dummy_inputs(snake_case			)
											snake_case_     							=       pipe(**snake_case			)
											snake_case_     							=       output.images

											snake_case_     							=       images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											snake_case_     							=       np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]			)

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




				@skip_mps
				def        a				(   self			):
											return super().test_save_load_local()




				@unittest.skip('non-deterministic pipeline'			)
				def        a				(   self			):
											return super().test_inference_batch_single_identical()




				@skip_mps
				def        a				(   self			):
											return super().test_dict_tuple_outputs_equivalent()




				@skip_mps
				def        a				(   self			):
											return super().test_save_load_optional_components()




				@skip_mps
				def        a				(   self			):
											return super().test_attention_slicing_forward_pass()







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




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




				def        a				(   self			):
											snake_case_     							=       load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/cycle-diffusion/black_colored_car.png'			)
											snake_case_     							=       load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'			)
											snake_case_     							=       init_image.resize((512, 512)			)

											snake_case_     							=       'CompVis/stable-diffusion-v1-4'
											snake_case_     							=       DDIMScheduler.from_pretrained(snake_case ,  subfolder='scheduler'			)
											snake_case_     							=       CycleDiffusionPipeline.from_pretrained(
											    snake_case ,  scheduler=snake_case ,  safety_checker=snake_case ,  torch_dtype=torch.floataa ,  revision='fp16'			)

											pipe.to(snake_case			)
											pipe.set_progress_bar_config(disable=snake_case			)
											pipe.enable_attention_slicing()

											snake_case_     							=       'A black colored car'
											snake_case_     							=       'A blue colored car'

											snake_case_     							=       torch.manual_seed(0			)
											snake_case_     							=       pipe(
											    prompt=snake_case ,  source_prompt=snake_case ,  image=snake_case ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=snake_case ,  output_type='np' ,  )
											snake_case_     							=       output.images

											# the values aren't exactly equal, but the images look the same visually
											assert np.abs(image - expected_image			).max() < 5e-1




				def        a				(   self			):
											snake_case_     							=       load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/cycle-diffusion/black_colored_car.png'			)
											snake_case_     							=       load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'			)
											snake_case_     							=       init_image.resize((512, 512)			)

											snake_case_     							=       'CompVis/stable-diffusion-v1-4'
											snake_case_     							=       DDIMScheduler.from_pretrained(snake_case ,  subfolder='scheduler'			)
											snake_case_     							=       CycleDiffusionPipeline.from_pretrained(snake_case ,  scheduler=snake_case ,  safety_checker=snake_case			)

											pipe.to(snake_case			)
											pipe.set_progress_bar_config(disable=snake_case			)
											pipe.enable_attention_slicing()

											snake_case_     							=       'A black colored car'
											snake_case_     							=       'A blue colored car'

											snake_case_     							=       torch.manual_seed(0			)
											snake_case_     							=       pipe(
											    prompt=snake_case ,  source_prompt=snake_case ,  image=snake_case ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=snake_case ,  output_type='np' ,  )
											snake_case_     							=       output.images

											assert np.abs(image - expected_image			).max() < 2e-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						:  Tuple									=			logging.get_logger(__name__)


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

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





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






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

							snake_case_     							=       set(UpperCamelCase__ )
							return pairs







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Optional[Any]			     =     VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE   :					Any			     =     PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE   :					str			     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     ['''input_ids''', '''attention_mask''']




				def __init__(   self ,  snake_case ,  snake_case ,  snake_case="__start__" ,  snake_case="__end__" ,  snake_case="__unk__" ,  snake_case="__null__" ,  **snake_case ,  ):
											super().__init__(unk_token=snake_case ,  bos_token=snake_case ,  eos_token=snake_case ,  pad_token=snake_case ,  **snake_case			)

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




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




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




				def        a				(   self ,  snake_case			):
											if token in self.cache:
																		return self.cache[token]
											snake_case_     							=       re.sub('([.,!?()])' ,  R' \1' ,  snake_case			)
											snake_case_     							=       re.sub('(\')' ,  R' \1 ' ,  snake_case			)
											snake_case_     							=       re.sub(R'\s{2,}' ,  ' ' ,  snake_case			)
											if "\n" in token:
																		snake_case_     							=       token.replace('\n' ,  ' __newln__'			)

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

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

																		if not pairs:
																									words.append(snake_case			)
																									continue

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

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

																																if word[i] == first and i < len(snake_case			) - 1 and word[i + 1] == second:
																																							new_word.append(first + second			)
																																							i += 2
																																else:
																																							new_word.append(word[i]			)
																																							i += 1
																									snake_case_     							=       tuple(snake_case			)
																									snake_case_     							=       new_word
																									if len(snake_case			) == 1:
																																break
																									else:
																																snake_case_     							=       get_pairs(snake_case			)
																		snake_case_     							=       '@@ '.join(snake_case			)
																		snake_case_     							=       word[:-4]

																		snake_case_     							=       word
																		words.append(snake_case			)
											return " ".join(snake_case			)




				def        a				(   self ,  snake_case			):
											snake_case_     							=       []

											snake_case_     							=       re.findall(R'\S+\n?' ,  snake_case			)

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




				def        a				(   self ,  snake_case			):
											snake_case_     							=       token.lower()
											return self.encoder.get(snake_case ,  self.encoder.get(self.unk_token			)			)




				def        a				(   self ,  snake_case			):
											return self.decoder.get(snake_case ,  self.unk_token			)




				def        a				(   self ,  snake_case			):
											snake_case_     							=       ' '.join(snake_case			).replace('@@ ' ,  ''			).strip()
											return out_string




				def        a				(   self ,  snake_case ,  snake_case = None			):
											if not os.path.isdir(snake_case			):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
																		return
											snake_case_     							=       os.path.join(
											    snake_case ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
											snake_case_     							=       os.path.join(
											    snake_case ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']			)

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

											snake_case_     							=       0
											with open(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 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!'			)
																																snake_case_     							=       token_index
																									writer.write(' '.join(snake_case			) + '\n'			)
																									index += 1

											return vocab_file, merge_file




