
import argparse

import requests
import torch
from PIL import Image

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




def 				__magic_name__				(     __lowerCAmelCase			:		Union[str, Any]							)    ->     Any:
				__lowerCamelCase					      =			CLIPSegTextConfig()
				__lowerCamelCase					      =			CLIPSegVisionConfig(patch_size=16							)

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

				__lowerCamelCase					      =			CLIPSegConfig.from_text_vision_configs(
				    __lowerCAmelCase	,						__lowerCAmelCase	,						use_complex_transposed_convolution=__lowerCAmelCase	,						reduce_dim=__lowerCAmelCase	,						)
				return config




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

				return name




def 				__magic_name__				(     __lowerCAmelCase			:		Union[str, Any]	,						__lowerCAmelCase			:		Optional[Any]							)    ->     Union[str, Any]:
				for key in orig_state_dict.copy().keys():
								__lowerCamelCase					      =			orig_state_dict.pop(__lowerCAmelCase							)

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

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

				return orig_state_dict




def 				__magic_name__				(     )    ->     str:
				__lowerCamelCase					      =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowerCamelCase					      =			Image.open(requests.get(__lowerCAmelCase	,						stream=__lowerCAmelCase							).raw							)
				return image




def 				__magic_name__				(     __lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		str	,						__lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		List[str]							)    ->     Union[str, Any]:
				__lowerCamelCase					      =			get_clipseg_config(__lowerCAmelCase							)
				__lowerCamelCase					      =			CLIPSegForImageSegmentation(__lowerCAmelCase							)
				model.eval()

				__lowerCamelCase					      =			torch.load(__lowerCAmelCase	,						map_location='''cpu'''							)

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

    # rename some keys
				__lowerCamelCase					      =			convert_state_dict(__lowerCAmelCase	,						__lowerCAmelCase							)
				__lowerCamelCase      ,					__lowerCamelCase					      =			model.load_state_dict(__lowerCAmelCase	,						strict=__lowerCAmelCase							)

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

				__lowerCamelCase					      =			ViTImageProcessor(size=352							)
				__lowerCamelCase					      =			CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''							)
				__lowerCamelCase					      =			CLIPSegProcessor(image_processor=__lowerCAmelCase	,						tokenizer=__lowerCAmelCase							)

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

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

				with torch.no_grad():
								__lowerCamelCase					      =			model(**__lowerCAmelCase							)

				# verify values
				__lowerCamelCase					      =			torch.tensor([0.1110, -0.1882, 0.1645]							)
				__lowerCamelCase					      =			torch.tensor([0.2692, -0.7197, -0.1328]							)
				if model_name == "clipseg-rd64-refined":
								__lowerCamelCase					      =			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":
								__lowerCamelCase					      =			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":
								__lowerCamelCase					      =			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]	,						__lowerCAmelCase	,						atol=1E-3							)
				assert torch.allclose(outputs.conditional_embeddings[0, :3]	,						__lowerCAmelCase	,						atol=1E-3							)
				assert torch.allclose(outputs.pooled_output[0, :3]	,						__lowerCAmelCase	,						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(__lowerCAmelCase							)
								processor.save_pretrained(__lowerCAmelCase							)

				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__":
					SCREAMING_SNAKE_CASE__     :    int      							=					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."
					)

					SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					parser.parse_args()
					convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch




def 				__magic_name__				(     )    ->     Union[str, Any]:
				__lowerCamelCase					      =			Dataset.from_dict({'''i''': range(17							)}							)
				__lowerCamelCase					      =			len(__lowerCAmelCase							)
				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(__lowerCAmelCase							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 				__magic_name__				(     )    ->     Optional[Any]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert sum(len(list(__lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




@pytest.mark.parametrize('''shards_per_node'''	,						[1, 2, 3]							)
def 				__magic_name__				(     __lowerCAmelCase			:		Any							)    ->     Union[str, Any]:
				def gen(__lowerCAmelCase			:		Union[str, Any]							):
								for shard in shards:
												yield from ({"i": i, "shard": shard} for i in range(17							))

				__lowerCamelCase					      =			3
				__lowerCamelCase					      =			shards_per_node * world_size
				__lowerCamelCase					      =			{'''shards''': [f'''shard_{shard_idx}.txt''' for shard_idx in range(__lowerCAmelCase							)]}
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase	,						gen_kwargs=__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)
				assert full_ds.n_shards == world_size * shards_per_node
				__lowerCamelCase					      =			[
				    split_dataset_by_node(__lowerCAmelCase	,						rank=__lowerCAmelCase	,						world_size=__lowerCAmelCase							) for rank in range(__lowerCAmelCase							)
				]
				assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
				assert sum(len(list(__lowerCAmelCase							)							) for ds in datasets_per_rank							) == full_size
				assert len({tuple(x.values()							) for ds in datasets_per_rank for x in ds}							) == full_size




def 				__magic_name__				(     )    ->     Optional[int]:
				def gen():
								return ({"i": i} for i in range(17							))

				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			IterableDataset.from_generator(__lowerCAmelCase							)
				__lowerCamelCase					      =			len(list(__lowerCAmelCase							)							)

				__lowerCamelCase					      =			split_dataset_by_node(__lowerCAmelCase	,						rank=0	,						world_size=__lowerCAmelCase							).shuffle(seed=42							)
				assert len(list(__lowerCAmelCase							)							) == 1 + full_size // world_size
				with pytest.raises(__lowerCAmelCase							):
								split_dataset_by_node(__lowerCAmelCase	,						rank=0	,						world_size=__lowerCAmelCase							).shuffle()

				__lowerCamelCase					      =			split_dataset_by_node(full_ds.shuffle(seed=42							)	,						rank=0	,						world_size=__lowerCAmelCase							)
				assert len(list(__lowerCAmelCase							)							) == 1 + full_size // world_size
				with pytest.raises(__lowerCAmelCase							):
								split_dataset_by_node(full_ds.shuffle()	,						rank=0	,						world_size=__lowerCAmelCase							)




@pytest.mark.parametrize('''streaming'''	,						[False, True]							)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''	,						reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def 				__magic_name__				(     __lowerCAmelCase			:		List[Any]							)    ->     Optional[int]:
				__lowerCamelCase					      =			2
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)







@pytest.mark.parametrize(
    '''nproc_per_node, num_workers'''	,						[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]	,						)
@require_torch
@pytest.mark.skipif(os.name == '''nt'''	,						reason='''execute_subprocess_async doesn\'t support windows'''							)
@pytest.mark.integration
def 				__magic_name__				(     __lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Any							)    ->     int:
				__lowerCamelCase					      =			True
				__lowerCamelCase					      =			get_torch_dist_unique_port()
				__lowerCamelCase					      =			Path(__lowerCAmelCase							).resolve().parent / '''distributed_scripts''' / '''launch_torch_distributed.py'''
				__lowerCamelCase					      =			f'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
				__lowerCamelCase					      =			f'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
				__lowerCamelCase					      =			[sys.executable] + distributed_args + args
				execute_subprocess_async(__lowerCAmelCase	,						env=os.environ.copy()							)



