'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def       UpperCamelCase_				(   _UpperCAmelCase    : Any       )       ->       List[Any]:


       """simple docstring"""







       if "img_encoder.pos_embed" in name:
              _UpperCAmelCase	:    List[str]				 =       name.replace("img_encoder.pos_embed"     ,      "vision_model.embeddings.position_embeddings"       )
       if "img_encoder.patch_embed.proj" in name:
              _UpperCAmelCase	:    Dict				 =       name.replace("img_encoder.patch_embed.proj"     ,      "vision_model.embeddings.patch_embeddings.projection"       )
       if "img_encoder.patch_embed.norm" in name:
              _UpperCAmelCase	:    int				 =       name.replace("img_encoder.patch_embed.norm"     ,      "vision_model.embeddings.layernorm"       )
       if "img_encoder.layers" in name:
              _UpperCAmelCase	:    Any				 =       name.replace("img_encoder.layers"     ,      "vision_model.encoder.stages"       )
       if "blocks" in name and "res" not in name:
              _UpperCAmelCase	:    Optional[Any]				 =       name.replace("blocks"     ,      "layers"       )
       if "attn" in name and "pre_assign" not in name:
              _UpperCAmelCase	:    str				 =       name.replace("attn"     ,      "self_attn"       )
       if "proj" in name and "self_attn" in name and "text" not in name:
              _UpperCAmelCase	:    List[str]				 =       name.replace("proj"     ,      "out_proj"       )
       if "pre_assign_attn.attn.proj" in name:
              _UpperCAmelCase	:    Optional[int]				 =       name.replace("pre_assign_attn.attn.proj"     ,      "pre_assign_attn.attn.out_proj"       )
       if "norm1" in name:
              _UpperCAmelCase	:    Optional[int]				 =       name.replace("norm1"     ,      "layer_norm1"       )
       if "norm2" in name and "pre_assign" not in name:
              _UpperCAmelCase	:    List[Any]				 =       name.replace("norm2"     ,      "layer_norm2"       )
       if "img_encoder.norm" in name:
              _UpperCAmelCase	:    Optional[int]				 =       name.replace("img_encoder.norm"     ,      "vision_model.layernorm"       )
       # text encoder
       if "text_encoder.token_embedding" in name:
              _UpperCAmelCase	:    Optional[int]				 =       name.replace("text_encoder.token_embedding"     ,      "text_model.embeddings.token_embedding"       )
       if "text_encoder.positional_embedding" in name:
              _UpperCAmelCase	:    Tuple				 =       name.replace("text_encoder.positional_embedding"     ,      "text_model.embeddings.position_embedding.weight"       )
       if "text_encoder.transformer.resblocks." in name:
              _UpperCAmelCase	:    int				 =       name.replace("text_encoder.transformer.resblocks."     ,      "text_model.encoder.layers."       )
       if "ln_1" in name:
              _UpperCAmelCase	:    List[str]				 =       name.replace("ln_1"     ,      "layer_norm1"       )
       if "ln_2" in name:
              _UpperCAmelCase	:    Optional[int]				 =       name.replace("ln_2"     ,      "layer_norm2"       )
       if "c_fc" in name:
              _UpperCAmelCase	:    Union[str, Any]				 =       name.replace("c_fc"     ,      "fc1"       )
       if "c_proj" in name:
              _UpperCAmelCase	:    str				 =       name.replace("c_proj"     ,      "fc2"       )
       if "text_encoder" in name:
              _UpperCAmelCase	:    str				 =       name.replace("text_encoder"     ,      "text_model"       )
       if "ln_final" in name:
              _UpperCAmelCase	:    int				 =       name.replace("ln_final"     ,      "final_layer_norm"       )
       # projection layers
       if "img_projector.linear_hidden." in name:
              _UpperCAmelCase	:    Tuple				 =       name.replace("img_projector.linear_hidden."     ,      "visual_projection."       )
       if "img_projector.linear_out." in name:
              _UpperCAmelCase	:    List[str]				 =       name.replace("img_projector.linear_out."     ,      "visual_projection.3."       )
       if "text_projector.linear_hidden" in name:
              _UpperCAmelCase	:    Union[str, Any]				 =       name.replace("text_projector.linear_hidden"     ,      "text_projection"       )
       if "text_projector.linear_out" in name:
              _UpperCAmelCase	:    Dict				 =       name.replace("text_projector.linear_out"     ,      "text_projection.3"       )

       return name

def       UpperCamelCase_				(   _UpperCAmelCase    : Dict     ,      _UpperCAmelCase    : Any       )       ->       Any:


       """simple docstring"""







       for key in orig_state_dict.copy().keys():
              _UpperCAmelCase	:    Union[str, Any]				 =       orig_state_dict.pop(lowerCamelCase_       )

              if "qkv" in key:
                     # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     _UpperCAmelCase	:    Union[str, Any]				 =       key.split("."       )
                     _UpperCAmelCase	:    List[str]				 =       int(key_split[2]       ), int(key_split[4]       )
                     _UpperCAmelCase	:    str				 =       config.vision_config.hidden_size
                     if "weight" in key:
                            _UpperCAmelCase	:    Union[str, Any]				 =       val[:dim, :]
                            _UpperCAmelCase	:    Union[str, Any]				 =       val[dim : dim * 2, :]
                            _UpperCAmelCase	:    Dict				 =       val[-dim:, :]
                     else:
                            _UpperCAmelCase	:    Optional[Any]				 =       val[:dim]
                            _UpperCAmelCase	:    List[Any]				 =       val[dim : dim * 2]
                            _UpperCAmelCase	:    str				 =       val[-dim:]
              elif "in_proj" in key:
                     # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
                     # we need to split them up into separate matrices/vectors
                     _UpperCAmelCase	:    Tuple				 =       key.split("."       )
                     _UpperCAmelCase	:    str				 =       int(key_split[3]       )
                     _UpperCAmelCase	:    Dict				 =       config.text_config.hidden_size
                     if "weight" in key:
                            _UpperCAmelCase	:    str				 =       val[:dim, :]
                            _UpperCAmelCase	:    Union[str, Any]				 =       val[
                                dim : dim * 2, :
                            ]
                            _UpperCAmelCase	:    int				 =       val[-dim:, :]
                     else:
                            _UpperCAmelCase	:    Dict				 =       val[:dim]
                            _UpperCAmelCase	:    List[Any]				 =       val[dim : dim * 2]
                            _UpperCAmelCase	:    List[Any]				 =       val[-dim:]
              else:
                     _UpperCAmelCase	:    Union[str, Any]				 =       rename_key(lowerCamelCase_       )
                     # squeeze if necessary
                     if (
                         "text_projection.0" in new_name
                         or "text_projection.3" in new_name
                         or "visual_projection.0" in new_name
                         or "visual_projection.3" in new_name
                     ):
                            _UpperCAmelCase	:    Any				 =       val.squeeze_()
                     else:
                            _UpperCAmelCase	:    Optional[Any]				 =       val

       return orig_state_dict

def       UpperCamelCase_				(   )       ->       List[Any]:


       """simple docstring"""







       _UpperCAmelCase	:    Dict				 =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase	:    List[Any]				 =       Image.open(requests.get(lowerCamelCase_     ,      stream=lowerCamelCase_       ).raw       )
       return im

@torch.no_grad()
def       UpperCamelCase_				(   _UpperCAmelCase    : str     ,      _UpperCAmelCase    : Optional[int]     ,      _UpperCAmelCase    : str="groupvit-gcc-yfcc"     ,      _UpperCAmelCase    : Dict=False       )       ->       List[Any]:


       """simple docstring"""







       _UpperCAmelCase	:    Optional[Any]				 =       GroupViTConfig()
       _UpperCAmelCase	:    Optional[int]				 =       GroupViTModel(lowerCamelCase_       ).eval()

       _UpperCAmelCase	:    str				 =       torch.load(lowerCamelCase_     ,      map_location="cpu"       )["""model"""]
       _UpperCAmelCase	:    Tuple				 =       convert_state_dict(lowerCamelCase_     ,      lowerCamelCase_       )
       _UpperCAmelCase	:    Optional[Any]				 =       model.load_state_dict(lowerCamelCase_     ,      strict=lowerCamelCase_       )
       assert missing_keys == ["text_model.embeddings.position_ids"]
       assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowerCamelCase_       ) == 0)

       # verify result
       _UpperCAmelCase	:    Union[str, Any]				 =       CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"       )
       _UpperCAmelCase	:    List[str]				 =       prepare_img()
       _UpperCAmelCase	:    List[str]				 =       processor(text=["a photo of a cat", "a photo of a dog"]     ,      images=lowerCamelCase_     ,      padding=lowerCamelCase_     ,      return_tensors="pt"       )

       with torch.no_grad():
              _UpperCAmelCase	:    Optional[int]				 =       model(**lowerCamelCase_       )

       if model_name == "groupvit-gcc-yfcc":
              _UpperCAmelCase	:    Any				 =       torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]       )
       elif model_name == "groupvit-gcc-redcaps":
              _UpperCAmelCase	:    int				 =       torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]       )
       else:
              raise ValueError(F"""Model name {model_name} not supported."""       )
       assert torch.allclose(outputs.logits_per_image     ,      lowerCamelCase_     ,      atol=1e-3       )

       processor.save_pretrained(lowerCamelCase_       )
       model.save_pretrained(lowerCamelCase_       )
       print("Successfully saved processor and model to"     ,      lowerCamelCase_       )

       if push_to_hub:
              print("Pushing to the hub..."       )
              processor.push_to_hub(lowerCamelCase_     ,      organization="nielsr"       )
              model.push_to_hub(lowerCamelCase_     ,      organization="nielsr"       )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE			:	Optional[int]	=    argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
  )
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
  parser.add_argument(
      """--model_name""",
      default="""groupvit-gccy-fcc""",
      type=str,
      help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
      help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
  )
  __SCREAMING_SNAKE_CASE			:	Tuple	=    parser.parse_args()

  convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)








'''simple docstring'''




__UpperCAmelCase            =       """0.18.2"""

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
							if not is_onnx_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_onnx_objects import *  # noqa F403
else:
							from .pipelines import OnnxRuntimeModel

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_pt_objects import *  # noqa F403
else:
							from .models import (
							    AutoencoderKL,
							    ControlNetModel,
							    ModelMixin,
							    PriorTransformer,
							    TaFilmDecoder,
							    TransformeraDModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    VQModel,
							)
							from .optimization import (
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							    get_scheduler,
							)
							from .pipelines import (
							    AudioPipelineOutput,
							    ConsistencyModelPipeline,
							    DanceDiffusionPipeline,
							    DDIMPipeline,
							    DDPMPipeline,
							    DiffusionPipeline,
							    DiTPipeline,
							    ImagePipelineOutput,
							    KarrasVePipeline,
							    LDMPipeline,
							    LDMSuperResolutionPipeline,
							    PNDMPipeline,
							    RePaintPipeline,
							    ScoreSdeVePipeline,
							)
							from .schedulers import (
							    CMStochasticIterativeScheduler,
							    DDIMInverseScheduler,
							    DDIMParallelScheduler,
							    DDIMScheduler,
							    DDPMParallelScheduler,
							    DDPMScheduler,
							    DEISMultistepScheduler,
							    DPMSolverMultistepInverseScheduler,
							    DPMSolverMultistepScheduler,
							    DPMSolverSinglestepScheduler,
							    EulerAncestralDiscreteScheduler,
							    EulerDiscreteScheduler,
							    HeunDiscreteScheduler,
							    IPNDMScheduler,
							    KarrasVeScheduler,
							    KDPMaAncestralDiscreteScheduler,
							    KDPMaDiscreteScheduler,
							    PNDMScheduler,
							    RePaintScheduler,
							    SchedulerMixin,
							    ScoreSdeVeScheduler,
							    UnCLIPScheduler,
							    UniPCMultistepScheduler,
							    VQDiffusionScheduler,
							)
							from .training_utils import EMAModel

try:
							if not (is_torch_available() and is_scipy_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
							from .schedulers import LMSDiscreteScheduler

try:
							if not (is_torch_available() and is_torchsde_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
							from .schedulers import DPMSolverSDEScheduler

try:
							if not (is_torch_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
							from .pipelines import (
							    AltDiffusionImgaImgPipeline,
							    AltDiffusionPipeline,
							    AudioLDMPipeline,
							    CycleDiffusionPipeline,
							    IFImgaImgPipeline,
							    IFImgaImgSuperResolutionPipeline,
							    IFInpaintingPipeline,
							    IFInpaintingSuperResolutionPipeline,
							    IFPipeline,
							    IFSuperResolutionPipeline,
							    ImageTextPipelineOutput,
							    KandinskyImgaImgPipeline,
							    KandinskyInpaintPipeline,
							    KandinskyPipeline,
							    KandinskyPriorPipeline,
							    KandinskyVaaControlnetImgaImgPipeline,
							    KandinskyVaaControlnetPipeline,
							    KandinskyVaaImgaImgPipeline,
							    KandinskyVaaInpaintPipeline,
							    KandinskyVaaPipeline,
							    KandinskyVaaPriorEmbaEmbPipeline,
							    KandinskyVaaPriorPipeline,
							    LDMTextToImagePipeline,
							    PaintByExamplePipeline,
							    SemanticStableDiffusionPipeline,
							    ShapEImgaImgPipeline,
							    ShapEPipeline,
							    StableDiffusionAttendAndExcitePipeline,
							    StableDiffusionControlNetImgaImgPipeline,
							    StableDiffusionControlNetInpaintPipeline,
							    StableDiffusionControlNetPipeline,
							    StableDiffusionDepthaImgPipeline,
							    StableDiffusionDiffEditPipeline,
							    StableDiffusionImageVariationPipeline,
							    StableDiffusionImgaImgPipeline,
							    StableDiffusionInpaintPipeline,
							    StableDiffusionInpaintPipelineLegacy,
							    StableDiffusionInstructPixaPixPipeline,
							    StableDiffusionLatentUpscalePipeline,
							    StableDiffusionLDMaDPipeline,
							    StableDiffusionModelEditingPipeline,
							    StableDiffusionPanoramaPipeline,
							    StableDiffusionParadigmsPipeline,
							    StableDiffusionPipeline,
							    StableDiffusionPipelineSafe,
							    StableDiffusionPixaPixZeroPipeline,
							    StableDiffusionSAGPipeline,
							    StableDiffusionUpscalePipeline,
							    StableUnCLIPImgaImgPipeline,
							    StableUnCLIPPipeline,
							    TextToVideoSDPipeline,
							    TextToVideoZeroPipeline,
							    UnCLIPImageVariationPipeline,
							    UnCLIPPipeline,
							    UniDiffuserModel,
							    UniDiffuserPipeline,
							    UniDiffuserTextDecoder,
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							    VideoToVideoSDPipeline,
							    VQDiffusionPipeline,
							)

try:
							if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
							from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
							if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
							from .pipelines import StableDiffusionKDiffusionPipeline

try:
							if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
							from .pipelines import (
							    OnnxStableDiffusionImgaImgPipeline,
							    OnnxStableDiffusionInpaintPipeline,
							    OnnxStableDiffusionInpaintPipelineLegacy,
							    OnnxStableDiffusionPipeline,
							    OnnxStableDiffusionUpscalePipeline,
							    StableDiffusionOnnxPipeline,
							)

try:
							if not (is_torch_available() and is_librosa_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
							from .pipelines import AudioDiffusionPipeline, Mel

try:
							if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
							from .pipelines import SpectrogramDiffusionPipeline

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_objects import *  # noqa F403
else:
							from .models.controlnet_flax import FlaxControlNetModel
							from .models.modeling_flax_utils import FlaxModelMixin
							from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .models.vae_flax import FlaxAutoencoderKL
							from .pipelines import FlaxDiffusionPipeline
							from .schedulers import (
							    FlaxDDIMScheduler,
							    FlaxDDPMScheduler,
							    FlaxDPMSolverMultistepScheduler,
							    FlaxKarrasVeScheduler,
							    FlaxLMSDiscreteScheduler,
							    FlaxPNDMScheduler,
							    FlaxSchedulerMixin,
							    FlaxScoreSdeVeScheduler,
							)


try:
							if not (is_flax_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
							from .pipelines import (
							    FlaxStableDiffusionControlNetPipeline,
							    FlaxStableDiffusionImgaImgPipeline,
							    FlaxStableDiffusionInpaintPipeline,
							    FlaxStableDiffusionPipeline,
							)

try:
							if not (is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_note_seq_objects import *  # noqa F403
else:
							from .pipelines import MidiProcessor



