'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)



def  _lowerCAmelCase				(				__a      )  ->      Optional[int]:
     '''simple docstring'''







     _UpperCamelCase  :Optional[Any]       =CLIPSegTextConfig()
     _UpperCamelCase  :List[str]       =CLIPSegVisionConfig(patch_size=16      )

     _UpperCamelCase  :List[Any]       =True if """refined""" in model_name else False
     _UpperCamelCase  :str       =16 if """rd16""" in model_name else 64

     _UpperCamelCase  :str       =CLIPSegConfig.from_text_vision_configs(
         __a			,      __a			,      use_complex_transposed_convolution=__a			,      reduce_dim=__a			,      )
     return config



def  _lowerCAmelCase				(				__a      )  ->      List[str]:
     '''simple docstring'''







     if "clip_model" in name:
          _UpperCamelCase  :str       =name.replace("""clip_model"""			,      """clip"""      )
     if "transformer" in name:
          if "visual" in name:
               _UpperCamelCase  :Tuple       =name.replace("""visual.transformer"""			,      """vision_model"""      )
          else:
               _UpperCamelCase  :int       =name.replace("""transformer"""			,      """text_model"""      )
     if "resblocks" in name:
          _UpperCamelCase  :Tuple       =name.replace("""resblocks"""			,      """encoder.layers"""      )
     if "ln_1" in name:
          _UpperCamelCase  :Any       =name.replace("""ln_1"""			,      """layer_norm1"""      )
     if "ln_2" in name:
          _UpperCamelCase  :List[str]       =name.replace("""ln_2"""			,      """layer_norm2"""      )
     if "c_fc" in name:
          _UpperCamelCase  :List[Any]       =name.replace("""c_fc"""			,      """fc1"""      )
     if "c_proj" in name:
          _UpperCamelCase  :int       =name.replace("""c_proj"""			,      """fc2"""      )
     if "attn" in name and "self" not in name:
          _UpperCamelCase  :List[str]       =name.replace("""attn"""			,      """self_attn"""      )
     # text encoder
     if "token_embedding" in name:
          _UpperCamelCase  :int       =name.replace("""token_embedding"""			,      """text_model.embeddings.token_embedding"""      )
     if "positional_embedding" in name and "visual" not in name:
          _UpperCamelCase  :Tuple       =name.replace("""positional_embedding"""			,      """text_model.embeddings.position_embedding.weight"""      )
     if "ln_final" in name:
          _UpperCamelCase  :Optional[Any]       =name.replace("""ln_final"""			,      """text_model.final_layer_norm"""      )
     # vision encoder
     if "visual.class_embedding" in name:
          _UpperCamelCase  :Optional[Any]       =name.replace("""visual.class_embedding"""			,      """vision_model.embeddings.class_embedding"""      )
     if "visual.conv1" in name:
          _UpperCamelCase  :int       =name.replace("""visual.conv1"""			,      """vision_model.embeddings.patch_embedding"""      )
     if "visual.positional_embedding" in name:
          _UpperCamelCase  :str       =name.replace("""visual.positional_embedding"""			,      """vision_model.embeddings.position_embedding.weight"""      )
     if "visual.ln_pre" in name:
          _UpperCamelCase  :Dict       =name.replace("""visual.ln_pre"""			,      """vision_model.pre_layrnorm"""      )
     if "visual.ln_post" in name:
          _UpperCamelCase  :int       =name.replace("""visual.ln_post"""			,      """vision_model.post_layernorm"""      )
     # projection layers
     if "visual.proj" in name:
          _UpperCamelCase  :Dict       =name.replace("""visual.proj"""			,      """visual_projection.weight"""      )
     if "text_projection" in name:
          _UpperCamelCase  :List[Any]       =name.replace("""text_projection"""			,      """text_projection.weight"""      )
     # decoder
     if "trans_conv" in name:
          _UpperCamelCase  :Tuple       =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  :Any       ="""decoder.""" + name
     if "blocks" in name:
          _UpperCamelCase  :int       =name.replace("""blocks"""			,      """decoder.layers"""      )
     if "linear1" in name:
          _UpperCamelCase  :Optional[int]       =name.replace("""linear1"""			,      """mlp.fc1"""      )
     if "linear2" in name:
          _UpperCamelCase  :Optional[int]       =name.replace("""linear2"""			,      """mlp.fc2"""      )
     if "norm1" in name and "layer_" not in name:
          _UpperCamelCase  :Union[str, Any]       =name.replace("""norm1"""			,      """layer_norm1"""      )
     if "norm2" in name and "layer_" not in name:
          _UpperCamelCase  :List[str]       =name.replace("""norm2"""			,      """layer_norm2"""      )

     return name



def  _lowerCAmelCase				(				__a			,      __a      )  ->      Tuple:
     '''simple docstring'''







     for key in orig_state_dict.copy().keys():
          _UpperCamelCase  :str       =orig_state_dict.pop(__a      )

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

               if "weight" in key:
                    _UpperCamelCase  :Dict       =val[:dim, :]
                    _UpperCamelCase  :Dict       =val[
                        dim : dim * 2, :
                    ]
                    _UpperCamelCase  :Any       =val[-dim:, :]
               else:
                    _UpperCamelCase  :Dict       =val[:dim]
                    _UpperCamelCase  :List[str]       =val[dim : dim * 2]
                    _UpperCamelCase  :str       =val[-dim:]
          elif "self_attn" in key and "out_proj" not in key:
               _UpperCamelCase  :int       =key.split("""."""      )
               _UpperCamelCase  :Optional[int]       =int(key_split[1]      )
               _UpperCamelCase  :str       =config.reduce_dim
               if "weight" in key:
                    _UpperCamelCase  :Union[str, Any]       =val[:dim, :]
                    _UpperCamelCase  :List[Any]       =val[dim : dim * 2, :]
                    _UpperCamelCase  :Tuple       =val[-dim:, :]
               else:
                    _UpperCamelCase  :str       =val[:dim]
                    _UpperCamelCase  :int       =val[dim : dim * 2]
                    _UpperCamelCase  :List[str]       =val[-dim:]
          else:
               _UpperCamelCase  :Union[str, Any]       =rename_key(__a      )
               if "visual_projection" in new_name or "text_projection" in new_name:
                    _UpperCamelCase  :Any       =val.T
               _UpperCamelCase  :Optional[int]       =val

     return orig_state_dict



def  _lowerCAmelCase				(				)  ->      str:
     '''simple docstring'''







     _UpperCamelCase  :Tuple       ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
     _UpperCamelCase  :Dict       =Image.open(requests.get(__a			,      stream=__a      ).raw      )
     return image



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a      )  ->      Dict:
     '''simple docstring'''







     _UpperCamelCase  :str       =get_clipseg_config(__a      )
     _UpperCamelCase  :Dict       =CLIPSegForImageSegmentation(__a      )
     model.eval()

     _UpperCamelCase  :List[str]       =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  :Tuple       =convert_state_dict(__a			,      __a      )
     _UpperCamelCase       ,      _UpperCamelCase  :Optional[int]       =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  :Dict       =ViTImageProcessor(size=3_52      )
     _UpperCamelCase  :Optional[int]       =CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""      )
     _UpperCamelCase  :str       =CLIPSegProcessor(image_processor=__a			,      tokenizer=__a      )

     _UpperCamelCase  :List[Any]       =prepare_img()
     _UpperCamelCase  :List[Any]       =["""a glass""", """something to fill""", """wood""", """a jar"""]

     _UpperCamelCase  :Optional[Any]       =processor(text=__a			,      images=[image] * len(__a      )			,      padding="""max_length"""			,      return_tensors="""pt"""      )

     with torch.no_grad():
          _UpperCamelCase  :List[Any]       =model(**__a      )

     # verify values
     _UpperCamelCase  :List[Any]       =torch.tensor([0.11_10, -0.18_82, 0.16_45]      )
     _UpperCamelCase  :List[Any]       =torch.tensor([0.26_92, -0.71_97, -0.13_28]      )
     if model_name == "clipseg-rd64-refined":
          _UpperCamelCase  :List[str]       =torch.tensor(
              [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]      )
     elif model_name == "clipseg-rd64":
          _UpperCamelCase  :Dict       =torch.tensor(
              [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]      )
     elif model_name == "clipseg-rd16":
          _UpperCamelCase  :Optional[int]       =torch.tensor(
              [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]      )
     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 :				Optional[Any]														= 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 :				int														= parser.parse_args()
      convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
"""simple docstring"""




import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					import torch

					from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				@slow
				@unittest.skip('''Model is not available.'''  )
				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					'''nvidia/megatron-gpt2-345m/'''
									if "MYDIR" in os.environ:
														lowerCamelCase__		:   str  				=					os.path.join(os.environ['''MYDIR''']      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Dict  				=					GPTaLMHeadModel.from_pretrained(__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.half()

									lowerCamelCase__		:   Dict  				=					torch.tensor(
									    [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]      ,device=__UpperCAmelCase      ,dtype=torch.long      ,)

									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(__UpperCAmelCase  ).logits

									lowerCamelCase__		:   List[str]  				=					torch.Size((1, 9, 5_02_57)  )
									self.assertEqual(output.shape      ,__UpperCAmelCase  )

									lowerCamelCase__		:   Tuple  				=					torch.tensor(
									    [
									        4.9_414,
									        -0.2_920,
									        -1.2_148,
									        -4.0_273,
									        -0.5_161,
									        -5.2_109,
									        -1.2_412,
									        -1.8_301,
									        -1.7_734,
									        -4.7_148,
									        -0.2_317,
									        -1.0_811,
									        -2.1_777,
									        0.4_141,
									        -3.7_969,
									        -4.0_586,
									        -2.5_332,
									        -3.3_809,
									        4.3_867,
									    ]      ,device=__UpperCAmelCase      ,dtype=torch.half      ,)

									for i in range(19  ):
														lowerCamelCase__				,						lowerCamelCase__		:   Dict  				=					8 * i // 17, 27_92 * i  # along the diagonal
														lowerCamelCase__				,						lowerCamelCase__		:   Optional[int]  				=					output[0, r, c], expected_diag[i]
														lowerCamelCase__		:   Dict  				=					F"""row={r} col={c} computed={computed} expected={expected}"""
														self.assertAlmostEqual(__UpperCAmelCase      ,__UpperCAmelCase      ,delta=1E-4      ,msg=__UpperCAmelCase  )






