'''simple docstring'''




import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
  import torch

  from transformers import BlipTextModel
  from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class 	__UpperCAmelCase				:





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=12    ,     _SCREAMING_SNAKE_CASE=7    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=99    ,     _SCREAMING_SNAKE_CASE=32    ,     _SCREAMING_SNAKE_CASE=32    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=4    ,     _SCREAMING_SNAKE_CASE=37    ,     _SCREAMING_SNAKE_CASE=0.1    ,     _SCREAMING_SNAKE_CASE=0.1    ,     _SCREAMING_SNAKE_CASE=512    ,     _SCREAMING_SNAKE_CASE=0.02    ,     _SCREAMING_SNAKE_CASE=0    ,     _SCREAMING_SNAKE_CASE=None    ,     )		->     Optional[Any]:
           A_     =   parent
           A_     =   batch_size
           A_     =   seq_length
           A_     =   is_training
           A_     =   use_input_mask
           A_     =   use_labels
           A_     =   vocab_size
           A_     =   hidden_size
           A_     =   projection_dim
           A_     =   num_hidden_layers
           A_     =   num_attention_heads
           A_     =   intermediate_size
           A_     =   dropout
           A_     =   attention_dropout
           A_     =   max_position_embeddings
           A_     =   initializer_range
           A_     =   scope
           A_     =   bos_token_id

       def 					__A       (      self )		->     Optional[int]:
           A_     =   ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size )

           A_     =   None
           if self.use_input_mask:
               A_     =   random_attention_mask([self.batch_size, self.seq_length] )

           if input_mask is not None:
               A_   ,A_     =   input_mask.shape
               A_     =   np.random.randint(1    ,     seq_length - 1    ,     size=(batch_size,) )
               for batch_idx, start_index in enumerate(_SCREAMING_SNAKE_CASE ):
                   A_     =   1
                   A_     =   0

           A_     =   self.get_config()

           return config, input_ids, input_mask

       def 					__A       (      self )		->     List[str]:
           return BlipTextConfig(
               vocab_size=self.vocab_size    ,     hidden_size=self.hidden_size    ,     projection_dim=self.projection_dim    ,     num_hidden_layers=self.num_hidden_layers    ,     num_attention_heads=self.num_attention_heads    ,     intermediate_size=self.intermediate_size    ,     dropout=self.dropout    ,     attention_dropout=self.attention_dropout    ,     max_position_embeddings=self.max_position_embeddings    ,     initializer_range=self.initializer_range    ,     bos_token_id=self.bos_token_id    ,     )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     List[Any]:
           A_     =   BlipTextModel(config=_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()
           with torch.no_grad():
               A_     =   model(_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE )
               A_     =   model(_SCREAMING_SNAKE_CASE )
           self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size) )
           self.parent.assertEqual(result.pooler_output.shape    ,     (self.batch_size, self.hidden_size) )

       def 					__A       (      self )		->     str:
           A_     =   self.prepare_config_and_inputs()
           A_   ,A_   ,A_     =   config_and_inputs
           A_     =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict




@require_torch
class 	__UpperCAmelCase				(				_UpperCamelCase						,      unittest.TestCase     ):





       '''simple docstring'''



       __lowercase					:					Any    =				(BlipTextModel,) if is_torch_available() else ()
       __lowercase					:					Tuple    =				False
       __lowercase					:					int    =				False
       __lowercase					:					Dict    =				False

       def 					__A       (      self )		->     str:
           A_     =   BlipTextModelTester(self )
           A_     =   ConfigTester(self    ,     config_class=_SCREAMING_SNAKE_CASE    ,     hidden_size=37 )

       def 					__A       (      self )		->     List[Any]:
           self.config_tester.run_common_tests()

       def 					__A       (      self )		->     int:
           A_     =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     List[Any]:
           pass

       def 					__A       (      self )		->     Any:
           pass

       @unittest.skip(reason='''Blip does not use inputs_embeds''' )
       def 					__A       (      self )		->     str:
           pass

       @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
       def 					__A       (      self )		->     Optional[Any]:
           pass

       @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
       def 					__A       (      self )		->     Dict:
           pass

       @slow
       def 					__A       (      self )		->     Any:
           for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               A_     =   BlipTextModel.from_pretrained(_SCREAMING_SNAKE_CASE )
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     Optional[Any]:
           super().test_pt_tf_model_equivalence(allow_missing_keys=_SCREAMING_SNAKE_CASE )







'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

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





def      _UpperCAmelCase    (							_UpperCamelCase    :	Dict   )	->      Union[str, Any]:
    A_     =   CLIPSegTextConfig()
    A_     =   CLIPSegVisionConfig(patch_size=16   )

    A_     =   True if '''refined''' in model_name else False
    A_     =   16 if '''rd16''' in model_name else 64

    A_     =   CLIPSegConfig.from_text_vision_configs(
        _UpperCamelCase,   _UpperCamelCase,   use_complex_transposed_convolution=_UpperCamelCase,   reduce_dim=_UpperCamelCase,   )
    return config





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

    return name





def      _UpperCAmelCase    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	Dict   )	->      Union[str, Any]:
    for key in orig_state_dict.copy().keys():
        A_     =   orig_state_dict.pop(_UpperCamelCase   )

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

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

    return orig_state_dict





def      _UpperCAmelCase    (							)	->      Tuple:
    A_     =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A_     =   Image.open(requests.get(_UpperCamelCase,   stream=_UpperCamelCase   ).raw   )
    return image





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	int,   _UpperCamelCase    :	List[Any],   _UpperCamelCase    :	str   )	->      List[str]:
    A_     =   get_clipseg_config(_UpperCamelCase   )
    A_     =   CLIPSegForImageSegmentation(_UpperCamelCase   )
    model.eval()

    A_     =   torch.load(_UpperCamelCase,   map_location='''cpu'''   )

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

    # rename some keys
    A_     =   convert_state_dict(_UpperCamelCase,   _UpperCamelCase   )
    A_   ,A_     =   model.load_state_dict(_UpperCamelCase,   strict=_UpperCamelCase   )

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

    A_     =   ViTImageProcessor(size=3_52   )
    A_     =   CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''   )
    A_     =   CLIPSegProcessor(image_processor=_UpperCamelCase,   tokenizer=_UpperCamelCase   )

    A_     =   prepare_img()
    A_     =   ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

    A_     =   processor(text=_UpperCamelCase,   images=[image] * len(_UpperCamelCase   ),   padding='''max_length''',   return_tensors='''pt'''   )

    with torch.no_grad():
        A_     =   model(**_UpperCamelCase   )

    # verify values
    A_     =   torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]   )
    A_     =   torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]   )
    if model_name == "clipseg-rd64-refined":
        A_     =   torch.tensor(
            [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]   )
    elif model_name == "clipseg-rd64":
        A_     =   torch.tensor(
            [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]   )
    elif model_name == "clipseg-rd16":
        A_     =   torch.tensor(
            [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]   )
    else:
        raise ValueError(F'''Model name {model_name} not supported.'''   )

    assert torch.allclose(outputs.logits[0, :3, :3],   _UpperCamelCase,   atol=1E-3   )
    assert torch.allclose(outputs.conditional_embeddings[0, :3],   _UpperCamelCase,   atol=1E-3   )
    assert torch.allclose(outputs.pooled_output[0, :3],   _UpperCamelCase,   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(_UpperCamelCase   )
        processor.save_pretrained(_UpperCamelCase   )

    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__":
  __snake_case   :      Dict						    =  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.'
  )

  __snake_case   :      str						    =  parser.parse_args()
  convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






