



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


lowerCAmelCase__		:					Union[str, Any]							=logging.get_logger(__name__)

@add_end_docstrings(
    UpperCamelCase_   ,   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        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													if self.framework == "tf":
																				__SCREAMING_SNAKE_CASE									= tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()
													elif self.framework == "pt":
																				__SCREAMING_SNAKE_CASE									= torch.nonzero(input_ids == self.tokenizer.mask_token_id				,							as_tuple=_A		)
													else:
																				raise ValueError('Unsupported framework'		)
													return masked_index






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_masked_index(_A		)
													__SCREAMING_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 	_A     (   self				,							_A		):







													'''simple docstring'''

													if isinstance(_A				,							_A		):
																				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(_A		)






						def 	_A     (   self				,							_A				,							_A=None				,							**_A		):







													'''simple docstring'''

													if return_tensors is None:
																				__SCREAMING_SNAKE_CASE									= self.framework
													__SCREAMING_SNAKE_CASE									= self.tokenizer(_A				,							return_tensors=_A		)
													self.ensure_exactly_one_mask_token(_A		)
													return model_inputs






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model(**_A		)
													__SCREAMING_SNAKE_CASE									= model_inputs['input_ids']
													return model_outputs






						def 	_A     (   self				,							_A				,							_A=5				,							_A=None		):







													'''simple docstring'''

													if target_ids is not None and target_ids.shape[0] < top_k:
																				__SCREAMING_SNAKE_CASE									= target_ids.shape[0]
													__SCREAMING_SNAKE_CASE									= model_outputs['input_ids'][0]
													__SCREAMING_SNAKE_CASE									= model_outputs['logits']

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

																				__SCREAMING_SNAKE_CASE									= outputs.numpy()

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

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

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

																				__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= probs.topk(_A		)

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

																											__SCREAMING_SNAKE_CASE									= p
																											# Filter padding out:
																											__SCREAMING_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
																											__SCREAMING_SNAKE_CASE									= self.tokenizer.decode(_A				,							skip_special_tokens=_A		)
																											__SCREAMING_SNAKE_CASE									= {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]		), 'sequence': sequence}
																											row.append(_A		)
																				result.append(_A		)
													if single_mask:
																				return result[0]
													return result






						def 	_A     (   self				,							_A				,							_A=None		):







													'''simple docstring'''

													if isinstance(_A				,							_A		):
																				__SCREAMING_SNAKE_CASE									= [targets]
													try:
																				__SCREAMING_SNAKE_CASE									= self.tokenizer.get_vocab()
													except Exception:
																				__SCREAMING_SNAKE_CASE									= {}
													__SCREAMING_SNAKE_CASE									= []
													for target in targets:
																				__SCREAMING_SNAKE_CASE									= vocab.get(_A				,							_A		)
																				if id_ is None:
																											__SCREAMING_SNAKE_CASE									= self.tokenizer(
																											    _A				,							add_special_tokens=_A				,							return_attention_mask=_A				,							return_token_type_ids=_A				,							max_length=1				,							truncation=_A				,							)['input_ids']
																											if len(_A		) == 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
																											__SCREAMING_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_		)
													__SCREAMING_SNAKE_CASE									= list(set(_A		)		)
													if len(_A		) == 0:
																				raise ValueError('At least one target must be provided when passed.'		)
													__SCREAMING_SNAKE_CASE									= np.array(_A		)
													return target_ids






						def 	_A     (   self				,							_A=None				,							_A=None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= {}

													if targets is not None:
																				__SCREAMING_SNAKE_CASE									= self.get_target_ids(_A				,							_A		)
																				__SCREAMING_SNAKE_CASE									= target_ids

													if top_k is not None:
																				__SCREAMING_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				,							_A				,							*_A				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= super().__call__(_A				,							**_A		)
													if isinstance(_A				,							_A		) and len(_A		) == 1:
																				return outputs[0]
													return outputs











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        UpperCAmelCase_  (       UpperCamelCase_   ,   UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			List[str]      =  CycleDiffusionPipeline
						UpperCamelCase__      :			int      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    '''negative_prompt''',
						    '''height''',
						    '''width''',
						    '''negative_prompt_embeds''',
						}
						UpperCamelCase__      :			Union[str, Any]      =  PipelineTesterMixin.required_optional_params - {'''latents'''}
						UpperCamelCase__      :			List[str]      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}	)
						UpperCamelCase__      :			Optional[int]      =  IMAGE_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase__      :			str      =  IMAGE_TO_IMAGE_IMAGE_PARAMS






						def 	_A     (   self		):







													'''simple docstring'''

													torch.manual_seed(0		)
													__SCREAMING_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				,							)
													__SCREAMING_SNAKE_CASE									= DDIMScheduler(
													    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule='scaled_linear'				,							num_train_timesteps=1_000				,							clip_sample=_A				,							set_alpha_to_one=_A				,							)
													torch.manual_seed(0		)
													__SCREAMING_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		)
													__SCREAMING_SNAKE_CASE									= CLIPTextConfig(
													    bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_000				,							)
													__SCREAMING_SNAKE_CASE									= CLIPTextModel(_A		)
													__SCREAMING_SNAKE_CASE									= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

													__SCREAMING_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				,							_A				,							_A=0		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= floats_tensor((1, 3, 32, 32)				,							rng=random.Random(_A		)		).to(_A		)
													__SCREAMING_SNAKE_CASE									= image / 2 + 0.5
													if str(_A		).startswith('mps'		):
																				__SCREAMING_SNAKE_CASE									= torch.manual_seed(_A		)
													else:
																				__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(_A		)
													__SCREAMING_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		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'cpu'  # ensure determinism for the device-dependent torch.Generator

													__SCREAMING_SNAKE_CASE									= self.get_dummy_components()
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline(**_A		)
													__SCREAMING_SNAKE_CASE									= pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= self.get_dummy_inputs(_A		)
													__SCREAMING_SNAKE_CASE									= pipe(**_A		)
													__SCREAMING_SNAKE_CASE									= output.images

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

													assert images.shape == (1, 32, 32, 3)
													__SCREAMING_SNAKE_CASE									= np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]		)

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






						@unittest.skipIf(torch_device != 'cuda'				,							'This test requires a GPU'		)
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_dummy_components()
													for name, module in components.items():
																				if hasattr(_A				,							'half'		):
																											__SCREAMING_SNAKE_CASE									= module.half()
													__SCREAMING_SNAKE_CASE									= CycleDiffusionPipeline(**_A		)
													__SCREAMING_SNAKE_CASE									= pipe.to(_A		)
													pipe.set_progress_bar_config(disable=_A		)

													__SCREAMING_SNAKE_CASE									= self.get_dummy_inputs(_A		)
													__SCREAMING_SNAKE_CASE									= pipe(**_A		)
													__SCREAMING_SNAKE_CASE									= output.images

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

													assert images.shape == (1, 32, 32, 3)
													__SCREAMING_SNAKE_CASE									= np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]		)

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






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_save_load_local()






						@unittest.skip('non-deterministic pipeline'		)
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_inference_batch_single_identical()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_save_load_optional_components()






						@skip_mps
						def 	_A     (   self		):







													'''simple docstring'''

													return super().test_attention_slicing_forward_pass()







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

						'''simple docstring'''




						def 	_A     (   self		):







													'''simple docstring'''

													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						def 	_A     (   self		):







													'''simple docstring'''

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

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

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

													__SCREAMING_SNAKE_CASE									= 'A black colored car'
													__SCREAMING_SNAKE_CASE									= 'A blue colored car'

													__SCREAMING_SNAKE_CASE									= torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe(
													    prompt=_A				,							source_prompt=_A				,							image=_A				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=_A				,							output_type='np'				,							)
													__SCREAMING_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		):







													'''simple docstring'''

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

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

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

													__SCREAMING_SNAKE_CASE									= 'A black colored car'
													__SCREAMING_SNAKE_CASE									= 'A blue colored car'

													__SCREAMING_SNAKE_CASE									= torch.manual_seed(0		)
													__SCREAMING_SNAKE_CASE									= pipe(
													    prompt=_A				,							source_prompt=_A				,							image=_A				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=_A				,							output_type='np'				,							)
													__SCREAMING_SNAKE_CASE									= output.images

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






