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


A     :  List[str]					     =       logging.get_logger(__name__)

A     :  str					     =       {
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''blip_2_vision_model'''







  def __init__(							self :       Optional[Any]  ,     __magic_name__ :       List[str]=1_408  ,     __magic_name__ :       int=6_144  ,     __magic_name__ :       Any=39  ,     __magic_name__ :       List[Any]=16  ,     __magic_name__ :       Optional[int]=224  ,     __magic_name__ :       int=14  ,     __magic_name__ :       Any="gelu"  ,     __magic_name__ :       List[str]=0.0_0001  ,     __magic_name__ :       Optional[Any]=0.0  ,     __magic_name__ :       List[str]=1e-10  ,     __magic_name__ :       List[Any]=True  ,     **__magic_name__ :       Union[str, Any]  ,     )  ->	Optional[Any]:
        super().__init__(**__lowerCAmelCase   )

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







  @classmethod
  def    __A     (							cls :       List[Any]  ,     __magic_name__ :       Optional[Any]  ,     **__magic_name__ :       Tuple   )  ->	int:
        cls._set_token_in_kwargs(__lowerCAmelCase   )

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  cls.get_config_dict(__lowerCAmelCase  ,     **__lowerCAmelCase   )

        # get the vision config dict if we are loading from Blip2Config
        if config_dict.get("model_type"   ) == "blip-2":
              SCREAMING_SNAKE_CASE_          =  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(__lowerCAmelCase  ,     **__lowerCAmelCase   )







class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''blip_2_qformer'''







  def __init__(							self :       List[str]  ,     __magic_name__ :       str=30_522  ,     __magic_name__ :       int=768  ,     __magic_name__ :       Dict=12  ,     __magic_name__ :       Optional[Any]=12  ,     __magic_name__ :       Union[str, Any]=3_072  ,     __magic_name__ :       int="gelu"  ,     __magic_name__ :       Optional[Any]=0.1  ,     __magic_name__ :       Union[str, Any]=0.1  ,     __magic_name__ :       Dict=512  ,     __magic_name__ :       Dict=0.02  ,     __magic_name__ :       str=1e-12  ,     __magic_name__ :       Dict=0  ,     __magic_name__ :       List[str]="absolute"  ,     __magic_name__ :       int=2  ,     __magic_name__ :       Any=1_408  ,     **__magic_name__ :       Any  ,     )  ->	List[str]:
        super().__init__(pad_token_id=__lowerCAmelCase  ,     **__lowerCAmelCase   )

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







  @classmethod
  def    __A     (							cls :       Optional[Any]  ,     __magic_name__ :       Any  ,     **__magic_name__ :       Tuple   )  ->	int:
        cls._set_token_in_kwargs(__lowerCAmelCase   )

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  cls.get_config_dict(__lowerCAmelCase  ,     **__lowerCAmelCase   )

        # get the qformer config dict if we are loading from Blip2Config
        if config_dict.get("model_type"   ) == "blip-2":
              SCREAMING_SNAKE_CASE_          =  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(__lowerCAmelCase  ,     **__lowerCAmelCase   )







class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''blip-2'''
  lowerCamelCase__     =						True







  def __init__(							self :       str  ,     __magic_name__ :       Dict=None  ,     __magic_name__ :       int=None  ,     __magic_name__ :       Dict=None  ,     __magic_name__ :       Dict=32  ,     **__magic_name__ :       Dict   )  ->	Dict:
        super().__init__(**__lowerCAmelCase   )

        if vision_config is None:
              SCREAMING_SNAKE_CASE_          =  {}
              logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."   )

        if qformer_config is None:
              SCREAMING_SNAKE_CASE_          =  {}
              logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."   )

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

        SCREAMING_SNAKE_CASE_          =  BlipaVisionConfig(**__lowerCAmelCase   )
        SCREAMING_SNAKE_CASE_          =  BlipaQFormerConfig(**__lowerCAmelCase   )
        SCREAMING_SNAKE_CASE_          =  text_config["model_type"] if "model_type" in text_config else "opt"
        SCREAMING_SNAKE_CASE_          =  CONFIG_MAPPING[text_model_type](**__lowerCAmelCase   )

        SCREAMING_SNAKE_CASE_          =  self.text_config.tie_word_embeddings
        SCREAMING_SNAKE_CASE_          =  self.text_config.is_encoder_decoder

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







  @classmethod
  def    __A     (							cls :       List[Any]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       int  ,     __magic_name__ :       Any  ,     **__magic_name__ :       Any  ,     )  ->	int:
        return cls(
            vision_config=vision_config.to_dict()  ,     qformer_config=qformer_config.to_dict()  ,     text_config=text_config.to_dict()  ,     **__lowerCAmelCase  ,     )







  def    __A     (							self :       str   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  copy.deepcopy(self.__dict__   )
        SCREAMING_SNAKE_CASE_          =  self.vision_config.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.qformer_config.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.text_config.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.__class__.model_type
        return output



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


A     :  List[str]					     =       False


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  pass



@nightly
@require_torch_gpu
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Union[str, Any]   )  ->	List[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def    __A     (							self :       str   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"   )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

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

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

        SCREAMING_SNAKE_CASE_          =  generator.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     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 :       int   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained(
            "shi-labs/versatile-diffusion"  ,     torch_dtype=torch.floataa   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

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

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

        assert image.shape == (1, 512, 512, 3)
        SCREAMING_SNAKE_CASE_          =  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]   )

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


