






'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

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

def 							a	(   _UpperCAmelCase ):






					"""simple docstring"""







					a_									=   CLIPSegTextConfig()
					a_									=   CLIPSegVisionConfig(patch_size=1_6 )

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

					a_									=   CLIPSegConfig.from_text_vision_configs(
					    _UpperCAmelCase					,  _UpperCAmelCase					,  use_complex_transposed_convolution=_UpperCAmelCase					,  reduce_dim=_UpperCAmelCase					,  )
					return config

def 							a	(   _UpperCAmelCase ):






					"""simple docstring"""







					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 							a	(   _UpperCAmelCase					,  _UpperCAmelCase ):






					"""simple docstring"""







					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 							a	(   ):






					"""simple docstring"""







					a_									=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a_									=   Image.open(requests.get(_UpperCAmelCase					,  stream=_UpperCAmelCase ).raw )
					return image

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase ):






					"""simple docstring"""







					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_									=   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_5_2 )
					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__":
						__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)









'''simple docstring'''
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch


class    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					@patch('sys.argv'   , ['fakeprogrampath', 'env']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# test transformers-cli env
										import transformers.commands.transformers_cli

										with CaptureStd() as cs:
															transformers.commands.transformers_cli.main()
										self.assertIn('Python version'   , cs.out     )
										self.assertIn('Platform'   , cs.out     )
										self.assertIn('Using distributed or parallel set-up in script?'   , cs.out     )




					@is_pt_tf_cross_test
					@patch(
					    'sys.argv'   , ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[str]:
										import transformers.commands.transformers_cli

										shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'   , ignore_errors=UpperCAmelCase__     )  # cleans potential past runs
										transformers.commands.transformers_cli.main()

										# The original repo has no TF weights -- if they exist, they were created by the CLI
										self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'     )     )




					@require_torch
					@patch('sys.argv'   , ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	List[Any]:
										import transformers.commands.transformers_cli

										# # remove any previously downloaded model to start clean
										shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'   , ignore_errors=UpperCAmelCase__     )

										# run the command
										transformers.commands.transformers_cli.main()

										# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'     )     )




					@require_torch
					@patch(
					    'sys.argv'   , [
					        'fakeprogrampath',
					        'download',
					        'hf-internal-testing/test_dynamic_model_with_tokenizer',
					        '--trust-remote-code',
					        '--cache-dir',
					        '/tmp',
					    ]   , )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	int:
										import transformers.commands.transformers_cli

										# # remove any previously downloaded model to start clean
										shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'   , ignore_errors=UpperCAmelCase__     )

										# run the command
										transformers.commands.transformers_cli.main()

										# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'     )     )
										self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'     )     )
										self.assertTrue(
										    os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'     )     )

