

import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	CLIPSegTextConfig()
  UpperCAmelCase          =	CLIPSegVisionConfig(patch_size=16      )

  UpperCAmelCase          =	True if "refined" in model_name else False
  UpperCAmelCase          =	16 if "rd16" in model_name else 64

  UpperCAmelCase          =	CLIPSegConfig.from_text_vision_configs(
      __A ,							__A ,							use_complex_transposed_convolution=__A ,							reduce_dim=__A ,							)
  return config
def   _lowerCAmelCase(						__A      ):
  # update prefixes
  if "clip_model" in name:
    UpperCAmelCase          =	name.replace("clip_model" ,							"clip"      )
  if "transformer" in name:
    if "visual" in name:
      UpperCAmelCase          =	name.replace("visual.transformer" ,							"vision_model"      )
    else:
      UpperCAmelCase          =	name.replace("transformer" ,							"text_model"      )
  if "resblocks" in name:
    UpperCAmelCase          =	name.replace("resblocks" ,							"encoder.layers"      )
  if "ln_1" in name:
    UpperCAmelCase          =	name.replace("ln_1" ,							"layer_norm1"      )
  if "ln_2" in name:
    UpperCAmelCase          =	name.replace("ln_2" ,							"layer_norm2"      )
  if "c_fc" in name:
    UpperCAmelCase          =	name.replace("c_fc" ,							"fc1"      )
  if "c_proj" in name:
    UpperCAmelCase          =	name.replace("c_proj" ,							"fc2"      )
  if "attn" in name and "self" not in name:
    UpperCAmelCase          =	name.replace("attn" ,							"self_attn"      )
  # text encoder
  if "token_embedding" in name:
    UpperCAmelCase          =	name.replace("token_embedding" ,							"text_model.embeddings.token_embedding"      )
  if "positional_embedding" in name and "visual" not in name:
    UpperCAmelCase          =	name.replace("positional_embedding" ,							"text_model.embeddings.position_embedding.weight"      )
  if "ln_final" in name:
    UpperCAmelCase          =	name.replace("ln_final" ,							"text_model.final_layer_norm"      )
  # vision encoder
  if "visual.class_embedding" in name:
    UpperCAmelCase          =	name.replace("visual.class_embedding" ,							"vision_model.embeddings.class_embedding"      )
  if "visual.conv1" in name:
    UpperCAmelCase          =	name.replace("visual.conv1" ,							"vision_model.embeddings.patch_embedding"      )
  if "visual.positional_embedding" in name:
    UpperCAmelCase          =	name.replace("visual.positional_embedding" ,							"vision_model.embeddings.position_embedding.weight"      )
  if "visual.ln_pre" in name:
    UpperCAmelCase          =	name.replace("visual.ln_pre" ,							"vision_model.pre_layrnorm"      )
  if "visual.ln_post" in name:
    UpperCAmelCase          =	name.replace("visual.ln_post" ,							"vision_model.post_layernorm"      )
  # projection layers
  if "visual.proj" in name:
    UpperCAmelCase          =	name.replace("visual.proj" ,							"visual_projection.weight"      )
  if "text_projection" in name:
    UpperCAmelCase          =	name.replace("text_projection" ,							"text_projection.weight"      )
  # decoder
  if "trans_conv" in name:
    UpperCAmelCase          =	name.replace("trans_conv" ,							"transposed_convolution"      )
  if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
    UpperCAmelCase          =	"decoder." + name
  if "blocks" in name:
    UpperCAmelCase          =	name.replace("blocks" ,							"decoder.layers"      )
  if "linear1" in name:
    UpperCAmelCase          =	name.replace("linear1" ,							"mlp.fc1"      )
  if "linear2" in name:
    UpperCAmelCase          =	name.replace("linear2" ,							"mlp.fc2"      )
  if "norm1" in name and "layer_" not in name:
    UpperCAmelCase          =	name.replace("norm1" ,							"layer_norm1"      )
  if "norm2" in name and "layer_" not in name:
    UpperCAmelCase          =	name.replace("norm2" ,							"layer_norm2"      )

  return name
def   _lowerCAmelCase(						__A ,							__A      ):
  for key in orig_state_dict.copy().keys():
    UpperCAmelCase          =	orig_state_dict.pop(__A      )

    if key.startswith("clip_model"      ) and "attn.in_proj" in key:
      UpperCAmelCase          =	key.split("."      )
      if "visual" in key:
        UpperCAmelCase          =	int(key_split[4]      )
        UpperCAmelCase          =	config.vision_config.hidden_size
        UpperCAmelCase          =	"vision_model"
      else:
        UpperCAmelCase          =	int(key_split[3]      )
        UpperCAmelCase          =	config.text_config.hidden_size
        UpperCAmelCase          =	"text_model"

      if "weight" in key:
        UpperCAmelCase          =	val[:dim, :]
        UpperCAmelCase          =	val[
            dim : dim * 2, :
        ]
        UpperCAmelCase          =	val[-dim:, :]
      else:
        UpperCAmelCase          =	val[:dim]
        UpperCAmelCase          =	val[dim : dim * 2]
        UpperCAmelCase          =	val[-dim:]
    elif "self_attn" in key and "out_proj" not in key:
      UpperCAmelCase          =	key.split("."      )
      UpperCAmelCase          =	int(key_split[1]      )
      UpperCAmelCase          =	config.reduce_dim
      if "weight" in key:
        UpperCAmelCase          =	val[:dim, :]
        UpperCAmelCase          =	val[dim : dim * 2, :]
        UpperCAmelCase          =	val[-dim:, :]
      else:
        UpperCAmelCase          =	val[:dim]
        UpperCAmelCase          =	val[dim : dim * 2]
        UpperCAmelCase          =	val[-dim:]
    else:
      UpperCAmelCase          =	rename_key(__A      )
      if "visual_projection" in new_name or "text_projection" in new_name:
        UpperCAmelCase          =	val.T
      UpperCAmelCase          =	val

  return orig_state_dict
def   _lowerCAmelCase(						):
  UpperCAmelCase          =	"http://images.cocodataset.org/val2017/000000039769.jpg"
  UpperCAmelCase          =	Image.open(requests.get(__A ,							stream=__A      ).raw      )
  return image
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A      ):
  UpperCAmelCase          =	get_clipseg_config(__A      )
  UpperCAmelCase          =	CLIPSegForImageSegmentation(__A      )
  model.eval()

  UpperCAmelCase          =	torch.load(__A ,							map_location="cpu"      )

  # remove some keys
  for key in state_dict.copy().keys():
    if key.startswith("model"      ):
      state_dict.pop(__A ,							__A      )

    # rename some keys
  UpperCAmelCase          =	convert_state_dict(__A ,							__A      )
  UpperCAmelCase   ,						UpperCAmelCase          =	model.load_state_dict(__A ,							strict=__A      )

  if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
    raise ValueError("Missing keys that are not expected: {}".format(__A      )      )
  if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
    raise ValueError(F"Unexpected keys: {unexpected_keys}"      )

  UpperCAmelCase          =	ViTImageProcessor(size=352      )
  UpperCAmelCase          =	CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"      )
  UpperCAmelCase          =	CLIPSegProcessor(image_processor=__A ,							tokenizer=__A      )

  UpperCAmelCase          =	prepare_img()
  UpperCAmelCase          =	["a glass", "something to fill", "wood", "a jar"]

  UpperCAmelCase          =	processor(text=__A ,							images=[image] * len(__A      ) ,							padding="max_length" ,							return_tensors="pt"      )

  with torch.no_grad():
    UpperCAmelCase          =	model(**__A      )

  # verify values
  UpperCAmelCase          =	torch.tensor([0.1110, -0.1882, 0.1645]      )
  UpperCAmelCase          =	torch.tensor([0.2692, -0.7197, -0.1328]      )
  if model_name == "clipseg-rd64-refined":
    UpperCAmelCase          =	torch.tensor(
        [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]      )
  elif model_name == "clipseg-rd64":
    UpperCAmelCase          =	torch.tensor(
        [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]      )
  elif model_name == "clipseg-rd16":
    UpperCAmelCase          =	torch.tensor(
        [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]      )
  else:
    raise ValueError(F"Model name {model_name} not supported."      )

  assert torch.allclose(outputs.logits[0, :3, :3] ,							__A ,							atol=1E-3      )
  assert torch.allclose(outputs.conditional_embeddings[0, :3] ,							__A ,							atol=1E-3      )
  assert torch.allclose(outputs.pooled_output[0, :3] ,							__A ,							atol=1E-3      )
  print("Looks ok!"      )

  if pytorch_dump_folder_path is not None:
    print(F"Saving model and processor to {pytorch_dump_folder_path}"      )
    model.save_pretrained(__A      )
    processor.save_pretrained(__A      )

  if push_to_hub:
    print(F"Pushing model and processor for {model_name} to the hub"      )
    model.push_to_hub(F"CIDAS/{model_name}"      )
    processor.push_to_hub(F"CIDAS/{model_name}"      )


if __name__ == "__main__":
      lowerCAmelCase__							=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--model_name",
          default="clipseg-rd64",
          type=str,
          choices=["clipseg-rd16", "clipseg-rd64", "clipseg-rd64-refined"],
          help=(
              "Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"
              " reduce dimension)"
          ),
      )
      parser.add_argument(
          "--checkpoint_path",
          default="/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth",
          type=str,
          help=(
              "Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"
              " the decoder weights."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )
      parser.add_argument(
          "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
      )

      lowerCAmelCase__							=			parser.parse_args()
      convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCAmelCase__							=			{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			["XLMProphetNetTokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			[
          "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMProphetNetDecoder",
          "XLMProphetNetEncoder",
          "XLMProphetNetForCausalLM",
          "XLMProphetNetForConditionalGeneration",
          "XLMProphetNetModel",
          "XLMProphetNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      lowerCAmelCase__							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



