



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''altclip_text_model'''



			def __init__(						self       :  str			,		A       :  Dict=25_00_02			,		A       :  Dict=10_24			,		A       :  str=24			,		A       :  Tuple=16			,		A       :  Optional[Any]=40_96			,		A       :  Dict="gelu"			,		A       :  Union[str, Any]=0.1			,		A       :  Union[str, Any]=0.1			,		A       :  str=5_14			,		A       :  Union[str, Any]=1			,		A       :  Tuple=0.0_2			,		A       :  int=0.0_2			,		A       :  Any=1E-05			,		A       :  Union[str, Any]=1			,		A       :  Any=0			,		A       :  List[Any]=2			,		A       :  Any="absolute"			,		A       :  Optional[Any]=True			,		A       :  Any=7_68			,		**A       :  str			,		)       ->   str:



						"""simple docstring"""






						super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A)

						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       max_position_embeddings
						_UpperCAmelCase			=       type_vocab_size
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       initializer_factor
						_UpperCAmelCase			=       layer_norm_eps
						_UpperCAmelCase			=       position_embedding_type
						_UpperCAmelCase			=       use_cache
						_UpperCAmelCase			=       project_dim







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''altclip_vision_model'''



			def __init__(						self       :  str			,		A       :  Any=7_68			,		A       :  List[Any]=30_72			,		A       :  Tuple=5_12			,		A       :  Any=12			,		A       :  Dict=12			,		A       :  int=3			,		A       :  int=2_24			,		A       :  Union[str, Any]=32			,		A       :  Optional[int]="quick_gelu"			,		A       :  List[str]=1E-5			,		A       :  List[Any]=0.0			,		A       :  List[str]=0.0_2			,		A       :  Any=1.0			,		**A       :  str			,		)       ->   List[Any]:



						"""simple docstring"""






						super().__init__(**A)

						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       projection_dim
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       num_channels
						_UpperCAmelCase			=       patch_size
						_UpperCAmelCase			=       image_size
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       initializer_factor
						_UpperCAmelCase			=       attention_dropout
						_UpperCAmelCase			=       layer_norm_eps
						_UpperCAmelCase			=       hidden_act



			@classmethod
			def    _lowerCamelCase	(						cls       :  str			,		A       :  Union[str, os.PathLike]			,		**A       :  Optional[Any])       ->   "PretrainedConfig":



						"""simple docstring"""






						cls._set_token_in_kwargs(A)

						_UpperCAmelCase	,					_UpperCAmelCase			=       cls.get_config_dict(A			,		**A)

						# get the vision config dict if we are loading from AltCLIPConfig
						if config_dict.get('model_type') == "altclip":
									_UpperCAmelCase			=       config_dict['vision_config']

						if "model_type" in config_dict and hasattr(cls			,		'model_type') and config_dict["model_type"] != cls.model_type:
									logger.warning(
									    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
									    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors.")

						return cls.from_dict(A			,		**A)







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''altclip'''
			UpperCamelCase        =					True



			def __init__(						self       :  Tuple			,		A       :  Dict=None			,		A       :  Tuple=None			,		A       :  Any=7_68			,		A       :  Tuple=2.6_5_9_2			,		**A       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       kwargs.pop('text_config_dict'			,		A)
						_UpperCAmelCase			=       kwargs.pop('vision_config_dict'			,		A)

						super().__init__(**A)

						# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
						# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
						# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
						if text_config_dict is not None:
									if text_config is None:
												_UpperCAmelCase			=       {}

									# This is the complete result when using `text_config_dict`.
									_UpperCAmelCase			=       AltCLIPTextConfig(**A).to_dict()

									# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
									for key, value in _text_config_dict.items():
												if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
															# If specified in `text_config_dict`
															if key in text_config_dict:
																		_UpperCAmelCase			=       (
																		    F"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
																		    F"The value `text_config_dict[\"{key}\"]` will be used instead."
																		)
															# If inferred from default argument values (just to be super careful)
															else:
																		_UpperCAmelCase			=       (
																		    F"`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The "
																		    F"value `text_config[\"{key}\"]` will be overriden."
																		)
															logger.warning(A)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
									text_config.update(_text_config_dict)

						if vision_config_dict is not None:
									if vision_config is None:
												_UpperCAmelCase			=       {}

									# This is the complete result when using `vision_config_dict`.
									_UpperCAmelCase			=       AltCLIPVisionConfig(**A).to_dict()
									# convert keys to string instead of integer
									if "id2label" in _vision_config_dict:
												_UpperCAmelCase			=       {
												    str(A): value for key, value in _vision_config_dict['id2label'].items()
												}

									# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
									for key, value in _vision_config_dict.items():
												if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
															# If specified in `vision_config_dict`
															if key in vision_config_dict:
																		_UpperCAmelCase			=       (
																		    F"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
																		    F"values. The value `vision_config_dict[\"{key}\"]` will be used instead."
																		)
															# If inferred from default argument values (just to be super careful)
															else:
																		_UpperCAmelCase			=       (
																		    F"`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. "
																		    F"The value `vision_config[\"{key}\"]` will be overriden."
																		)
															logger.warning(A)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
									vision_config.update(_vision_config_dict)

						if text_config is None:
									_UpperCAmelCase			=       {}
									logger.info('`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.')

						if vision_config is None:
									_UpperCAmelCase			=       {}
									logger.info('`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.')

						_UpperCAmelCase			=       AltCLIPTextConfig(**A)
						_UpperCAmelCase			=       AltCLIPVisionConfig(**A)

						_UpperCAmelCase			=       projection_dim
						_UpperCAmelCase			=       logit_scale_init_value
						_UpperCAmelCase			=       1.0



			@classmethod
			def    _lowerCamelCase	(						cls       :  Union[str, Any]			,		A       :  AltCLIPTextConfig			,		A       :  AltCLIPVisionConfig			,		**A       :  str)       ->   List[Any]:



						"""simple docstring"""






						return cls(text_config=text_config.to_dict()			,		vision_config=vision_config.to_dict()			,		**A)



			def    _lowerCamelCase	(						self       :  List[str])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       copy.deepcopy(self.__dict__)
						_UpperCAmelCase			=       self.text_config.to_dict()
						_UpperCAmelCase			=       self.vision_config.to_dict()
						_UpperCAmelCase			=       self.__class__.model_type
						return output





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   (      A    ,						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       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   int:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  str)       ->   List[str]:



						"""simple docstring"""






						return self.time_input_dim * 4



			@property
			def    _lowerCamelCase	(						self       :  List[str])       ->   List[Any]:



						"""simple docstring"""






						return 8



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase			=       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			,		)

						_UpperCAmelCase			=       CLIPVisionModel(A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       CLIPImageProcessor(
						    crop_size=2_24			,		do_center_crop=A			,		do_normalize=A			,		do_resize=A			,		image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]			,		image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]			,		resample=3			,		size=2_24			,		)

						return image_processor



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    '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,
						}

						_UpperCAmelCase			=       PriorTransformer(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   List[str]:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    '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,
						    ),
						}
						_UpperCAmelCase			=       ShapERenderer(**A)
						return model



			def    _lowerCamelCase	(						self       :  Any)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       self.dummy_prior
						_UpperCAmelCase			=       self.dummy_image_encoder
						_UpperCAmelCase			=       self.dummy_image_processor
						_UpperCAmelCase			=       self.dummy_renderer

						_UpperCAmelCase			=       HeunDiscreteScheduler(
						    beta_schedule='exp'			,		num_train_timesteps=10_24			,		prediction_type='sample'			,		use_karras_sigmas=A			,		clip_sample=A			,		clip_sample_range=1.0			,		)
						_UpperCAmelCase			=       {
						    'prior': prior,
						    'image_encoder': image_encoder,
						    'image_processor': image_processor,
						    'renderer': renderer,
						    'scheduler': scheduler,
						}

						return components



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  Optional[Any]			,		A       :  Tuple=0)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor((1, 3, 64, 64)			,		rng=random.Random(A)).to(A)

						if str(A).startswith('mps'):
									_UpperCAmelCase			=       torch.manual_seed(A)
						else:
									_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(A)
						_UpperCAmelCase			=       {
						    'image': input_image,
						    'generator': generator,
						    'num_inference_steps': 1,
						    'frame_size': 32,
						    'output_type': 'np',
						}
						return inputs



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

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

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

						_UpperCAmelCase			=       np.array(
						    [
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						        0.0_0_0_3_9_2_1_6,
						    ])

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



			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						self._test_inference_batch_consistent(batch_sizes=[1, 2])



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       torch_device == 'cpu'
						_UpperCAmelCase			=       True
						self._test_inference_batch_single_identical(
						    batch_size=2			,		test_max_difference=A			,		relax_max_difference=A			,		)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_dummy_components()
						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)
						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       1
						_UpperCAmelCase			=       2

						_UpperCAmelCase			=       self.get_dummy_inputs(A)

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

						_UpperCAmelCase			=       pipe(**A			,		num_images_per_prompt=A)[0]

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







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



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   str:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Dict)       ->   List[str]:



						"""simple docstring"""






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

						_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(0)

						_UpperCAmelCase			=       pipe(
						    A			,		generator=A			,		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(A			,		A)
