import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase										= logging.get_logger(__name__)



def lowerCamelCase  (       SCREAMING_SNAKE_CASE :    Dict     ):
    '''simple docstring'''







    __UpperCamelCase					:Union[str, Any]        =		SwinConfig.from_pretrained(
        '''microsoft/swin-tiny-patch4-window7-224'''  ,      out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']     )
    __UpperCamelCase					:Optional[Any]        =		MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Any        =		'''huggingface/label-files'''
    if "ade20k-full" in model_name:
        # this should be ok
        __UpperCamelCase					:List[Any]        =		847
        __UpperCamelCase					:str        =		'''maskformer-ade20k-full-id2label.json'''
    elif "ade" in model_name:
        # this should be ok
        __UpperCamelCase					:List[str]        =		150
        __UpperCamelCase					:int        =		'''ade20k-id2label.json'''
    elif "coco-stuff" in model_name:
        # this should be ok
        __UpperCamelCase					:Any        =		171
        __UpperCamelCase					:int        =		'''maskformer-coco-stuff-id2label.json'''
    elif "coco" in model_name:
        # TODO
        __UpperCamelCase					:List[str]        =		133
        __UpperCamelCase					:List[str]        =		'''coco-panoptic-id2label.json'''
    elif "cityscapes" in model_name:
        # this should be ok
        __UpperCamelCase					:Optional[Any]        =		19
        __UpperCamelCase					:Optional[int]        =		'''cityscapes-id2label.json'''
    elif "vistas" in model_name:
        # this should be ok
        __UpperCamelCase					:Dict        =		65
        __UpperCamelCase					:Optional[Any]        =		'''mapillary-vistas-id2label.json'''

    __UpperCamelCase					:List[Any]        =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      repo_type='''dataset'''     )  ,      '''r'''     )     )
    __UpperCamelCase					:List[Any]        =		{int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}

    return config



def lowerCamelCase  (       SCREAMING_SNAKE_CASE :    Tuple     ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		[]
    # stem
    # fmt: off
    rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')     )
    rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')     )
    rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')     )
    rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')     )
    # stages
    for i in range(len(config.backbone_config.depths     )     ):
        for j in range(config.backbone_config.depths[i]     ):
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")     )

        if i < 3:
            rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")     )
            rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")     )
        rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")     )
        rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")     )

    # FPN
    rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')     )
    rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')     )
    rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')     )
    for source_index, target_index in zip(range(3  ,      0  ,      -1     )  ,      range(0  ,      3     )     ):
        rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")     )
        rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")     )
        rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")     )
        rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")     )
        rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")     )
        rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")     )
    rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')     )
    rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')     )

    # Transformer decoder
    for idx in range(config.decoder_config.decoder_layers     ):
        # self-attention out projection
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")     )
        # cross-attention out projection
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")     )
        # MLP 1
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")     )
        # MLP 2
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")     )
        # layernorm 1 (self-attention layernorm)
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")     )
        # layernorm 2 (cross-attention layernorm)
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")     )
        # layernorm 3 (final layernorm)
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")     )

    rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')     )
    rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')     )

    # heads on top
    rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')     )

    rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')     )
    rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')     )

    rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')     )
    rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')     )

    for i in range(3     ):
        rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")     )
        rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")     )
    # fmt: on

    return rename_keys



def lowerCamelCase  (       SCREAMING_SNAKE_CASE :    Any  ,      SCREAMING_SNAKE_CASE :    List[Any]  ,      SCREAMING_SNAKE_CASE :    int     ):
    '''simple docstring'''







    __UpperCamelCase					:Tuple        =		dct.pop(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:str        =		val



def lowerCamelCase  (       SCREAMING_SNAKE_CASE :    List[str]  ,      SCREAMING_SNAKE_CASE :    List[Any]     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		[int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
    for i in range(len(backbone_config.depths     )     ):
        __UpperCamelCase					:Any        =		num_features[i]
        for j in range(backbone_config.depths[i]     ):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            __UpperCamelCase					:str        =		state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
            __UpperCamelCase					:Any        =		state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""     )
            # next, add query, keys and values (in that order) to the state dict
            __UpperCamelCase					:Union[str, Any]        =		in_proj_weight[:dim, :]
            __UpperCamelCase					:Optional[Any]        =		in_proj_bias[: dim]
            __UpperCamelCase					:Union[str, Any]        =		in_proj_weight[
                dim : dim * 2, :
            ]
            __UpperCamelCase					:Any        =		in_proj_bias[
                dim : dim * 2
            ]
            __UpperCamelCase					:Union[str, Any]        =		in_proj_weight[
                -dim :, :
            ]
            __UpperCamelCase					:Union[str, Any]        =		in_proj_bias[-dim :]
            # fmt: on



def lowerCamelCase  (       SCREAMING_SNAKE_CASE :    Union[str, Any]  ,      SCREAMING_SNAKE_CASE :    Union[str, Any]     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		config.decoder_config.hidden_size
    for idx in range(config.decoder_config.decoder_layers     ):
        # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
        __UpperCamelCase					:List[str]        =		state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""     )
        __UpperCamelCase					:Optional[Any]        =		state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""     )
        # next, add query, keys and values (in that order) to the state dict
        __UpperCamelCase					:int        =		in_proj_weight[: hidden_size, :]
        __UpperCamelCase					:List[str]        =		in_proj_bias[:config.hidden_size]
        __UpperCamelCase					:Optional[Any]        =		in_proj_weight[hidden_size : hidden_size * 2, :]
        __UpperCamelCase					:Optional[Any]        =		in_proj_bias[hidden_size : hidden_size * 2]
        __UpperCamelCase					:Tuple        =		in_proj_weight[-hidden_size :, :]
        __UpperCamelCase					:List[Any]        =		in_proj_bias[-hidden_size :]
        # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
        __UpperCamelCase					:int        =		state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""     )
        __UpperCamelCase					:Any        =		state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""     )
        # next, add query, keys and values (in that order) to the state dict
        __UpperCamelCase					:List[str]        =		in_proj_weight[: hidden_size, :]
        __UpperCamelCase					:List[Any]        =		in_proj_bias[:config.hidden_size]
        __UpperCamelCase					:Optional[Any]        =		in_proj_weight[hidden_size : hidden_size * 2, :]
        __UpperCamelCase					:Tuple        =		in_proj_bias[hidden_size : hidden_size * 2]
        __UpperCamelCase					:str        =		in_proj_weight[-hidden_size :, :]
        __UpperCamelCase					:List[str]        =		in_proj_bias[-hidden_size :]
    # fmt: on



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Union[str, Any]        =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCamelCase					:Any        =		Image.open(requests.get(SCREAMING_SNAKE_CASE  ,      stream=SCREAMING_SNAKE_CASE     ).raw     )
    return im



@torch.no_grad()
def lowerCamelCase  (       SCREAMING_SNAKE_CASE :    Any  ,      SCREAMING_SNAKE_CASE :    str  ,      SCREAMING_SNAKE_CASE :    Optional[int]  ,      SCREAMING_SNAKE_CASE :    List[Any] = False     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		get_maskformer_config(SCREAMING_SNAKE_CASE     )

    # load original state_dict
    with open(SCREAMING_SNAKE_CASE  ,      '''rb'''     ) as f:
        __UpperCamelCase					:Optional[int]        =		pickle.load(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Optional[int]        =		data['''model''']

    # for name, param in state_dict.items():
    #     print(name, param.shape)

    # rename keys
    __UpperCamelCase					:Optional[int]        =		create_rename_keys(SCREAMING_SNAKE_CASE     )
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    read_in_swin_q_k_v(SCREAMING_SNAKE_CASE  ,      config.backbone_config     )
    read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

    # update to torch tensors
    for key, value in state_dict.items():
        __UpperCamelCase					:int        =		torch.from_numpy(SCREAMING_SNAKE_CASE     )

    # load 🤗 model
    __UpperCamelCase					:Union[str, Any]        =		MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE     )
    model.eval()

    for name, param in model.named_parameters():
        print(SCREAMING_SNAKE_CASE  ,      param.shape     )

    __UpperCamelCase					:List[str]        =		model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    assert missing_keys == [
        "model.pixel_level_module.encoder.model.layernorm.weight",
        "model.pixel_level_module.encoder.model.layernorm.bias",
    ]
    assert len(SCREAMING_SNAKE_CASE     ) == 0, f"""Unexpected keys: {unexpected_keys}"""

    # verify results
    __UpperCamelCase					:Tuple        =		prepare_img()
    if "vistas" in model_name:
        __UpperCamelCase					:Dict        =		65
    elif "cityscapes" in model_name:
        __UpperCamelCase					:Dict        =		65_535
    else:
        __UpperCamelCase					:Tuple        =		255
    __UpperCamelCase					:str        =		True if '''ade''' in model_name else False
    __UpperCamelCase					:Dict        =		MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE  ,      reduce_labels=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Any        =		image_processor(SCREAMING_SNAKE_CASE  ,      return_tensors='''pt'''     )

    __UpperCamelCase					:List[Any]        =		model(**SCREAMING_SNAKE_CASE     )

    print('''Logits:'''  ,      outputs.class_queries_logits[0, :3, :3]     )

    if model_name == "maskformer-swin-tiny-ade":
        __UpperCamelCase					:List[str]        =		torch.tensor(
            [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]     )
    assert torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,      SCREAMING_SNAKE_CASE  ,      atol=1e-4     )
    print('''Looks ok!'''     )

    if pytorch_dump_folder_path is not None:
        print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
        Path(SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE     )
        model.save_pretrained(SCREAMING_SNAKE_CASE     )
        image_processor.save_pretrained(SCREAMING_SNAKE_CASE     )

    if push_to_hub:
        print('''Pushing model and image processor to the hub...'''     )
        model.push_to_hub(f"""nielsr/{model_name}"""     )
        image_processor.push_to_hub(f"""nielsr/{model_name}"""     )


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''maskformer-swin-tiny-ade''',
        type=str,
        help=('''Name of the MaskFormer model you\'d like to convert''',),
    )
    parser.add_argument(
        '''--checkpoint_path''',
        default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
        type=str,
        help='''Path to the original state dict (.pth file).''',
    )
    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.'''
    )

    __lowercase										= parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )






import os

import pytest

from transformers.dynamic_module_utils import get_imports


__lowercase										= '''
import os
'''

__lowercase										= '''
def foo():
    import os
    return False
'''

__lowercase										= '''
def foo():
    def bar():
        if True:
            import os
        return False
    return bar()
'''

__lowercase										= '''
import os

try:
    import bar
except ImportError:
    raise ValueError()
'''

__lowercase										= '''
import os

def foo():
    try:
        import bar
    except ImportError:
        raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
except (ImportError, AttributeError):
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
except ImportError as e:
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
except:
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
    import baz
except ImportError:
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
    import baz
except ImportError:
    x = 1
    raise ValueError()
'''

__lowercase										= [
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]



@pytest.mark.parametrize('''case'''  ,      SCREAMING_SNAKE_CASE     )
def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		os.path.join(SCREAMING_SNAKE_CASE  ,      '''test_file.py'''     )
    with open(SCREAMING_SNAKE_CASE  ,      '''w'''     ) as _tmp_file:
        _tmp_file.write(SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Dict        =		get_imports(SCREAMING_SNAKE_CASE     )
    assert parsed_imports == ["os"]





