





"""simple docstring"""





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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_big_bird import BigBirdTokenizer
else:
       _UpperCamelCase							=      None

_UpperCamelCase							=      logging.get_logger(__name__)
_UpperCamelCase							=      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_UpperCamelCase							=      {
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    },
    '''tokenizer_file''': {
        '''google/bigbird-roberta-base''': (
            '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
        ),
    },
}

_UpperCamelCase							=      {
    '''google/bigbird-roberta-base''': 4096,
    '''google/bigbird-roberta-large''': 4096,
    '''google/bigbird-base-trivia-itc''': 4096,
}


_UpperCamelCase							=      '''▁'''
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
    """simple docstring"""
    __snake_case   :       List[str]	 =		VOCAB_FILES_NAMES
    __snake_case   :       Tuple	 =		PRETRAINED_VOCAB_FILES_MAP
    __snake_case   :       str	 =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __snake_case   :       Optional[Any]	 =		BigBirdTokenizer
    __snake_case   :       Dict	 =		['''input_ids''', '''attention_mask''']
    __snake_case   :       List[int]	 =		[]




    def __init__(     self  :Optional[Any]						,    __lowercase  :str=None						,    __lowercase  :Tuple=None						,    __lowercase  :Optional[Any]="<unk>"						,    __lowercase  :Optional[Any]="<s>"						,    __lowercase  :Optional[int]="</s>"						,    __lowercase  :str="<pad>"						,    __lowercase  :Tuple="[SEP]"						,    __lowercase  :Dict="[MASK]"						,    __lowercase  :Optional[int]="[CLS]"						,    **__lowercase  :List[Any]						,    ):
           __lowerCamelCase      :      Optional[Any]         =AddedToken(_A						,    lstrip=_A						,    rstrip=_A			) if isinstance(_A						,    _A			) else bos_token
           __lowerCamelCase      :      Tuple         =AddedToken(_A						,    lstrip=_A						,    rstrip=_A			) if isinstance(_A						,    _A			) else eos_token
           __lowerCamelCase      :      Optional[int]         =AddedToken(_A						,    lstrip=_A						,    rstrip=_A			) if isinstance(_A						,    _A			) else unk_token
           __lowerCamelCase      :      Any         =AddedToken(_A						,    lstrip=_A						,    rstrip=_A			) if isinstance(_A						,    _A			) else pad_token
           __lowerCamelCase      :      Optional[Any]         =AddedToken(_A						,    lstrip=_A						,    rstrip=_A			) if isinstance(_A						,    _A			) else cls_token
           __lowerCamelCase      :      Dict         =AddedToken(_A						,    lstrip=_A						,    rstrip=_A			) if isinstance(_A						,    _A			) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           __lowerCamelCase      :      int         =AddedToken(_A						,    lstrip=_A						,    rstrip=_A			) if isinstance(_A						,    _A			) else mask_token

           super().__init__(
               _A						,    tokenizer_file=_A						,    bos_token=_A						,    eos_token=_A						,    unk_token=_A						,    sep_token=_A						,    pad_token=_A						,    cls_token=_A						,    mask_token=_A						,    **_A						,    )

           __lowerCamelCase      :      int         =vocab_file
           __lowerCamelCase      :      str         =False if not self.vocab_file else True




    def 					__lowercase  (     self  :List[str]						,    __lowercase  :int						,    __lowercase  :Dict = None			):
           __lowerCamelCase      :      List[str]         =[self.sep_token_id]
           __lowerCamelCase      :      List[Any]         =[self.cls_token_id]
           if token_ids_a is None:
                  return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep




    def 					__lowercase  (     self  :List[str]						,    __lowercase  :List[str]						,    __lowercase  :Optional[Any] = None						,    __lowercase  :List[Any] = False			):

           if already_has_special_tokens:
                  if token_ids_a is not None:
                         raise ValueError(
                             '''You should not supply a second sequence if the provided sequence of '''
                             '''ids is already formatted with special tokens for the model.'''			)
                  return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is None:
                  return [1] + ([0] * len(_A			)) + [1]
           return [1] + ([0] * len(_A			)) + [1] + ([0] * len(_A			)) + [1]




    def 					__lowercase  (     self  :Any						,    __lowercase  :Optional[int]						,    __lowercase  :List[Any] = None			):
           __lowerCamelCase      :      Optional[int]         =[self.sep_token_id]
           __lowerCamelCase      :      Optional[Any]         =[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			) * [0] + len(token_ids_a + sep			) * [1]




    def 					__lowercase  (     self  :List[str]						,    __lowercase  :Union[str, Any]						,    __lowercase  :List[str] = None			):
           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                      '''tokenizer.'''			)

           if not os.path.isdir(_A			):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
                  return
           __lowerCamelCase      :      Any         =os.path.join(
               _A						,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

           return (out_vocab_file,)











"""simple docstring"""





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 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Dict	 =		CycleDiffusionPipeline
				__snake_case   :       Optional[int]	 =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    """negative_prompt""",
				    """height""",
				    """width""",
				    """negative_prompt_embeds""",
				}
				__snake_case   :       Any	 =		PipelineTesterMixin.required_optional_params - {"""latents"""}
				__snake_case   :       int	 =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}   )
				__snake_case   :       Optional[int]	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				__snake_case   :       str	 =		IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 					__lowercase  (     self  :Dict			):
											torch.manual_seed(0			)
											__lowerCamelCase      :      List[str]         =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      :      List[Any]         =DDIMScheduler(
											    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='''scaled_linear'''						,    num_train_timesteps=1000						,    clip_sample=__lowercase						,    set_alpha_to_one=__lowercase						,    )
											torch.manual_seed(0			)
											__lowerCamelCase      :      Optional[int]         =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      :      int         =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      :      Optional[Any]         =CLIPTextModel(__lowercase			)
											__lowerCamelCase      :      Tuple         =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

											__lowerCamelCase      :      Optional[int]         ={
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''vae''': vae,
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''safety_checker''': None,
											    '''feature_extractor''': None,
											}
											return components




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :Optional[int]						,    __lowercase  :str=0			):
											__lowerCamelCase      :      List[Any]         =floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__lowercase			)			).to(__lowercase			)
											__lowerCamelCase      :      str         =image / 2 + 0.5
											if str(__lowercase			).startswith('''mps'''			):
																		__lowerCamelCase      :      Union[str, Any]         =torch.manual_seed(__lowercase			)
											else:
																		__lowerCamelCase      :      Any         =torch.Generator(device=__lowercase			).manual_seed(__lowercase			)
											__lowerCamelCase      :      Dict         ={
											    '''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 					__lowercase  (     self  :Optional[int]			):
											__lowerCamelCase      :      int         ='''cpu'''  # ensure determinism for the device-dependent torch.Generator

											__lowerCamelCase      :      Tuple         =self.get_dummy_components()
											__lowerCamelCase      :      List[str]         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      Tuple         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      List[Any]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      int         =pipe(**__lowercase			)
											__lowerCamelCase      :      Dict         =output.images

											__lowerCamelCase      :      Union[str, Any]         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      Optional[Any]         =np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]			)

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




				@unittest.skipIf(torch_device != '''cuda'''						,    '''This test requires a GPU'''			)
				def 					__lowercase  (     self  :str			):
											__lowerCamelCase      :      str         =self.get_dummy_components()
											for name, module in components.items():
																		if hasattr(__lowercase						,    '''half'''			):
																									__lowerCamelCase      :      Union[str, Any]         =module.half()
											__lowerCamelCase      :      int         =CycleDiffusionPipeline(**__lowercase			)
											__lowerCamelCase      :      List[str]         =pipe.to(__lowercase			)
											pipe.set_progress_bar_config(disable=__lowercase			)

											__lowerCamelCase      :      Optional[int]         =self.get_dummy_inputs(__lowercase			)
											__lowerCamelCase      :      Dict         =pipe(**__lowercase			)
											__lowerCamelCase      :      List[str]         =output.images

											__lowerCamelCase      :      Dict         =images[0, -3:, -3:, -1]

											assert images.shape == (1, 32, 32, 3)
											__lowerCamelCase      :      str         =np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]			)

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




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_save_load_local()




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




				@skip_mps
				def 					__lowercase  (     self  :Optional[Any]			):
											return super().test_dict_tuple_outputs_equivalent()




				@skip_mps
				def 					__lowercase  (     self  :str			):
											return super().test_save_load_optional_components()




				@skip_mps
				def 					__lowercase  (     self  :Dict			):
											return super().test_attention_slicing_forward_pass()

@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




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




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Dict         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      Union[str, Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''			)
											__lowerCamelCase      :      Any         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Optional[Any]         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      Optional[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      Optional[Any]         =CycleDiffusionPipeline.from_pretrained(
											    __lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase						,    torch_dtype=torch.floataa						,    revision='''fp16'''			)

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

											__lowerCamelCase      :      Dict         ='''A black colored car'''
											__lowerCamelCase      :      Union[str, Any]         ='''A blue colored car'''

											__lowerCamelCase      :      Dict         =torch.manual_seed(0			)
											__lowerCamelCase      :      Tuple         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Tuple         =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 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Tuple         =load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/cycle-diffusion/black_colored_car.png'''			)
											__lowerCamelCase      :      List[Any]         =load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''			)
											__lowerCamelCase      :      Optional[Any]         =init_image.resize((512, 512)			)

											__lowerCamelCase      :      Any         ='''CompVis/stable-diffusion-v1-4'''
											__lowerCamelCase      :      List[Any]         =DDIMScheduler.from_pretrained(__lowercase						,    subfolder='''scheduler'''			)
											__lowerCamelCase      :      str         =CycleDiffusionPipeline.from_pretrained(__lowercase						,    scheduler=__lowercase						,    safety_checker=__lowercase			)

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

											__lowerCamelCase      :      Any         ='''A black colored car'''
											__lowerCamelCase      :      int         ='''A blue colored car'''

											__lowerCamelCase      :      Tuple         =torch.manual_seed(0			)
											__lowerCamelCase      :      Union[str, Any]         =pipe(
											    prompt=__lowercase						,    source_prompt=__lowercase						,    image=__lowercase						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__lowercase						,    output_type='''np'''						,    )
											__lowerCamelCase      :      Dict         =output.images

											assert np.abs(image - expected_image			).max() < 2e-2




