


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


_SCREAMING_SNAKE_CASE      :			Union[str, Any]      				=   logging.get_logger(__name__)  # pylint: disable=invalid-name







class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , ):
         super().__init__()

         if safety_checker is None:
              logger.warning(
                  F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                  ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
                  ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
                  ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
                  ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
                  ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''   )

         self.register_modules(
             speech_model=__snake_case      , speech_processor=__snake_case      , vae=__snake_case      , text_encoder=__snake_case      , tokenizer=__snake_case      , unet=__snake_case      , scheduler=__snake_case      , feature_extractor=__snake_case      , )
    def 		a_      (	self      , __snake_case = "auto"   ):
         if slice_size == "auto":
              snake_case					=    self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(__snake_case   )
    def 		a_      (	self   ):
         self.enable_attention_slicing(__snake_case   )




    @torch.no_grad()
    def __call__(	self      , __snake_case      , __snake_case=1_6_0_0_0      , __snake_case = 5_1_2      , __snake_case = 5_1_2      , __snake_case = 5_0      , __snake_case = 7.5      , __snake_case = None      , __snake_case = 1      , __snake_case = 0.0      , __snake_case = None      , __snake_case = None      , __snake_case = "pil"      , __snake_case = True      , __snake_case = None      , __snake_case = 1      , **__snake_case      , ):
         snake_case					=    self.speech_processor.feature_extractor(
             __snake_case      , return_tensors='''pt'''      , sampling_rate=__snake_case   ).input_features.to(self.device   )
         snake_case					=    self.speech_model.generate(__snake_case      , max_length=4_8_0_0_0_0   )

         snake_case					=    self.speech_processor.tokenizer.batch_decode(__snake_case      , skip_special_tokens=__snake_case      , normalize=__snake_case   )[
             0
         ]

         if isinstance(__snake_case      , __snake_case   ):
              snake_case					=    1
         elif isinstance(__snake_case      , __snake_case   ):
              snake_case					=    len(__snake_case   )
         else:
              raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__snake_case   )}'''   )

         if height % 8 != 0 or width % 8 != 0:
              raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''   )

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(__snake_case      , __snake_case   ) or callback_steps <= 0)
         ):
              raise ValueError(
                  F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                  F''' {type(__snake_case   )}.'''   )

         # get prompt text embeddings
         snake_case					=    self.tokenizer(
             __snake_case      , padding='''max_length'''      , max_length=self.tokenizer.model_max_length      , return_tensors='''pt'''      , )
         snake_case					=    text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
              snake_case					=    self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
              logger.warning(
                  '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                  F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''   )
              snake_case					=    text_input_ids[:, : self.tokenizer.model_max_length]
         snake_case					=    self.text_encoder(text_input_ids.to(self.device   )   )[0]

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         snake_case			,  snake_case			,  snake_case					=    text_embeddings.shape
         snake_case					=    text_embeddings.repeat(1      , __snake_case      , 1   )
         snake_case					=    text_embeddings.view(bs_embed * num_images_per_prompt      , __snake_case      , -1   )

         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         snake_case					=    guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
              snake_case					=    4_2
              if negative_prompt is None:
                   snake_case					=    [''''''] * batch_size
              elif type(__snake_case   ) is not type(__snake_case   ):
                   raise TypeError(
                       F'''`negative_prompt` should be the same type to `prompt`, but got {type(__snake_case   )} !='''
                       F''' {type(__snake_case   )}.'''   )
              elif isinstance(__snake_case      , __snake_case   ):
                   snake_case					=    [negative_prompt]
              elif batch_size != len(__snake_case   ):
                   raise ValueError(
                       F'''`negative_prompt`: {negative_prompt} has batch size {len(__snake_case   )}, but `prompt`:'''
                       F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                       ''' the batch size of `prompt`.'''   )
              else:
                   snake_case					=    negative_prompt

              snake_case					=    text_input_ids.shape[-1]
              snake_case					=    self.tokenizer(
                  __snake_case      , padding='''max_length'''      , max_length=__snake_case      , truncation=__snake_case      , return_tensors='''pt'''      , )
              snake_case					=    self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
              snake_case					=    uncond_embeddings.shape[1]
              snake_case					=    uncond_embeddings.repeat(1      , __snake_case      , 1   )
              snake_case					=    uncond_embeddings.view(batch_size * num_images_per_prompt      , __snake_case      , -1   )

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              snake_case					=    torch.cat([uncond_embeddings, text_embeddings]   )

         # get the initial random noise unless the user supplied it

         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
         snake_case					=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         snake_case					=    text_embeddings.dtype
         if latents is None:
              if self.device.type == "mps":
                   # randn does not exist on mps
                   snake_case					=    torch.randn(__snake_case      , generator=__snake_case      , device='''cpu'''      , dtype=__snake_case   ).to(
                       self.device   )
              else:
                   snake_case					=    torch.randn(__snake_case      , generator=__snake_case      , device=self.device      , dtype=__snake_case   )
         else:
              if latents.shape != latents_shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''   )
              snake_case					=    latents.to(self.device   )

         # set timesteps
         self.scheduler.set_timesteps(__snake_case   )

         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         snake_case					=    self.scheduler.timesteps.to(self.device   )

         # scale the initial noise by the standard deviation required by the scheduler
         snake_case					=    latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         snake_case					=    '''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
         snake_case					=    {}
         if accepts_eta:
              snake_case					=    eta

         for i, t in enumerate(self.progress_bar(__snake_case   )   ):
              # expand the latents if we are doing classifier free guidance
              snake_case					=    torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
              snake_case					=    self.scheduler.scale_model_input(__snake_case      , __snake_case   )

              # predict the noise residual
              snake_case					=    self.unet(__snake_case      , __snake_case      , encoder_hidden_states=__snake_case   ).sample

              # perform guidance
              if do_classifier_free_guidance:
                   snake_case			,  snake_case					=    noise_pred.chunk(2   )
                   snake_case					=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              # compute the previous noisy sample x_t -> x_t-1
              snake_case					=    self.scheduler.step(__snake_case      , __snake_case      , __snake_case      , **__snake_case   ).prev_sample

              # call the callback, if provided
              if callback is not None and i % callback_steps == 0:
                   callback(__snake_case      , __snake_case      , __snake_case   )

         snake_case					=    1 / 0.1_8215 * latents
         snake_case					=    self.vae.decode(__snake_case   ).sample

         snake_case					=    (image / 2 + 0.5).clamp(0      , 1   )

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         snake_case					=    image.cpu().permute(0      , 2      , 3      , 1   ).float().numpy()

         if output_type == "pil":
              snake_case					=    self.numpy_to_pil(__snake_case   )

         if not return_dict:
              return image

         return StableDiffusionPipelineOutput(images=__snake_case      , nsfw_content_detected=__snake_case   )








import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Any      				=   {
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'detr'
    __magic_name__        =		['past_key_values']
    __magic_name__        =		{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }
    def __init__(	self      , __snake_case=True      , __snake_case=None      , __snake_case=3      , __snake_case=1_0_0      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=6      , __snake_case=2_0_4_8      , __snake_case=8      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=True      , __snake_case="relu"      , __snake_case=2_5_6      , __snake_case=0.1      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.02      , __snake_case=1.0      , __snake_case=False      , __snake_case="sine"      , __snake_case="resnet50"      , __snake_case=True      , __snake_case=False      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=1      , __snake_case=1      , __snake_case=5      , __snake_case=2      , __snake_case=0.1      , **__snake_case      , ):
         if backbone_config is not None and use_timm_backbone:
              raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''   )

         if not use_timm_backbone:
              if backbone_config is None:
                   logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''   )
                   snake_case					=    CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
              elif isinstance(__snake_case      , __snake_case   ):
                   snake_case					=    backbone_config.get('''model_type'''   )
                   snake_case					=    CONFIG_MAPPING[backbone_model_type]
                   snake_case					=    config_class.from_dict(__snake_case   )
              # set timm attributes to None
              snake_case			,  snake_case			,  snake_case					=    None, None, None

         snake_case					=    use_timm_backbone
         snake_case					=    backbone_config
         snake_case					=    num_channels
         snake_case					=    num_queries
         snake_case					=    d_model
         snake_case					=    encoder_ffn_dim
         snake_case					=    encoder_layers
         snake_case					=    encoder_attention_heads
         snake_case					=    decoder_ffn_dim
         snake_case					=    decoder_layers
         snake_case					=    decoder_attention_heads
         snake_case					=    dropout
         snake_case					=    attention_dropout
         snake_case					=    activation_dropout
         snake_case					=    activation_function
         snake_case					=    init_std
         snake_case					=    init_xavier_std
         snake_case					=    encoder_layerdrop
         snake_case					=    decoder_layerdrop
         snake_case					=    encoder_layers
         snake_case					=    auxiliary_loss
         snake_case					=    position_embedding_type
         snake_case					=    backbone
         snake_case					=    use_pretrained_backbone
         snake_case					=    dilation
         # Hungarian matcher
         snake_case					=    class_cost
         snake_case					=    bbox_cost
         snake_case					=    giou_cost
         # Loss coefficients
         snake_case					=    mask_loss_coefficient
         snake_case					=    dice_loss_coefficient
         snake_case					=    bbox_loss_coefficient
         snake_case					=    giou_loss_coefficient
         snake_case					=    eos_coefficient
         super().__init__(is_encoder_decoder=__snake_case      , **__snake_case   )
    @property
    def 		a_      (	self   ):
         return self.encoder_attention_heads
    @property
    def 		a_      (	self   ):
         return self.d_model
    @classmethod
    def 		a_      (	cls      , __snake_case      , **__snake_case   ):
         return cls(backbone_config=__snake_case      , **__snake_case   )




    def 		a_      (	self   ):
         snake_case					=    copy.deepcopy(self.__dict__   )
         if output["backbone_config"] is not None:
              snake_case					=    self.backbone_config.to_dict()
         snake_case					=    self.__class__.model_type
         return output






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]   )
    @property
    def 		a_      (	self   ):
         return 1E-5




    @property
    def 		a_      (	self   ):
         return 1_2




