import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "instructblip_vision_model"







							def __init__(   self      :     str ,			_A      :     Dict=1408 ,			_A      :     Union[str, Any]=6144 ,			_A      :     Union[str, Any]=39 ,			_A      :     int=16 ,			_A      :     Dict=224 ,			_A      :     Dict=14 ,			_A      :     Any="gelu" ,			_A      :     Dict=1e-6 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=1e-10 ,			_A      :     List[str]=True ,			**_A      :     Dict ,			):
												super().__init__(**_A		)

												_UpperCamelCase			     =     hidden_size
												_UpperCamelCase			     =     intermediate_size
												_UpperCamelCase			     =     num_hidden_layers
												_UpperCamelCase			     =     num_attention_heads
												_UpperCamelCase			     =     patch_size
												_UpperCamelCase			     =     image_size
												_UpperCamelCase			     =     initializer_range
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     layer_norm_eps
												_UpperCamelCase			     =     hidden_act
												_UpperCamelCase			     =     qkv_bias







							@classmethod
							def  UpperCamelCase_						(   cls      :     Dict ,			_A      :     Union[str, os.PathLike] ,			**_A      :     Union[str, Any]		):
												cls._set_token_in_kwargs(_A		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     cls.get_config_dict(_A ,			**_A		)

												# get the vision config dict if we are loading from InstructBlipConfig
												if config_dict.get('''model_type'''		) == "instructblip":
																	_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_     (       __lowercase ):
							UpperCAmelCase				   =     "instructblip_qformer"







							def __init__(   self      :     List[str] ,			_A      :     int=3_0522 ,			_A      :     Tuple=768 ,			_A      :     List[str]=12 ,			_A      :     List[Any]=12 ,			_A      :     Any=3072 ,			_A      :     int="gelu" ,			_A      :     Union[str, Any]=0.1 ,			_A      :     Dict=0.1 ,			_A      :     str=512 ,			_A      :     Any=0.02 ,			_A      :     Optional[int]=1e-12 ,			_A      :     Tuple=0 ,			_A      :     Any="absolute" ,			_A      :     Dict=2 ,			_A      :     Dict=1408 ,			**_A      :     Optional[Any] ,			):
												super().__init__(pad_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			     =     initializer_range
												_UpperCamelCase			     =     layer_norm_eps
												_UpperCamelCase			     =     position_embedding_type
												_UpperCamelCase			     =     cross_attention_frequency
												_UpperCamelCase			     =     encoder_hidden_size







							@classmethod
							def  UpperCamelCase_						(   cls      :     List[str] ,			_A      :     Union[str, os.PathLike] ,			**_A      :     Tuple		):
												cls._set_token_in_kwargs(_A		)

												_UpperCamelCase     ,    _UpperCamelCase			     =     cls.get_config_dict(_A ,			**_A		)

												# get the qformer config dict if we are loading from InstructBlipConfig
												if config_dict.get('''model_type'''		) == "instructblip":
																	_UpperCamelCase			     =     config_dict['''qformer_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_     (       __lowercase ):
							UpperCAmelCase				   =     "instructblip"
							UpperCAmelCase				   =     True







							def __init__(   self      :     Tuple ,			_A      :     List[str]=None ,			_A      :     str=None ,			_A      :     Optional[int]=None ,			_A      :     Any=32 ,			**_A      :     Dict		):
												super().__init__(**_A		)

												if vision_config is None:
																	_UpperCamelCase			     =     {}
																	logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.'''		)

												if qformer_config is None:
																	_UpperCamelCase			     =     {}
																	logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'''		)

												if text_config is None:
																	_UpperCamelCase			     =     {}
																	logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).'''		)

												_UpperCamelCase			     =     InstructBlipVisionConfig(**_A		)
												_UpperCamelCase			     =     InstructBlipQFormerConfig(**_A		)
												_UpperCamelCase			     =     text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
												_UpperCamelCase			     =     CONFIG_MAPPING[text_model_type](**_A		)

												_UpperCamelCase			     =     self.text_config.tie_word_embeddings
												_UpperCamelCase			     =     self.text_config.is_encoder_decoder

												_UpperCamelCase			     =     num_query_tokens
												_UpperCamelCase			     =     self.vision_config.hidden_size
												_UpperCamelCase			     =     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
												_UpperCamelCase			     =     1.0
												_UpperCamelCase			     =     0.02







							@classmethod
							def  UpperCamelCase_						(   cls      :     str ,			_A      :     InstructBlipVisionConfig ,			_A      :     InstructBlipQFormerConfig ,			_A      :     PretrainedConfig ,			**_A      :     Tuple ,			):
												return cls(
												    vision_config=vision_config.to_dict() ,			qformer_config=qformer_config.to_dict() ,			text_config=text_config.to_dict() ,			**_A ,			)







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     copy.deepcopy(self.__dict__		)
												_UpperCamelCase			     =     self.vision_config.to_dict()
												_UpperCamelCase			     =     self.qformer_config.to_dict()
												_UpperCamelCase			     =     self.text_config.to_dict()
												_UpperCamelCase			     =     self.__class__.model_type
												return output

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     ShapEPipeline
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     [
							    "num_images_per_prompt",
							    "num_inference_steps",
							    "generator",
							    "latents",
							    "guidance_scale",
							    "frame_size",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase				   =     False







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return 32







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return 32







							@property
							def  UpperCamelCase_						(   self      :     List[str]		):
												return self.time_input_dim * 4







							@property
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												return 8







							@property
							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
												return tokenizer







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												torch.manual_seed(0		)
												_UpperCamelCase			     =     CLIPTextConfig(
												    bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=self.text_embedder_hidden_size ,			projection_dim=self.text_embedder_hidden_size ,			intermediate_size=37 ,			layer_norm_eps=1e-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=1000 ,			)
												return CLIPTextModelWithProjection(_A		)







							@property
							def  UpperCamelCase_						(   self      :     int		):
												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''',
												    '''encoder_hid_proj_type''': None,
												    '''added_emb_type''': None,
												}

												_UpperCamelCase			     =     PriorTransformer(**_A		)
												return model







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												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  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     self.dummy_prior
												_UpperCamelCase			     =     self.dummy_text_encoder
												_UpperCamelCase			     =     self.dummy_tokenizer
												_UpperCamelCase			     =     self.dummy_renderer

												_UpperCamelCase			     =     HeunDiscreteScheduler(
												    beta_schedule='''exp''' ,			num_train_timesteps=1024 ,			prediction_type='''sample''' ,			use_karras_sigmas=_A ,			clip_sample=_A ,			clip_sample_range=1.0 ,			)
												_UpperCamelCase			     =     {
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     Tuple ,			_A      :     Optional[int]=0		):
												if str(_A		).startswith('''mps'''		):
																	_UpperCamelCase			     =     torch.manual_seed(_A		)
												else:
																	_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(_A		)
												_UpperCamelCase			     =     {
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''frame_size''': 32,
												    '''output_type''': '''np''',
												}
												return inputs







							def  UpperCamelCase_						(   self      :     Any		):
												_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(
												    [
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												    ]		)

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







							def  UpperCamelCase_						(   self      :     Any		):
												# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
												self._test_inference_batch_consistent(batch_sizes=[1, 2]		)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     torch_device == '''cpu'''
												_UpperCamelCase			     =     True

												self._test_inference_batch_single_identical(
												    batch_size=2 ,			test_max_difference=_A ,			relax_max_difference=_A ,			)







							def  UpperCamelCase_						(   self      :     Any		):
												_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  UpperCamelCase_						(   self      :     str		):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/shap_e/test_shap_e_np_out.npy'''		)
												_UpperCamelCase			     =     ShapEPipeline.from_pretrained('''openai/shap-e'''		)
												_UpperCamelCase			     =     pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(0		)

												_UpperCamelCase			     =     pipe(
												    '''a shark''' ,			generator=_A ,			guidance_scale=15.0 ,			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		)
