



'''simple docstring'''





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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__a          =		logging.get_logger(__name__)  # pylint: disable=invalid-name







class     UpperCAmelCase_       (       __SCREAMING_SNAKE_CASE		,		__SCREAMING_SNAKE_CASE	):
    """simple docstring"""







    @register_to_config
    def __init__(    self					:     List[Any] ,     snake_case_					:     int ,     snake_case_					:     Dict = None ,     snake_case_					:     Optional[int] = None						):
          super().__init__()

          snake_case__  :    int    						=      learnable

          if self.learnable:
                assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
                assert length is not None, "learnable=True requires `length` to be set"

                snake_case__  :    List[Any]    						=      torch.zeros(snake_case_ ,     snake_case_						)
          else:
                snake_case__  :    List[Any]    						=      None

          snake_case__  :    List[Any]    						=      torch.nn.Parameter(snake_case_						)







class     UpperCAmelCase_       (       __SCREAMING_SNAKE_CASE	):
    """simple docstring"""







    lowercase					=		42
    lowercase					=		42
    lowercase					=		42
    lowercase					=		42
    lowercase					=		42
    lowercase					=		42




    def __init__(    self					:     Union[str, Any] ,     snake_case_					:     List[str] ,     snake_case_					:     Dict ,     snake_case_					:     str ,     snake_case_					:     Optional[int] ,     snake_case_					:     Any ,     snake_case_					:     str ,     ):
          super().__init__()

          self.register_modules(
              vqvae=snake_case_ ,     transformer=snake_case_ ,     text_encoder=snake_case_ ,     tokenizer=snake_case_ ,     scheduler=snake_case_ ,     learned_classifier_free_sampling_embeddings=snake_case_ ,     )




    def lowerCamelCase       (    self					:     List[str] ,     snake_case_					:     Optional[int] ,     snake_case_					:     str ,     snake_case_					:     Optional[Any]						):
          snake_case__  :    int    						=      len(snake_case_						) if isinstance(snake_case_ ,     snake_case_						) else 1

          # get prompt text embeddings
          snake_case__  :    Tuple    						=      self.tokenizer(
              snake_case_ ,     padding="""max_length""" ,     max_length=self.tokenizer.model_max_length ,     return_tensors="""pt""" ,     )
          snake_case__  :    Dict    						=      text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                snake_case__  :    Union[str, Any]    						=      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__  :    Union[str, Any]    						=      text_input_ids[:, : self.tokenizer.model_max_length]
          snake_case__  :    Any    						=      self.text_encoder(text_input_ids.to(self.device						)						)[0]

          # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
          # While CLIP does normalize the pooled output of the text transformer when combining
          # the image and text embeddings, CLIP does not directly normalize the last hidden state.
          #
          # CLIP normalizing the pooled output.
          # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
          snake_case__  :    Tuple    						=      prompt_embeds / prompt_embeds.norm(dim=-1 ,     keepdim=snake_case_						)

          # duplicate text embeddings for each generation per prompt
          snake_case__  :    Optional[Any]    						=      prompt_embeds.repeat_interleave(snake_case_ ,     dim=0						)

          if do_classifier_free_guidance:
                if self.learned_classifier_free_sampling_embeddings.learnable:
                      snake_case__  :    Any    						=      self.learned_classifier_free_sampling_embeddings.embeddings
                      snake_case__  :    List[str]    						=      negative_prompt_embeds.unsqueeze(0						).repeat(snake_case_ ,     1 ,     1						)
                else:
                      snake_case__  :    Optional[int]    						=      [""""""] * batch_size

                      snake_case__  :    int    						=      text_input_ids.shape[-1]
                      snake_case__  :    Tuple    						=      self.tokenizer(
                          snake_case_ ,     padding="""max_length""" ,     max_length=snake_case_ ,     truncation=snake_case_ ,     return_tensors="""pt""" ,     )
                      snake_case__  :    Optional[int]    						=      self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
                      # See comment for normalizing text embeddings
                      snake_case__  :    Dict    						=      negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1 ,     keepdim=snake_case_						)

                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                snake_case__  :    Dict    						=      negative_prompt_embeds.shape[1]
                snake_case__  :    int    						=      negative_prompt_embeds.repeat(1 ,     snake_case_ ,     1						)
                snake_case__  :    Tuple    						=      negative_prompt_embeds.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__  :    List[str]    						=      torch.cat([negative_prompt_embeds, prompt_embeds]						)

          return prompt_embeds




    @torch.no_grad()
    def __call__(    self					:     Any ,     snake_case_					:     Optional[int] ,     snake_case_					:     List[str] = 100 ,     snake_case_					:     Dict = 5.0 ,     snake_case_					:     Tuple = 1.0 ,     snake_case_					:     List[Any] = 1 ,     snake_case_					:     Union[str, Any] = None ,     snake_case_					:     List[Any] = None ,     snake_case_					:     List[str] = "pil" ,     snake_case_					:     Dict = True ,     snake_case_					:     Optional[Any] = None ,     snake_case_					:     Optional[int] = 1 ,     ):
          if isinstance(snake_case_ ,     snake_case_						):
                snake_case__  :    List[str]    						=      1
          elif isinstance(snake_case_ ,     snake_case_						):
                snake_case__  :    Dict    						=      len(snake_case_						)
          else:
                raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(snake_case_						)}"						)

          snake_case__  :    Tuple    						=      batch_size * num_images_per_prompt

          snake_case__  :    Dict    						=      guidance_scale > 1.0

          snake_case__  :    Tuple    						=      self._encode_prompt(snake_case_ ,     snake_case_ ,     snake_case_						)

          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 the initial completely masked latents unless the user supplied it

          snake_case__  :    List[Any]    						=      (batch_size, self.transformer.num_latent_pixels)
          if latents is None:
                snake_case__  :    int    						=      self.transformer.num_vector_embeds - 1
                snake_case__  :    Any    						=      torch.full(snake_case_ ,     snake_case_						).to(self.device						)
          else:
                if latents.shape != latents_shape:
                      raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"						)
                if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                      raise ValueError(
                          """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
                          f" {self.transformer.num_vector_embeds - 1} (inclusive)."						)
                snake_case__  :    Union[str, Any]    						=      latents.to(self.device						)

          # set timesteps
          self.scheduler.set_timesteps(snake_case_ ,     device=self.device						)

          snake_case__  :    Tuple    						=      self.scheduler.timesteps.to(self.device						)

          snake_case__  :    Any    						=      latents

          for i, t in enumerate(self.progress_bar(snake_case_						)						):
                # expand the sample if we are doing classifier free guidance
                snake_case__  :    Dict    						=      torch.cat([sample] * 2						) if do_classifier_free_guidance else sample

                # predict the un-noised image
                # model_output == `log_p_x_0`
                snake_case__  :    Tuple    						=      self.transformer(snake_case_ ,     encoder_hidden_states=snake_case_ ,     timestep=snake_case_						).sample

                if do_classifier_free_guidance:
                      snake_case__  :    Optional[int]    						=      model_output.chunk(2						)
                      snake_case__  :    Optional[int]    						=      model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                      model_output -= torch.logsumexp(snake_case_ ,     dim=1 ,     keepdim=snake_case_						)

                snake_case__  :    List[str]    						=      self.truncate(snake_case_ ,     snake_case_						)

                # remove `log(0)`'s (`-inf`s)
                snake_case__  :    List[Any]    						=      model_output.clamp(-70						)

                # compute the previous noisy sample x_t -> x_t-1
                snake_case__  :    Dict    						=      self.scheduler.step(snake_case_ ,     timestep=snake_case_ ,     sample=snake_case_ ,     generator=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__  :    int    						=      self.vqvae.config.vq_embed_dim
          snake_case__  :    Optional[Any]    						=      (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
          snake_case__  :    Optional[int]    						=      self.vqvae.quantize.get_codebook_entry(snake_case_ ,     shape=snake_case_						)
          snake_case__  :    Any    						=      self.vqvae.decode(snake_case_ ,     force_not_quantize=snake_case_						).sample

          snake_case__  :    Any    						=      (image / 2 + 0.5).clamp(0 ,     1						)
          snake_case__  :    int    						=      image.cpu().permute(0 ,     2 ,     3 ,     1						).numpy()

          if output_type == "pil":
                snake_case__  :    List[Any]    						=      self.numpy_to_pil(snake_case_						)

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=snake_case_						)




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     List[Any] ,     snake_case_					:     Optional[Any]						):
          snake_case__  :    Optional[Any]    						=      torch.sort(snake_case_ ,     1 ,     descending=snake_case_						)
          snake_case__  :    List[Any]    						=      torch.exp(snake_case_						)
          snake_case__  :    Dict    						=      sorted_p_x_0.cumsum(dim=1						) < truncation_rate

          # Ensure that at least the largest probability is not zeroed out
          snake_case__  :    Optional[Any]    						=      torch.full_like(keep_mask[:, 0:1, :] ,     snake_case_						)
          snake_case__  :    Union[str, Any]    						=      torch.cat((all_true, keep_mask) ,     dim=1						)
          snake_case__  :    Optional[int]    						=      keep_mask[:, :-1, :]

          snake_case__  :    str    						=      keep_mask.gather(1 ,     indices.argsort(1						)						)

          snake_case__  :    Optional[Any]    						=      log_p_x_0.clone()

          snake_case__  :    str    						=      -torch.inf  # -inf = log(0)

          return rv


import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""conditional_detr"""
      _UpperCAmelCase				:    Optional[int]           =			["""past_key_values"""]
      _UpperCAmelCase				:    Optional[Any]           =			{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__=None    ,  __magic_name__=3    ,  __magic_name__=3_0_0    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=True    ,  __magic_name__="relu"    ,  __magic_name__=2_5_6    ,  __magic_name__=0.1    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.02    ,  __magic_name__=1.0    ,  __magic_name__=False    ,  __magic_name__="sine"    ,  __magic_name__="resnet50"    ,  __magic_name__=True    ,  __magic_name__=False    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=1    ,  __magic_name__=1    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=0.25    ,  **__magic_name__    ,  ):
       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."""      )
         lowerCamelCase		:	Optional[int]							=      CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]      )
        elif isinstance(__magic_name__    ,  __magic_name__      ):
         lowerCamelCase		:	List[Any]							=      backbone_config.get("""model_type"""      )
         lowerCamelCase		:	Dict							=      CONFIG_MAPPING[backbone_model_type]
         lowerCamelCase		:	str							=      config_class.from_dict(__magic_name__      )

       lowerCamelCase		:	Dict							=      use_timm_backbone
       lowerCamelCase		:	str							=      backbone_config
       lowerCamelCase		:	Tuple							=      num_channels
       lowerCamelCase		:	Dict							=      num_queries
       lowerCamelCase		:	Any							=      d_model
       lowerCamelCase		:	Optional[Any]							=      encoder_ffn_dim
       lowerCamelCase		:	List[str]							=      encoder_layers
       lowerCamelCase		:	Union[str, Any]							=      encoder_attention_heads
       lowerCamelCase		:	Any							=      decoder_ffn_dim
       lowerCamelCase		:	Dict							=      decoder_layers
       lowerCamelCase		:	Union[str, Any]							=      decoder_attention_heads
       lowerCamelCase		:	Dict							=      dropout
       lowerCamelCase		:	List[str]							=      attention_dropout
       lowerCamelCase		:	Union[str, Any]							=      activation_dropout
       lowerCamelCase		:	Optional[int]							=      activation_function
       lowerCamelCase		:	int							=      init_std
       lowerCamelCase		:	str							=      init_xavier_std
       lowerCamelCase		:	Tuple							=      encoder_layerdrop
       lowerCamelCase		:	str							=      decoder_layerdrop
       lowerCamelCase		:	Tuple							=      encoder_layers
       lowerCamelCase		:	Optional[int]							=      auxiliary_loss
       lowerCamelCase		:	Optional[Any]							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      backbone
       lowerCamelCase		:	Union[str, Any]							=      use_pretrained_backbone
       lowerCamelCase		:	str							=      dilation
       # Hungarian matcher
       lowerCamelCase		:	Optional[Any]							=      class_cost
       lowerCamelCase		:	Dict							=      bbox_cost
       lowerCamelCase		:	Tuple							=      giou_cost
       # Loss coefficients
       lowerCamelCase		:	Union[str, Any]							=      mask_loss_coefficient
       lowerCamelCase		:	Dict							=      dice_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      cls_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      bbox_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      giou_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      focal_alpha
       super().__init__(is_encoder_decoder=__magic_name__    ,  **__magic_name__      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.encoder_attention_heads
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.d_model






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      copy.deepcopy(self.__dict__      )
       if self.backbone_config is not None:
        lowerCamelCase		:	Optional[int]							=      self.backbone_config.to_dict()
       lowerCamelCase		:	Optional[Any]							=      self.__class__.model_type
       return output





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Any           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-5






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




