




import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


__UpperCamelCase					:       Tuple    					=      logging.get_logger(__name__)


__UpperCamelCase					:       Tuple    					=      R"\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"

class 		__lowerCAmelCase	(	__magic_name__ ):
							@add_start_docstrings(__magic_name__				)
							def __call__(						self						:Union[str, Any]		,      __magic_name__						:torch.LongTensor		,      __magic_name__						:torch.FloatTensor		,      **__magic_name__						:Optional[int]				):







								'''simple docstring'''







								raise NotImplementedError("""StoppingCriteria needs to be subclassed"""				)







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Optional[Any]		,      __magic_name__						:int		,      __magic_name__						:Optional[int] = None				):







								'''simple docstring'''







								a										=							max_length
								a										=							max_position_embeddings





							@add_start_docstrings(__magic_name__				)
							def __call__(						self						:Optional[Any]		,      __magic_name__						:torch.LongTensor		,      __magic_name__						:torch.FloatTensor		,      **__magic_name__						:Optional[int]				):







								'''simple docstring'''







								a										=							input_ids.shape[-1]
								a										=							cur_len >= self.max_length
								if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
									logger.warning_once(
									    """This is a friendly reminder - the current text generation call will exceed the model's predefined """
									    F'maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '
									    """exceptions, performance degradation, or nothing at all."""				)
								return is_done







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:str		,      __magic_name__						:int		,      __magic_name__						:int				):







								'''simple docstring'''







								warnings.warn(
								    """The class `MaxNewTokensCriteria` is deprecated. """
								    F'Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '
								    """with `max_length = start_length + max_new_tokens` instead."""		,      __magic_name__		,      )
								a										=							start_length
								a										=							max_new_tokens
								a										=							start_length + max_new_tokens





							@add_start_docstrings(__magic_name__				)
							def __call__(						self						:Any		,      __magic_name__						:torch.LongTensor		,      __magic_name__						:torch.FloatTensor		,      **__magic_name__						:Any				):







								'''simple docstring'''







								return input_ids.shape[-1] >= self.max_length







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Tuple		,      __magic_name__						:float		,      __magic_name__						:Optional[float] = None				):







								'''simple docstring'''







								a										=							max_time
								a										=							time.time() if initial_timestamp is None else initial_timestamp





							@add_start_docstrings(__magic_name__				)
							def __call__(						self						:str		,      __magic_name__						:torch.LongTensor		,      __magic_name__						:torch.FloatTensor		,      **__magic_name__						:int				):







								'''simple docstring'''







								return time.time() - self.initial_timestamp > self.max_time







class 		__lowerCAmelCase	(	__magic_name__ ):
							@add_start_docstrings(__magic_name__				)
							def __call__(						self						:str		,      __magic_name__						:torch.LongTensor		,      __magic_name__						:torch.FloatTensor		,      **__magic_name__						:str				):







								'''simple docstring'''







								return any(criteria(__magic_name__		,      __magic_name__				) for criteria in self				)





							@property
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								for stopping_criterium in self:
									if isinstance(__magic_name__		,      __magic_name__				):
										return stopping_criterium.max_length
									elif isinstance(__magic_name__		,      __magic_name__				):
										return stopping_criterium.max_length
								return None







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       StoppingCriteriaList:
	a										=							stopping_criteria.max_length
	a										=							deepcopy(__lowerCamelCase    )
	if stopping_max_length is not None and stopping_max_length != max_length:
		warnings.warn("""You set different `max_length` for stopping criteria and `max_length` parameter"""							,			__lowerCamelCase    )
	elif stopping_max_length is None:
		new_stopping_criteria.append(MaxLengthCriteria(max_length=__lowerCamelCase    )    )
	return new_stopping_criteria







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  ShapEImgaImgPipeline
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  [
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCamelCase__					     =  False
							@property
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								return self.time_input_dim * 4
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								return 8
							@property
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								torch.manual_seed(0				)
								a										=							CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size		,      image_size=64		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=37		,      num_attention_heads=4		,      num_channels=3		,      num_hidden_layers=5		,      patch_size=1		,      )

								a										=							CLIPVisionModel(__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							CLIPImageProcessor(
								    crop_size=224		,      do_center_crop=__magic_name__		,      do_normalize=__magic_name__		,      do_resize=__magic_name__		,      image_mean=[0.48145466, 0.4578275, 0.40821073]		,      image_std=[0.26862954, 0.26130258, 0.27577711]		,      resample=3		,      size=224		,      )

								return image_processor
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """num_attention_heads""": 2,
								    """attention_head_dim""": 16,
								    """embedding_dim""": self.time_input_dim,
								    """num_embeddings""": 32,
								    """embedding_proj_dim""": self.text_embedder_hidden_size,
								    """time_embed_dim""": self.time_embed_dim,
								    """num_layers""": 1,
								    """clip_embed_dim""": self.time_input_dim * 2,
								    """additional_embeddings""": 0,
								    """time_embed_act_fn""": """gelu""",
								    """norm_in_type""": """layer""",
								    """embedding_proj_norm_type""": """layer""",
								    """encoder_hid_proj_type""": None,
								    """added_emb_type""": None,
								}

								a										=							PriorTransformer(**__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """param_shapes""": (
								        (self.renderer_dim, 93),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    """d_latent""": self.time_input_dim,
								    """d_hidden""": self.renderer_dim,
								    """n_output""": 12,
								    """background""": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								a										=							ShapERenderer(**__magic_name__				)
								return model
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								a										=							self.dummy_prior
								a										=							self.dummy_image_encoder
								a										=							self.dummy_image_processor
								a										=							self.dummy_renderer

								a										=							HeunDiscreteScheduler(
								    beta_schedule="""exp"""		,      num_train_timesteps=1024		,      prediction_type="""sample"""		,      use_karras_sigmas=__magic_name__		,      clip_sample=__magic_name__		,      clip_sample_range=1.0		,      )
								a										=							{
								    """prior""": prior,
								    """image_encoder""": image_encoder,
								    """image_processor""": image_processor,
								    """renderer""": renderer,
								    """scheduler""": scheduler,
								}

								return components
							def       lowerCamelCase__							(						self						:List[Any]		,      __magic_name__						:str		,      __magic_name__						:Tuple=0				):







								'''simple docstring'''







								a										=							floats_tensor((1, 3, 64, 64)		,      rng=random.Random(__magic_name__				)				).to(__magic_name__				)

								if str(__magic_name__				).startswith("""mps"""				):
									a										=							torch.manual_seed(__magic_name__				)
								else:
									a										=							torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
								a										=							{
								    """image""": input_image,
								    """generator""": generator,
								    """num_inference_steps""": 1,
								    """frame_size""": 32,
								    """output_type""": """np""",
								}
								return inputs
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							"""cpu"""

								a										=							self.get_dummy_components()

								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)

								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							pipe(**self.get_dummy_inputs(__magic_name__				)				)
								a										=							output.images[0]
								a										=							image[0, -3:, -3:, -1]

								assert image.shape == (20, 32, 32, 3)

								a										=							np.array(
								    [
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								    ]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							torch_device == """cpu"""
								a										=							True
								self._test_inference_batch_single_identical(
								    batch_size=2		,      test_max_difference=__magic_name__		,      relax_max_difference=__magic_name__		,      )





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							self.get_dummy_components()
								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							1
								a										=							2

								a										=							self.get_dummy_inputs(__magic_name__				)

								for key in inputs.keys():
									if key in self.batch_params:
										a										=							batch_size * [inputs[key]]

								a										=							pipe(**__magic_name__		,      num_images_per_prompt=__magic_name__				)[0]

								assert images.shape[0] == batch_size * num_images_per_prompt







@slow
@require_torch_gpu
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""				)
								a										=							load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/shap_e/test_shap_e_img2img_out.npy"""				)
								a										=							ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							torch.Generator(device=__magic_name__				).manual_seed(0				)

								a										=							pipe(
								    __magic_name__		,      generator=__magic_name__		,      guidance_scale=3.0		,      num_inference_steps=64		,      frame_size=64		,      output_type="""np"""		,      ).images[0]

								assert images.shape == (20, 64, 64, 3)

								assert_mean_pixel_difference(__magic_name__		,      __magic_name__				)

