
import copy
import os
from typing import Union

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


_lowercase											=    logging.get_logger(__name__)

_lowercase											=    {
    """BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json""",
    """BridgeTower/bridgetower-base-itm-mlm""": (
        """https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"""
    ),
}


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						"bridgetower_vision_model"


       def __init__(    self						,       __A=768						,       __A=12						,       __A=3						,       __A=16						,       __A=288						,       __A=1						,       __A=1e-0_5						,       __A=False						,       __A=True						,       __A=False						,       **__A						,       )						->    Optional[int]:
              super().__init__(**__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=hidden_size
              SCREAMING_SNAKE_CASE_							:				int		=num_hidden_layers
              SCREAMING_SNAKE_CASE_							:				int		=num_channels
              SCREAMING_SNAKE_CASE_							:				Dict		=patch_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=image_size
              SCREAMING_SNAKE_CASE_							:				Any		=initializer_factor
              SCREAMING_SNAKE_CASE_							:				int		=layer_norm_eps
              SCREAMING_SNAKE_CASE_							:				Dict		=stop_gradient
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=share_layernorm
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=remove_last_layer





       @classmethod
       def 	_snake_case				(    cls						,       __A						,       **__A				)						->    "PretrainedConfig":
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=cls.get_config_dict(__A						,       **__A				)

              if config_dict.get('''model_type'''				) == "bridgetower":
                     SCREAMING_SNAKE_CASE_							:				List[str]		=config_dict['''text_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 			lowercase_		(      A       ):
       __lowerCamelCase				       =						"bridgetower_text_model"


       def __init__(    self						,       __A=50_265						,       __A=768						,       __A=12						,       __A=12						,       __A=1						,       __A=3_072						,       __A="gelu"						,       __A=0.1						,       __A=0.1						,       __A=514						,       __A=1						,       __A=1e-0_5						,       __A=1						,       __A=0						,       __A=2						,       __A="absolute"						,       __A=True						,       **__A						,       )						->    str:
              super().__init__(**__A				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=vocab_size
              SCREAMING_SNAKE_CASE_							:				int		=hidden_size
              SCREAMING_SNAKE_CASE_							:				Any		=num_hidden_layers
              SCREAMING_SNAKE_CASE_							:				Any		=num_attention_heads
              SCREAMING_SNAKE_CASE_							:				List[Any]		=hidden_act
              SCREAMING_SNAKE_CASE_							:				Any		=initializer_factor
              SCREAMING_SNAKE_CASE_							:				Any		=intermediate_size
              SCREAMING_SNAKE_CASE_							:				List[Any]		=hidden_dropout_prob
              SCREAMING_SNAKE_CASE_							:				List[str]		=attention_probs_dropout_prob
              SCREAMING_SNAKE_CASE_							:				str		=max_position_embeddings
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=type_vocab_size
              SCREAMING_SNAKE_CASE_							:				Dict		=layer_norm_eps
              SCREAMING_SNAKE_CASE_							:				str		=position_embedding_type
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=use_cache
              SCREAMING_SNAKE_CASE_							:				int		=pad_token_id
              SCREAMING_SNAKE_CASE_							:				Tuple		=bos_token_id
              SCREAMING_SNAKE_CASE_							:				List[str]		=eos_token_id





       @classmethod
       def 	_snake_case				(    cls						,       __A						,       **__A				)						->    "PretrainedConfig":
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				str		=cls.get_config_dict(__A						,       **__A				)

              if config_dict.get('''model_type'''				) == "bridgetower":
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=config_dict['''text_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 			lowercase_		(      A       ):
       __lowerCamelCase				       =						"bridgetower"


       def __init__(    self						,       __A=True						,       __A="gelu"						,       __A=768						,       __A=1						,       __A=1e-0_5						,       __A=False						,       __A="add"						,       __A=12						,       __A=6						,       __A=False						,       __A=False						,       __A=None						,       __A=None						,       **__A						,       )						->    Tuple:
              # TODO: remove this once the Hub files are updated.
              SCREAMING_SNAKE_CASE_							:				Any		=kwargs.pop('''text_config_dict'''						,       __A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=kwargs.pop('''vision_config_dict'''						,       __A				)

              super().__init__(**__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=share_cross_modal_transformer_layers
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=hidden_act
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=hidden_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=initializer_factor
              SCREAMING_SNAKE_CASE_							:				Any		=layer_norm_eps
              SCREAMING_SNAKE_CASE_							:				Any		=share_link_tower_layers
              SCREAMING_SNAKE_CASE_							:				List[Any]		=link_tower_type
              SCREAMING_SNAKE_CASE_							:				str		=num_attention_heads
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=num_hidden_layers
              SCREAMING_SNAKE_CASE_							:				Dict		=tie_word_embeddings
              SCREAMING_SNAKE_CASE_							:				Any		=init_layernorm_from_vision_encoder

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

              if vision_config is None:
                     SCREAMING_SNAKE_CASE_							:				List[str]		={}
                     logger.info('''`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.'''				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=BridgeTowerTextConfig(**__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=BridgeTowerVisionConfig(**__A				)


       @classmethod
       def 	_snake_case				(    cls						,       __A						,       __A						,       **__A				)						->    Union[str, Any]:
              return cls(text_config=text_config.to_dict()						,       vision_config=vision_config.to_dict()						,       **__A				)





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


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						42
       __lowerCamelCase				       =						42


       def __init__(    self						,       __A						,       __A				)						->    List[str]:
              super().__init__()
              self.register_modules(unet=__A						,       scheduler=__A				)





       @torch.no_grad()
       def __call__(    self						,       __A = 1						,       __A = 50						,       __A = None						,       __A = "pil"						,       __A = True						,       **__A						,       )						->    Union[Tuple, ImagePipelineOutput]:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.unet.config.sample_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=(batch_size, 3, img_size, img_size)

              SCREAMING_SNAKE_CASE_							:				int		=self.unet

              # sample x_0 ~ N(0, sigma_0^2 * I)
              SCREAMING_SNAKE_CASE_							:				Tuple		=randn_tensor(__A						,       generator=__A						,       device=self.device				) * self.scheduler.init_noise_sigma

              self.scheduler.set_timesteps(__A				)

              for t in self.progress_bar(self.scheduler.timesteps				):
                     # here sigma_t == t_i from the paper
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler.schedule[t]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.schedule[t - 1] if t > 0 else 0

                     # 1. Select temporarily increased noise level sigma_hat
                     # 2. Add new noise to move from sample_i to sample_hat
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.add_noise_to_input(__A						,       __A						,       generator=__A				)

                     # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                     # The model inputs and output are adjusted by following eq. (213) in [1].
                     SCREAMING_SNAKE_CASE_							:				Tuple		=(sigma_hat / 2) * model((sample_hat + 1) / 2						,       sigma_hat / 2				).sample

                     # 4. Evaluate dx/dt at sigma_hat
                     # 5. Take Euler step from sigma to sigma_prev
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.step(__A						,       __A						,       __A						,       __A				)

                     if sigma_prev != 0:
                            # 6. Apply 2nd order correction
                            # The model inputs and output are adjusted by following eq. (213) in [1].
                            SCREAMING_SNAKE_CASE_							:				int		=(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2						,       sigma_prev / 2				).sample
                            SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.step_correct(
                                __A						,       __A						,       __A						,       __A						,       step_output.prev_sample						,       step_output['''derivative''']						,       )
                     SCREAMING_SNAKE_CASE_							:				Any		=step_output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=(sample / 2 + 0.5).clamp(0						,       1				)
              SCREAMING_SNAKE_CASE_							:				Any		=sample.cpu().permute(0						,       2						,       3						,       1				).numpy()
              if output_type == "pil":
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.numpy_to_pil(__A				)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=__A				)
