





'''simple docstring'''




from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
			import tensorflow as tf

			from ..tf_utils import stable_softmax


if is_torch_available():
			import torch


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






@add_end_docstrings(
    __magic_name__       ,       r'''
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    '''       ,       )
class 				__snake_case     (					__magic_name__							):





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		np.ndarray:
		if self.framework == "tf":
			snake_case__	       =   tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()
		elif self.framework == "pt":
			snake_case__	       =   torch.nonzero(input_ids == self.tokenizer.mask_token_id      ,       as_tuple=UpperCamelCase_		)
		else:
			raise ValueError('Unsupported framework'		)
		return masked_index





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		np.ndarray:
		snake_case__	       =   self.get_masked_index(UpperCamelCase_		)
		snake_case__	       =   np.prod(masked_index.shape		)
		if numel < 1:
			raise PipelineException(
			    'fill-mask'      ,       self.model.base_model_prefix      ,       F'''No mask_token ({self.tokenizer.mask_token}) found on the input'''      ,       )





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		str:
		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		):
			for model_input in model_inputs:
				self._ensure_exactly_one_mask_token(model_input['input_ids'][0]		)
		else:
			for input_ids in model_inputs["input_ids"]:
				self._ensure_exactly_one_mask_token(UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=None      ,       **UpperCamelCase_		)      ->		Dict[str, GenericTensor]:
		if return_tensors is None:
			snake_case__	       =   self.framework
		snake_case__	       =   self.tokenizer(UpperCamelCase_      ,       return_tensors=UpperCamelCase_		)
		self.ensure_exactly_one_mask_token(UpperCamelCase_		)
		return model_inputs





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		str:
		snake_case__	       =   self.model(**UpperCamelCase_		)
		snake_case__	       =   model_inputs['input_ids']
		return model_outputs





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=5      ,       UpperCamelCase_=None		)      ->		List[Any]:
		# Cap top_k if there are targets
		if target_ids is not None and target_ids.shape[0] < top_k:
			snake_case__	       =   target_ids.shape[0]
		snake_case__	       =   model_outputs['input_ids'][0]
		snake_case__	       =   model_outputs['logits']

		if self.framework == "tf":
			snake_case__	       =   tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()[:, 0]

			snake_case__	       =   outputs.numpy()

			snake_case__	       =   outputs[0, masked_index, :]
			snake_case__	       =   stable_softmax(UpperCamelCase_      ,       axis=-1		)
			if target_ids is not None:
				snake_case__	       =   tf.gather_nd(tf.squeeze(UpperCamelCase_      ,       0		)      ,       target_ids.reshape(-1      ,       1		)		)
				snake_case__	       =   tf.expand_dims(UpperCamelCase_      ,       0		)

			snake_case__	       =   tf.math.top_k(UpperCamelCase_      ,       k=UpperCamelCase_		)
			snake_case__       ,  snake_case__	       =   topk.values.numpy(), topk.indices.numpy()
		else:
			snake_case__	       =   torch.nonzero(input_ids == self.tokenizer.mask_token_id      ,       as_tuple=UpperCamelCase_		).squeeze(-1		)
			# Fill mask pipeline supports only one ${mask_token} per sample

			snake_case__	       =   outputs[0, masked_index, :]
			snake_case__	       =   logits.softmax(dim=-1		)
			if target_ids is not None:
				snake_case__	       =   probs[..., target_ids]

			snake_case__       ,  snake_case__	       =   probs.topk(UpperCamelCase_		)

		snake_case__	       =   []
		snake_case__	       =   values.shape[0] == 1
		for i, (_values, _predictions) in enumerate(zip(values.tolist()      ,       predictions.tolist()		)		):
			snake_case__	       =   []
			for v, p in zip(_values      ,       _predictions		):
				# Copy is important since we're going to modify this array in place
				snake_case__	       =   input_ids.numpy().copy()
				if target_ids is not None:
					snake_case__	       =   target_ids[p].tolist()

				snake_case__	       =   p
				# Filter padding out:
				snake_case__	       =   tokens[np.where(tokens != self.tokenizer.pad_token_id		)]
				# Originally we skip special tokens to give readable output.
				# For multi masks though, the other [MASK] would be removed otherwise
				# making the output look odd, so we add them back
				snake_case__	       =   self.tokenizer.decode(UpperCamelCase_      ,       skip_special_tokens=UpperCamelCase_		)
				snake_case__	       =   {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]		), 'sequence': sequence}
				row.append(UpperCamelCase_		)
			result.append(UpperCamelCase_		)
		if single_mask:
			return result[0]
		return result





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=None		)      ->		Union[str, Any]:
		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		):
			snake_case__	       =   [targets]
		try:
			snake_case__	       =   self.tokenizer.get_vocab()
		except Exception:
			snake_case__	       =   {}
		snake_case__	       =   []
		for target in targets:
			snake_case__	       =   vocab.get(UpperCamelCase_      ,       UpperCamelCase_		)
			if id_ is None:
				snake_case__	       =   self.tokenizer(
				    UpperCamelCase_      ,       add_special_tokens=UpperCamelCase_      ,       return_attention_mask=UpperCamelCase_      ,       return_token_type_ids=UpperCamelCase_      ,       max_length=1      ,       truncation=UpperCamelCase_      ,       )['input_ids']
				if len(UpperCamelCase_		) == 0:
					logger.warning(
					    F'''The specified target token `{target}` does not exist in the model vocabulary. '''
					    'We cannot replace it with anything meaningful, ignoring it'		)
					continue
				snake_case__	       =   input_ids[0]
				# XXX: If users encounter this pass
				# it becomes pretty slow, so let's make sure
				# The warning enables them to fix the input to
				# get faster performance.
				logger.warning(
				    F'''The specified target token `{target}` does not exist in the model vocabulary. '''
				    F'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_		)}`.'''		)
			target_ids.append(id_		)
		snake_case__	       =   list(set(UpperCamelCase_		)		)
		if len(UpperCamelCase_		) == 0:
			raise ValueError('At least one target must be provided when passed.'		)
		snake_case__	       =   np.array(UpperCamelCase_		)
		return target_ids





	def   _snake_case   (		self      ,       UpperCamelCase_=None      ,       UpperCamelCase_=None		)      ->		Optional[int]:
		snake_case__	       =   {}

		if targets is not None:
			snake_case__	       =   self.get_target_ids(UpperCamelCase_      ,       UpperCamelCase_		)
			snake_case__	       =   target_ids

		if top_k is not None:
			snake_case__	       =   top_k

		if self.tokenizer.mask_token_id is None:
			raise PipelineException(
			    'fill-mask'      ,       self.model.base_model_prefix      ,       'The tokenizer does not define a `mask_token`.'		)
		return {}, {}, postprocess_params





	def __call__(		self      ,       UpperCamelCase_      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		Optional[int]:
		snake_case__	       =   super().__call__(UpperCamelCase_      ,       **UpperCamelCase_		)
		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		) and len(UpperCamelCase_		) == 1:
			return outputs[0]
		return outputs










'''simple docstring'''




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, 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


enable_full_determinism()






class 				__snake_case     (					unittest.TestCase							):





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





	@property
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   1
		snake_case__	       =   3
		snake_case__	       =   (32, 32)

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





	@property
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		torch.manual_seed(0		)
		snake_case__	       =   UNetaDConditionModel(
		    block_out_channels=(32, 32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=7      ,       out_channels=4      ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')      ,       up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')      ,       cross_attention_dim=32      ,       attention_head_dim=8      ,       use_linear_projection=UpperCamelCase_      ,       only_cross_attention=(True, True, False)      ,       num_class_embeds=100      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		Optional[Any]:
		torch.manual_seed(0		)
		snake_case__	       =   AutoencoderKL(
		    block_out_channels=[32, 32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']      ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']      ,       latent_channels=4      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		List[str]:
		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      ,       hidden_act='gelu'      ,       projection_dim=512      ,       )
		return CLIPTextModel(UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )

		snake_case__	       =   output.images

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       return_dict=UpperCamelCase_      ,       )[0]

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

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)
		snake_case__	       =   np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]		)

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





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   sd_pipe(
		    2 * [prompt]      ,       image=2 * [low_res_image]      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_images_per_prompt=2      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2





	@unittest.skipIf(torch_device != 'cuda'      ,       'This test requires a GPU'		)
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# put models in fp16, except vae as it overflows in fp16
		snake_case__	       =   unet.half()
		snake_case__	       =   text_encoder.half()

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=2      ,       output_type='np'      ,       ).images

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)






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





	def   _snake_case   (		self		)      ->		List[str]:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase_		)
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 1E-3





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat_fp16.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 5E-1





	def   _snake_case   (		self		)      ->		int:
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing(1		)
		pipe.enable_sequential_cpu_offload()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=5      ,       output_type='np'      ,       )

		snake_case__	       =   torch.cuda.max_memory_allocated()
		# make sure that less than 2.9 GB is allocated
		assert mem_bytes < 2.9 * 10**9



