

import argparse

import requests
import torch
from PIL import Image

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


def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Any:






    '''simple docstring'''
    __UpperCAmelCase									=			CLIPSegTextConfig()
    __UpperCAmelCase									=			CLIPSegVisionConfig(patch_size=1_6      )

    __UpperCAmelCase									=			True if '''refined''' in model_name else False
    __UpperCAmelCase									=			1_6 if '''rd16''' in model_name else 6_4

    __UpperCAmelCase									=			CLIPSegConfig.from_text_vision_configs(
        SCREAMING_SNAKE_CASE__       ,   SCREAMING_SNAKE_CASE__       ,   use_complex_transposed_convolution=SCREAMING_SNAKE_CASE__       ,   reduce_dim=SCREAMING_SNAKE_CASE__       ,   )
    return config


def       __a       (   SCREAMING_SNAKE_CASE      )      ->     int:






    '''simple docstring'''


    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       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Any:






    '''simple docstring'''


    for key in orig_state_dict.copy().keys():
        __UpperCAmelCase									=			orig_state_dict.pop(SCREAMING_SNAKE_CASE__      )

        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(SCREAMING_SNAKE_CASE__      )
            if "visual_projection" in new_name or "text_projection" in new_name:
                __UpperCAmelCase									=			val.T
            __UpperCAmelCase									=			val

    return orig_state_dict


def       __a       (   )      ->     int:






    '''simple docstring'''
    __UpperCAmelCase									=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCAmelCase									=			Image.open(requests.get(SCREAMING_SNAKE_CASE__       ,   stream=SCREAMING_SNAKE_CASE__      ).raw      )
    return image


def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     Dict:






    '''simple docstring'''
    __UpperCAmelCase									=			get_clipseg_config(SCREAMING_SNAKE_CASE__      )
    __UpperCAmelCase									=			CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE__      )
    model.eval()

    __UpperCAmelCase									=			torch.load(SCREAMING_SNAKE_CASE__       ,   map_location='''cpu'''      )

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

    # rename some keys
    __UpperCAmelCase									=			convert_state_dict(SCREAMING_SNAKE_CASE__       ,   SCREAMING_SNAKE_CASE__      )
    __UpperCAmelCase									=			model.load_state_dict(SCREAMING_SNAKE_CASE__       ,   strict=SCREAMING_SNAKE_CASE__      )

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

    __UpperCAmelCase									=			ViTImageProcessor(size=3_5_2      )
    __UpperCAmelCase									=			CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''      )
    __UpperCAmelCase									=			CLIPSegProcessor(image_processor=SCREAMING_SNAKE_CASE__       ,   tokenizer=SCREAMING_SNAKE_CASE__      )

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

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

    with torch.no_grad():
        __UpperCAmelCase									=			model(**SCREAMING_SNAKE_CASE__      )

    # 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]       ,   SCREAMING_SNAKE_CASE__       ,   atol=1e-3      )
    assert torch.allclose(outputs.conditional_embeddings[0, :3]       ,   SCREAMING_SNAKE_CASE__       ,   atol=1e-3      )
    assert torch.allclose(outputs.pooled_output[0, :3]       ,   SCREAMING_SNAKE_CASE__       ,   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(SCREAMING_SNAKE_CASE__      )
        processor.save_pretrained(SCREAMING_SNAKE_CASE__      )

    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__":
       A_		:			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.'
       )

       A_		:			int										=   parser.parse_args()
       convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import SwinaSRImageProcessor
       from transformers.image_transforms import get_image_size
class 					A_   (      unittest.TestCase						):


    '''simple docstring'''






    def __init__(self     ,      lowercase__     ,      lowercase__=7     ,      lowercase__=3     ,      lowercase__=18     ,      lowercase__=30     ,      lowercase__=400     ,      lowercase__=True     ,      lowercase__=1 / 255     ,      lowercase__=True     ,      lowercase__=8     ,      )     ->      List[str]:
        __UpperCAmelCase									=			parent
        __UpperCAmelCase									=			batch_size
        __UpperCAmelCase									=			num_channels
        __UpperCAmelCase									=			image_size
        __UpperCAmelCase									=			min_resolution
        __UpperCAmelCase									=			max_resolution
        __UpperCAmelCase									=			do_rescale
        __UpperCAmelCase									=			rescale_factor
        __UpperCAmelCase									=			do_pad
        __UpperCAmelCase									=			pad_size


    def      lowerCAmelCase_						(self  )     ->      int:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }






    def      lowerCAmelCase_						(self     ,      lowercase__=False     ,      lowercase__=False     ,      lowercase__=False  )     ->      Tuple:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
            __UpperCAmelCase									=			[]
            for i in range(self.batch_size  ):
                image_inputs.append(
                    np.random.randint(
                        255     ,      size=(self.num_channels, self.max_resolution, self.max_resolution)     ,      dtype=np.uinta  )  )
        else:
            __UpperCAmelCase									=			[]
            for i in range(self.batch_size  ):
                __UpperCAmelCase					,     __UpperCAmelCase									=			np.random.choice(np.arange(self.min_resolution     ,      self.max_resolution  )     ,      2  )
                image_inputs.append(np.random.randint(255     ,      size=(self.num_channels, width, height)     ,      dtype=np.uinta  )  )

        if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            __UpperCAmelCase									=			[Image.fromarray(np.moveaxis(lowercase__     ,      0     ,      -1  )  ) for x in image_inputs]

        if torchify:
            __UpperCAmelCase									=			[torch.from_numpy(lowercase__  ) for x in image_inputs]

        return image_inputs






@require_torch
@require_vision
class 					A_   (      _a ,     unittest.TestCase						):


    '''simple docstring'''






    a__     							=       SwinaSRImageProcessor if is_vision_available() else None


    def      lowerCAmelCase_						(self  )     ->      str:
        __UpperCAmelCase									=			SwinaSRImageProcessingTester(self  )


    @property
    def      lowerCAmelCase_						(self  )     ->      str:
        return self.image_processor_tester.prepare_image_processor_dict()


    def      lowerCAmelCase_						(self  )     ->      List[Any]:
        __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(lowercase__     ,      '''do_rescale'''  )  )
        self.assertTrue(hasattr(lowercase__     ,      '''rescale_factor'''  )  )
        self.assertTrue(hasattr(lowercase__     ,      '''do_pad'''  )  )
        self.assertTrue(hasattr(lowercase__     ,      '''pad_size'''  )  )


    def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
        pass


    def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
        __UpperCAmelCase					,     __UpperCAmelCase									=			get_image_size(lowercase__  )
        __UpperCAmelCase									=			self.image_processor_tester.pad_size

        __UpperCAmelCase									=			(old_height // size + 1) * size - old_height
        __UpperCAmelCase									=			(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width


    def      lowerCAmelCase_						(self  )     ->      Optional[int]:
        # Initialize image_processor
        __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__  )
        for image in image_inputs:
            self.assertIsInstance(lowercase__     ,      Image.Image  )

        # Test not batched input
        __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(np.array(image_inputs[0]  )  )
        self.assertEqual(
            encoded_images.shape     ,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,      )


    def      lowerCAmelCase_						(self  )     ->      str:
        # Initialize image_processor
        __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,      numpify=lowercase__  )
        for image in image_inputs:
            self.assertIsInstance(lowercase__     ,      np.ndarray  )

        # Test not batched input
        __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape     ,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,      )






    def      lowerCAmelCase_						(self  )     ->      int:
        # Initialize image_processor
        __UpperCAmelCase									=			self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        __UpperCAmelCase									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,      torchify=lowercase__  )
        for image in image_inputs:
            self.assertIsInstance(lowercase__     ,      torch.Tensor  )

        # Test not batched input
        __UpperCAmelCase									=			image_processor(image_inputs[0]     ,      return_tensors='''pt'''  ).pixel_values
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape     ,      (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )     ,      )




