
import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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 (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()





class 	_lowerCAmelCase					(	_UpperCAmelCase					,      _UpperCAmelCase					,      _UpperCAmelCase					,      unittest.TestCase       ):
				_lowercase					=StableDiffusionControlNetImgaImgPipeline
				_lowercase					=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
				_lowercase					=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				_lowercase					=IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}       )
				_lowercase					=IMAGE_TO_IMAGE_IMAGE_PARAMS




				def      __a						(					self     )				->      Union[str, Any]:
							torch.manual_seed(0     )
							lowerCAmelCase_						=	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				,				)
							torch.manual_seed(0     )
							lowerCAmelCase_						=	ControlNetModel(
							    block_out_channels=(32, 64)				,				layers_per_block=2				,				in_channels=4				,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,				cross_attention_dim=32				,				conditioning_embedding_out_channels=(16, 32)				,				)
							torch.manual_seed(0     )
							lowerCAmelCase_						=	DDIMScheduler(
							    beta_start=0.00085				,				beta_end=0.012				,				beta_schedule="scaled_linear"				,				clip_sample=_UpperCAmelCase				,				set_alpha_to_one=_UpperCAmelCase				,				)
							torch.manual_seed(0     )
							lowerCAmelCase_						=	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_						=	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=1_000				,				)
							lowerCAmelCase_						=	CLIPTextModel(_UpperCAmelCase     )
							lowerCAmelCase_						=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

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




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=0     )				->      str:
							if str(_UpperCAmelCase     ).startswith("mps"     ):
										lowerCAmelCase_						=	torch.manual_seed(_UpperCAmelCase     )
							else:
										lowerCAmelCase_						=	torch.Generator(device=_UpperCAmelCase     ).manual_seed(_UpperCAmelCase     )

							lowerCAmelCase_						=	2
							lowerCAmelCase_						=	randn_tensor(
							    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,				generator=_UpperCAmelCase				,				device=torch.device(_UpperCAmelCase     )				,				)
							lowerCAmelCase_						=	floats_tensor(control_image.shape				,				rng=random.Random(_UpperCAmelCase     )     ).to(_UpperCAmelCase     )
							lowerCAmelCase_						=	image.cpu().permute(0				,				2				,				3				,				1     )[0]
							lowerCAmelCase_						=	Image.fromarray(np.uinta(_UpperCAmelCase     )     ).convert("RGB"     ).resize((64, 64)     )
							lowerCAmelCase_						=	{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							    '''image''': image,
							    '''control_image''': control_image,
							}

							return inputs




				def      __a						(					self     )				->      Optional[Any]:
							return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3     )




				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()				,				reason="XFormers attention is only available with CUDA and `xformers` installed"				,				)
				def      __a						(					self     )				->      str:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3     )




				def      __a						(					self     )				->      Union[str, Any]:
							self._test_inference_batch_single_identical(expected_max_diff=2e-3     )





class 	_lowerCAmelCase					(	_UpperCAmelCase					,      _UpperCAmelCase					,      unittest.TestCase       ):
				_lowercase					=StableDiffusionControlNetImgaImgPipeline
				_lowercase					=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
				_lowercase					=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				_lowercase					=frozenset([]       )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




				def      __a						(					self     )				->      List[str]:
							torch.manual_seed(0     )
							lowerCAmelCase_						=	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				,				)
							torch.manual_seed(0     )

							def init_weights(_UpperCamelCase     ):
										if isinstance(_UpperCAmelCase				,				torch.nn.Convad     ):
													torch.nn.init.normal(m.weight     )
													m.bias.data.fill_(1.0     )

							lowerCAmelCase_						=	ControlNetModel(
							    block_out_channels=(32, 64)				,				layers_per_block=2				,				in_channels=4				,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,				cross_attention_dim=32				,				conditioning_embedding_out_channels=(16, 32)				,				)
							controlneta.controlnet_down_blocks.apply(_UpperCAmelCase     )

							torch.manual_seed(0     )
							lowerCAmelCase_						=	ControlNetModel(
							    block_out_channels=(32, 64)				,				layers_per_block=2				,				in_channels=4				,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")				,				cross_attention_dim=32				,				conditioning_embedding_out_channels=(16, 32)				,				)
							controlneta.controlnet_down_blocks.apply(_UpperCAmelCase     )

							torch.manual_seed(0     )
							lowerCAmelCase_						=	DDIMScheduler(
							    beta_start=0.00085				,				beta_end=0.012				,				beta_schedule="scaled_linear"				,				clip_sample=_UpperCAmelCase				,				set_alpha_to_one=_UpperCAmelCase				,				)
							torch.manual_seed(0     )
							lowerCAmelCase_						=	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_						=	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=1_000				,				)
							lowerCAmelCase_						=	CLIPTextModel(_UpperCAmelCase     )
							lowerCAmelCase_						=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

							lowerCAmelCase_						=	MultiControlNetModel([controlneta, controlneta]     )

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




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=0     )				->      Optional[int]:
							if str(_UpperCAmelCase     ).startswith("mps"     ):
										lowerCAmelCase_						=	torch.manual_seed(_UpperCAmelCase     )
							else:
										lowerCAmelCase_						=	torch.Generator(device=_UpperCAmelCase     ).manual_seed(_UpperCAmelCase     )

							lowerCAmelCase_						=	2

							lowerCAmelCase_						=	[
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,				generator=_UpperCAmelCase				,				device=torch.device(_UpperCAmelCase     )				,				),
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,				generator=_UpperCAmelCase				,				device=torch.device(_UpperCAmelCase     )				,				),
							]

							lowerCAmelCase_						=	floats_tensor(control_image[0].shape				,				rng=random.Random(_UpperCAmelCase     )     ).to(_UpperCAmelCase     )
							lowerCAmelCase_						=	image.cpu().permute(0				,				2				,				3				,				1     )[0]
							lowerCAmelCase_						=	Image.fromarray(np.uinta(_UpperCAmelCase     )     ).convert("RGB"     ).resize((64, 64)     )
							lowerCAmelCase_						=	{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							    '''image''': image,
							    '''control_image''': control_image,
							}

							return inputs




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	self.get_dummy_components()
							lowerCAmelCase_						=	self.pipeline_class(**_UpperCAmelCase     )
							pipe.to(_UpperCAmelCase     )

							lowerCAmelCase_						=	10.0
							lowerCAmelCase_						=	4

							lowerCAmelCase_						=	self.get_dummy_inputs(_UpperCAmelCase     )
							lowerCAmelCase_						=	steps
							lowerCAmelCase_						=	scale
							lowerCAmelCase_						=	pipe(**_UpperCAmelCase     )[0]

							lowerCAmelCase_						=	self.get_dummy_inputs(_UpperCAmelCase     )
							lowerCAmelCase_						=	steps
							lowerCAmelCase_						=	scale
							lowerCAmelCase_						=	pipe(**_UpperCAmelCase				,				control_guidance_start=0.1				,				control_guidance_end=0.2     )[0]

							lowerCAmelCase_						=	self.get_dummy_inputs(_UpperCAmelCase     )
							lowerCAmelCase_						=	steps
							lowerCAmelCase_						=	scale
							lowerCAmelCase_						=	pipe(**_UpperCAmelCase				,				control_guidance_start=[0.1, 0.3]				,				control_guidance_end=[0.2, 0.7]     )[0]

							lowerCAmelCase_						=	self.get_dummy_inputs(_UpperCAmelCase     )
							lowerCAmelCase_						=	steps
							lowerCAmelCase_						=	scale
							lowerCAmelCase_						=	pipe(**_UpperCAmelCase				,				control_guidance_start=0.4				,				control_guidance_end=[0.5, 0.8]     )[0]

							# make sure that all outputs are different
							assert np.sum(np.abs(output_a - output_a     )     ) > 1e-3
							assert np.sum(np.abs(output_a - output_a     )     ) > 1e-3
							assert np.sum(np.abs(output_a - output_a     )     ) > 1e-3




				def      __a						(					self     )				->      Any:
							return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3     )




				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()				,				reason="XFormers attention is only available with CUDA and `xformers` installed"				,				)
				def      __a						(					self     )				->      List[str]:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3     )




				def      __a						(					self     )				->      List[Any]:
							self._test_inference_batch_single_identical(expected_max_diff=2e-3     )




				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	self.get_dummy_components()
							lowerCAmelCase_						=	self.pipeline_class(**_UpperCAmelCase     )
							pipe.to(_UpperCAmelCase     )
							pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
							with tempfile.TemporaryDirectory() as tmpdir:
										try:
													# save_pretrained is not implemented for Multi-ControlNet
													pipe.save_pretrained(_UpperCAmelCase     )
										except NotImplementedError:
													pass





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




				def      __a						(					self     )				->      Any:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"     )

							lowerCAmelCase_						=	StableDiffusionControlNetImgaImgPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"				,				safety_checker=_UpperCAmelCase				,				controlnet=_UpperCAmelCase     )
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

							lowerCAmelCase_						=	torch.Generator(device="cpu"     ).manual_seed(0     )
							lowerCAmelCase_						=	'''evil space-punk bird'''
							lowerCAmelCase_						=	load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"     ).resize((512, 512)     )
							lowerCAmelCase_						=	load_image(
							    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"     ).resize((512, 512)     )

							lowerCAmelCase_						=	pipe(
							    _UpperCAmelCase				,				_UpperCAmelCase				,				control_image=_UpperCAmelCase				,				generator=_UpperCAmelCase				,				output_type="np"				,				num_inference_steps=50				,				strength=0.6				,				)

							lowerCAmelCase_						=	output.images[0]

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

							lowerCAmelCase_						=	load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"     )

							assert np.abs(expected_image - image     ).max() < 9e-2




"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A			   = logging.get_logger(__name__)

__A			   = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A			   = {
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A			   = {
    "yjernite/retribert-base-uncased": 5_1_2,
}


__A			   = {
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = VOCAB_FILES_NAMES
    _UpperCAmelCase	:str						    = PRETRAINED_VOCAB_FILES_MAP
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _UpperCAmelCase	:Optional[int]						    = PRETRAINED_INIT_CONFIGURATION
    _UpperCAmelCase	:str						    = RetriBertTokenizer
    _UpperCAmelCase	:List[str]						    = ["input_ids", "attention_mask"]






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="[UNK]"    ,			_UpperCAmelCase="[SEP]"    ,			_UpperCAmelCase="[PAD]"    ,			_UpperCAmelCase="[CLS]"    ,			_UpperCAmelCase="[MASK]"    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=None    ,			**_UpperCAmelCase    ,			):
       super().__init__(
           _UpperCAmelCase    ,			tokenizer_file=_UpperCAmelCase    ,			do_lower_case=_UpperCAmelCase    ,			unk_token=_UpperCAmelCase    ,			sep_token=_UpperCAmelCase    ,			pad_token=_UpperCAmelCase    ,			cls_token=_UpperCAmelCase    ,			mask_token=_UpperCAmelCase    ,			tokenize_chinese_chars=_UpperCAmelCase    ,			strip_accents=_UpperCAmelCase    ,			**_UpperCAmelCase    ,			)

       lowercase__:   Optional[Any]					=      json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
       if (
           normalizer_state.get('''lowercase'''    ,			_UpperCAmelCase							) != do_lower_case
           or normalizer_state.get('''strip_accents'''    ,			_UpperCAmelCase							) != strip_accents
           or normalizer_state.get('''handle_chinese_chars'''    ,			_UpperCAmelCase							) != tokenize_chinese_chars
       ):
          lowercase__:   str					=      getattr(_UpperCAmelCase    ,			normalizer_state.pop('''type'''							)							)
          lowercase__:   Optional[Any]					=      do_lower_case
          lowercase__:   Tuple					=      strip_accents
          lowercase__:   str					=      tokenize_chinese_chars
          lowercase__:   Union[str, Any]					=      normalizer_class(**_UpperCAmelCase							)

       lowercase__:   List[str]					=      do_lower_case






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=None							):
       lowercase__:   Union[str, Any]					=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
          output += token_ids_a + [self.sep_token_id]

       return output






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   Optional[int]					=      [self.sep_token_id]
       lowercase__:   Union[str, 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 _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   List[str]					=      self._tokenizer.model.save(_UpperCAmelCase    ,			name=_UpperCAmelCase							)
       return tuple(_UpperCAmelCase							)
