


'''simple docstring'''

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


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''instructblip_vision_model'''






     def __init__(			self     ,							__UpperCamelCase=1_408     ,							__UpperCamelCase=6_144     ,							__UpperCamelCase=39     ,							__UpperCamelCase=16     ,							__UpperCamelCase=224     ,							__UpperCamelCase=14     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=1e-6     ,							__UpperCamelCase=0.0     ,							__UpperCamelCase=1e-10     ,							__UpperCamelCase=True     ,							**__UpperCamelCase     ,							)       ->    Optional[Any]:
          super().__init__(**__UpperCamelCase		)

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






     @classmethod
     def  a_					(			cls     ,							__UpperCamelCase     ,							**__UpperCamelCase		)       ->    "PretrainedConfig":
          cls._set_token_in_kwargs(__UpperCamelCase		)

          _a			,			_a						=		cls.get_config_dict(__UpperCamelCase     ,							**__UpperCamelCase		)

          # get the vision config dict if we are loading from InstructBlipConfig
          if config_dict.get("model_type"		) == "instructblip":
               _a						=		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(__UpperCamelCase     ,							**__UpperCamelCase		)







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''instructblip_qformer'''






     def __init__(			self     ,							__UpperCamelCase=30_522     ,							__UpperCamelCase=768     ,							__UpperCamelCase=12     ,							__UpperCamelCase=12     ,							__UpperCamelCase=3_072     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=512     ,							__UpperCamelCase=0.02     ,							__UpperCamelCase=1e-12     ,							__UpperCamelCase=0     ,							__UpperCamelCase="absolute"     ,							__UpperCamelCase=2     ,							__UpperCamelCase=1_408     ,							**__UpperCamelCase     ,							)       ->    int:
          super().__init__(pad_token_id=__UpperCamelCase     ,							**__UpperCamelCase		)

          _a						=		vocab_size
          _a						=		hidden_size
          _a						=		num_hidden_layers
          _a						=		num_attention_heads
          _a						=		hidden_act
          _a						=		intermediate_size
          _a						=		hidden_dropout_prob
          _a						=		attention_probs_dropout_prob
          _a						=		max_position_embeddings
          _a						=		initializer_range
          _a						=		layer_norm_eps
          _a						=		position_embedding_type
          _a						=		cross_attention_frequency
          _a						=		encoder_hidden_size






     @classmethod
     def  a_					(			cls     ,							__UpperCamelCase     ,							**__UpperCamelCase		)       ->    "PretrainedConfig":
          cls._set_token_in_kwargs(__UpperCamelCase		)

          _a			,			_a						=		cls.get_config_dict(__UpperCamelCase     ,							**__UpperCamelCase		)

          # get the qformer config dict if we are loading from InstructBlipConfig
          if config_dict.get("model_type"		) == "instructblip":
               _a						=		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(__UpperCamelCase     ,							**__UpperCamelCase		)







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''instructblip'''
     UpperCAmelCase 					=				True






     def __init__(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase=32     ,							**__UpperCamelCase		)       ->    int:
          super().__init__(**__UpperCamelCase		)

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

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

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

          _a						=		InstructBlipVisionConfig(**__UpperCamelCase		)
          _a						=		InstructBlipQFormerConfig(**__UpperCamelCase		)
          _a						=		text_config["model_type"] if "model_type" in text_config else "opt"
          _a						=		CONFIG_MAPPING[text_model_type](**__UpperCamelCase		)

          _a						=		self.text_config.tie_word_embeddings
          _a						=		self.text_config.is_encoder_decoder

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






     @classmethod
     def  a_					(			cls     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							**__UpperCamelCase     ,							)       ->    int:
          return cls(
              vision_config=vision_config.to_dict()     ,							qformer_config=qformer_config.to_dict()     ,							text_config=text_config.to_dict()     ,							**__UpperCamelCase     ,							)






     def  a_					(			self		)       ->    Optional[Any]:
          _a						=		copy.deepcopy(self.__dict__		)
          _a						=		self.vision_config.to_dict()
          _a						=		self.qformer_config.to_dict()
          _a						=		self.text_config.to_dict()
          _a						=		self.__class__.model_type
          return output





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase__				     =		False





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):
     pass







@nightly
@require_torch_gpu
class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






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






     def  a_					(			self		)       ->    List[str]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(__UpperCamelCase		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		generator.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"






     def  a_					(			self		)       ->    List[Any]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
              "shi-labs/versatile-diffusion"     ,							torch_dtype=torch.floataa		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=50     ,							output_type="numpy"		).images

          _a						=		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a						=		np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]		)

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

