import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=1			):
    '''simple docstring'''







    if n_shave_prefix_segments >= 0:
        return ".".join(path.split('''.'''			)[n_shave_prefix_segments:]			)
    else:
        return ".".join(path.split('''.'''			)[:n_shave_prefix_segments]			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=0			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	[]
    for old_item in old_list:
        __UpperCamelCase  :Union[str, Any]    						=	old_item.replace('''in_layers.0'''						,    '''norm1'''			)
        __UpperCamelCase  :Dict    						=	new_item.replace('''in_layers.2'''						,    '''conv1'''			)

        __UpperCamelCase  :Optional[Any]    						=	new_item.replace('''out_layers.0'''						,    '''norm2'''			)
        __UpperCamelCase  :int    						=	new_item.replace('''out_layers.3'''						,    '''conv2'''			)

        __UpperCamelCase  :Dict    						=	new_item.replace('''emb_layers.1'''						,    '''time_emb_proj'''			)
        __UpperCamelCase  :Any    						=	new_item.replace('''skip_connection'''						,    '''conv_shortcut'''			)

        __UpperCamelCase  :List[str]    						=	shave_segments(SCREAMING_SNAKE_CASE						,    n_shave_prefix_segments=SCREAMING_SNAKE_CASE			)

        mapping.append({'''old''': old_item, '''new''': new_item}			)

    return mapping



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=0			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	[]
    for old_item in old_list:
        __UpperCamelCase  :List[Any]    						=	old_item

        __UpperCamelCase  :int    						=	new_item.replace('''norm.weight'''						,    '''group_norm.weight'''			)
        __UpperCamelCase  :Optional[int]    						=	new_item.replace('''norm.bias'''						,    '''group_norm.bias'''			)

        __UpperCamelCase  :List[str]    						=	new_item.replace('''proj_out.weight'''						,    '''proj_attn.weight'''			)
        __UpperCamelCase  :str    						=	new_item.replace('''proj_out.bias'''						,    '''proj_attn.bias'''			)

        __UpperCamelCase  :Dict    						=	shave_segments(SCREAMING_SNAKE_CASE						,    n_shave_prefix_segments=SCREAMING_SNAKE_CASE			)

        mapping.append({'''old''': old_item, '''new''': new_item}			)

    return mapping



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    assert isinstance(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			), "Paths should be a list of dicts containing 'old' and 'new' keys."

    # Splits the attention layers into three variables.
    if attention_paths_to_split is not None:
        for path, path_map in attention_paths_to_split.items():
            __UpperCamelCase  :Tuple    						=	old_checkpoint[path]
            __UpperCamelCase  :Optional[Any]    						=	old_tensor.shape[0] // 3

            __UpperCamelCase  :Optional[int]    						=	(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

            __UpperCamelCase  :Any    						=	old_tensor.shape[0] // config['''num_head_channels'''] // 3

            __UpperCamelCase  :Dict    						=	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]			)
            __UpperCamelCase  :int    						=	old_tensor.split(channels // num_heads						,    dim=1			)

            __UpperCamelCase  :Union[str, Any]    						=	query.reshape(SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Dict    						=	key.reshape(SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :int    						=	value.reshape(SCREAMING_SNAKE_CASE			)

    for path in paths:
        __UpperCamelCase  :int    						=	path['''new''']

        # These have already been assigned
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        # Global renaming happens here
        __UpperCamelCase  :Any    						=	new_path.replace('''middle_block.0'''						,    '''mid_block.resnets.0'''			)
        __UpperCamelCase  :List[Any]    						=	new_path.replace('''middle_block.1'''						,    '''mid_block.attentions.0'''			)
        __UpperCamelCase  :List[Any]    						=	new_path.replace('''middle_block.2'''						,    '''mid_block.resnets.1'''			)

        if additional_replacements is not None:
            for replacement in additional_replacements:
                __UpperCamelCase  :Optional[Any]    						=	new_path.replace(replacement['''old''']						,    replacement['''new''']			)

        # proj_attn.weight has to be converted from conv 1D to linear
        if "proj_attn.weight" in new_path:
            __UpperCamelCase  :str    						=	old_checkpoint[path['''old''']][:, :, 0]
        else:
            __UpperCamelCase  :int    						=	old_checkpoint[path['''old''']]



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







    __UpperCamelCase  :List[str]    						=	{}

    __UpperCamelCase  :Optional[int]    						=	checkpoint['''time_embed.0.weight''']
    __UpperCamelCase  :Union[str, Any]    						=	checkpoint['''time_embed.0.bias''']
    __UpperCamelCase  :str    						=	checkpoint['''time_embed.2.weight''']
    __UpperCamelCase  :List[str]    						=	checkpoint['''time_embed.2.bias''']

    __UpperCamelCase  :Optional[Any]    						=	checkpoint['''input_blocks.0.0.weight''']
    __UpperCamelCase  :Dict    						=	checkpoint['''input_blocks.0.0.bias''']

    __UpperCamelCase  :int    						=	checkpoint['''out.0.weight''']
    __UpperCamelCase  :str    						=	checkpoint['''out.0.bias''']
    __UpperCamelCase  :Optional[int]    						=	checkpoint['''out.2.weight''']
    __UpperCamelCase  :Any    						=	checkpoint['''out.2.bias''']

    # Retrieves the keys for the input blocks only
    __UpperCamelCase  :List[str]    						=	len({'''.'''.join(layer.split('''.'''			)[:2]			) for layer in checkpoint if '''input_blocks''' in layer}			)
    __UpperCamelCase  :str    						=	{
        layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
        for layer_id in range(SCREAMING_SNAKE_CASE			)
    }

    # Retrieves the keys for the middle blocks only
    __UpperCamelCase  :Union[str, Any]    						=	len({'''.'''.join(layer.split('''.'''			)[:2]			) for layer in checkpoint if '''middle_block''' in layer}			)
    __UpperCamelCase  :List[Any]    						=	{
        layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
        for layer_id in range(SCREAMING_SNAKE_CASE			)
    }

    # Retrieves the keys for the output blocks only
    __UpperCamelCase  :Tuple    						=	len({'''.'''.join(layer.split('''.'''			)[:2]			) for layer in checkpoint if '''output_blocks''' in layer}			)
    __UpperCamelCase  :List[str]    						=	{
        layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
        for layer_id in range(SCREAMING_SNAKE_CASE			)
    }

    for i in range(1						,    SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Any    						=	(i - 1) // (config['''num_res_blocks'''] + 1)
        __UpperCamelCase  :Tuple    						=	(i - 1) % (config['''num_res_blocks'''] + 1)

        __UpperCamelCase  :Dict    						=	[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
        __UpperCamelCase  :Tuple    						=	[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

        if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
            __UpperCamelCase  :Optional[Any]    						=	checkpoint[
                f"""input_blocks.{i}.0.op.weight"""
            ]
            __UpperCamelCase  :Tuple    						=	checkpoint[
                f"""input_blocks.{i}.0.op.bias"""
            ]
            continue

        __UpperCamelCase  :List[Any]    						=	renew_resnet_paths(SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Union[str, Any]    						=	{'''old''': f"""input_blocks.{i}.0""", '''new''': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
        __UpperCamelCase  :int    						=	{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
        assign_to_checkpoint(
            SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    additional_replacements=[meta_path, resnet_op]						,    config=SCREAMING_SNAKE_CASE			)

        if len(SCREAMING_SNAKE_CASE			):
            __UpperCamelCase  :Dict    						=	renew_attention_paths(SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Any    						=	{
                '''old''': f"""input_blocks.{i}.1""",
                '''new''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            __UpperCamelCase  :Dict    						=	{
                f"""input_blocks.{i}.1.qkv.bias""": {
                    '''key''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                    '''query''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                    '''value''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                },
                f"""input_blocks.{i}.1.qkv.weight""": {
                    '''key''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                    '''query''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                    '''value''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                },
            }
            assign_to_checkpoint(
                SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    additional_replacements=[meta_path]						,    attention_paths_to_split=SCREAMING_SNAKE_CASE						,    config=SCREAMING_SNAKE_CASE						,    )

    __UpperCamelCase  :Union[str, Any]    						=	middle_blocks[0]
    __UpperCamelCase  :str    						=	middle_blocks[1]
    __UpperCamelCase  :Dict    						=	middle_blocks[2]

    __UpperCamelCase  :List[str]    						=	renew_resnet_paths(SCREAMING_SNAKE_CASE			)
    assign_to_checkpoint(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    config=SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Tuple    						=	renew_resnet_paths(SCREAMING_SNAKE_CASE			)
    assign_to_checkpoint(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    config=SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Optional[int]    						=	renew_attention_paths(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Union[str, Any]    						=	{
        '''middle_block.1.qkv.bias''': {
            '''key''': '''mid_block.attentions.0.key.bias''',
            '''query''': '''mid_block.attentions.0.query.bias''',
            '''value''': '''mid_block.attentions.0.value.bias''',
        },
        '''middle_block.1.qkv.weight''': {
            '''key''': '''mid_block.attentions.0.key.weight''',
            '''query''': '''mid_block.attentions.0.query.weight''',
            '''value''': '''mid_block.attentions.0.value.weight''',
        },
    }
    assign_to_checkpoint(
        SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    attention_paths_to_split=SCREAMING_SNAKE_CASE						,    config=SCREAMING_SNAKE_CASE			)

    for i in range(SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :Optional[Any]    						=	i // (config['''num_res_blocks'''] + 1)
        __UpperCamelCase  :Tuple    						=	i % (config['''num_res_blocks'''] + 1)
        __UpperCamelCase  :Optional[Any]    						=	[shave_segments(SCREAMING_SNAKE_CASE						,    2			) for name in output_blocks[i]]
        __UpperCamelCase  :str    						=	{}

        for layer in output_block_layers:
            __UpperCamelCase  :Optional[int]    						=	layer.split('''.'''			)[0], shave_segments(SCREAMING_SNAKE_CASE						,    1			)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(SCREAMING_SNAKE_CASE			)
            else:
                __UpperCamelCase  :Union[str, Any]    						=	[layer_name]

        if len(SCREAMING_SNAKE_CASE			) > 1:
            __UpperCamelCase  :Union[str, Any]    						=	[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
            __UpperCamelCase  :int    						=	[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

            __UpperCamelCase  :List[Any]    						=	renew_resnet_paths(SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Dict    						=	renew_resnet_paths(SCREAMING_SNAKE_CASE			)

            __UpperCamelCase  :Any    						=	{'''old''': f"""output_blocks.{i}.0""", '''new''': f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
            assign_to_checkpoint(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    additional_replacements=[meta_path]						,    config=SCREAMING_SNAKE_CASE			)

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                __UpperCamelCase  :int    						=	list(output_block_list.values()			).index(['''conv.weight''', '''conv.bias''']			)
                __UpperCamelCase  :List[Any]    						=	checkpoint[
                    f"""output_blocks.{i}.{index}.conv.weight"""
                ]
                __UpperCamelCase  :List[str]    						=	checkpoint[
                    f"""output_blocks.{i}.{index}.conv.bias"""
                ]

                # Clear attentions as they have been attributed above.
                if len(SCREAMING_SNAKE_CASE			) == 2:
                    __UpperCamelCase  :Tuple    						=	[]

            if len(SCREAMING_SNAKE_CASE			):
                __UpperCamelCase  :Tuple    						=	renew_attention_paths(SCREAMING_SNAKE_CASE			)
                __UpperCamelCase  :Tuple    						=	{
                    '''old''': f"""output_blocks.{i}.1""",
                    '''new''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                }
                __UpperCamelCase  :Optional[Any]    						=	{
                    f"""output_blocks.{i}.1.qkv.bias""": {
                        '''key''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                        '''query''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                        '''value''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                    },
                    f"""output_blocks.{i}.1.qkv.weight""": {
                        '''key''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                        '''query''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                        '''value''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                    },
                }
                assign_to_checkpoint(
                    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    additional_replacements=[meta_path]						,    attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions			) else None						,    config=SCREAMING_SNAKE_CASE						,    )
        else:
            __UpperCamelCase  :Dict    						=	renew_resnet_paths(SCREAMING_SNAKE_CASE						,    n_shave_prefix_segments=1			)
            for path in resnet_0_paths:
                __UpperCamelCase  :Union[str, Any]    						=	'''.'''.join(['''output_blocks''', str(SCREAMING_SNAKE_CASE			), path['''old''']]			)
                __UpperCamelCase  :str    						=	'''.'''.join(['''up_blocks''', str(SCREAMING_SNAKE_CASE			), '''resnets''', str(SCREAMING_SNAKE_CASE			), path['''new''']]			)

                __UpperCamelCase  :List[Any]    						=	checkpoint[old_path]

    return new_checkpoint


if __name__ == "__main__":
     __lowercase											=      argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
     )

     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The config json file corresponding to the architecture.''',
     )

     parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

     __lowercase											=      parser.parse_args()

     __lowercase											=      torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          __lowercase											=      json.loads(f.read())

     __lowercase											=      convert_ldm_checkpoint(checkpoint, config)

     if "ldm" in config:
          del config["ldm"]

     __lowercase											=      UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          __lowercase											=      DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
          __lowercase											=      VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

          __lowercase											=      LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)






import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:


    class 			lowerCamelCase_  :






        '''simple docstring'''






        @staticmethod
        def     UpperCamelCase__     (			*__lowercase				,				**__lowercase) ->			Dict:
            pass


@is_pipeline_test
@require_vision
@require_torch
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    a__			:		str      			=   MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase				,				__lowercase) ->			str:
        __UpperCamelCase  :Union[str, Any]    						=	pipeline(
            '''zero-shot-object-detection'''				,				model='''hf-internal-testing/tiny-random-owlvit-object-detection''')

        __UpperCamelCase  :Dict    						=	[
            {
                '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
            }
        ]
        return object_detector, examples







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase) ->			Optional[Any]:
        __UpperCamelCase  :Optional[Any]    						=	object_detector(examples[0]				,				threshold=0.0)

        __UpperCamelCase  :Dict    						=	len(__lowercase)
        self.assertGreater(__lowercase				,				0)
        self.assertEqual(
            __lowercase				,				[
                {
                    '''score''': ANY(__lowercase),
                    '''label''': ANY(__lowercase),
                    '''box''': {'''xmin''': ANY(__lowercase), '''ymin''': ANY(__lowercase), '''xmax''': ANY(__lowercase), '''ymax''': ANY(__lowercase)},
                }
                for i in range(__lowercase)
            ]				,				)







    @require_tf
    @unittest.skip('''Zero Shot Object Detection not implemented in TF''')
    def     UpperCamelCase__     (			self) ->			int:
        pass







    @require_torch
    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Tuple    						=	pipeline(
            '''zero-shot-object-detection'''				,				model='''hf-internal-testing/tiny-random-owlvit-object-detection''')

        __UpperCamelCase  :str    						=	object_detector(
            '''./tests/fixtures/tests_samples/COCO/000000039769.png'''				,				candidate_labels=['''cat''', '''remote''', '''couch''']				,				threshold=0.64				,				)

        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
            ]				,				)

        __UpperCamelCase  :str    						=	object_detector(
            [
                {
                    '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                }
            ]				,				threshold=0.64				,				)

        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                [
                    {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                    {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                    {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                ]
            ]				,				)







    @require_torch
    @slow
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[str]    						=	pipeline('''zero-shot-object-detection''')

        __UpperCamelCase  :List[str]    						=	object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''				,				candidate_labels=['''cat''', '''remote''', '''couch''']				,				)
        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
            ]				,				)

        __UpperCamelCase  :Dict    						=	object_detector(
            [
                {
                    '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                },
                {
                    '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                },
            ]				,				)
        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                [
                    {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                    {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                    {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                    {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                    {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                ],
                [
                    {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                    {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                    {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                    {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                    {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                ],
            ]				,				)







    @require_tf
    @unittest.skip('''Zero Shot Object Detection not implemented in TF''')
    def     UpperCamelCase__     (			self) ->			Dict:
        pass







    @require_torch
    @slow
    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Tuple    						=	0.2
        __UpperCamelCase  :str    						=	pipeline('''zero-shot-object-detection''')

        __UpperCamelCase  :Any    						=	object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''				,				candidate_labels=['''cat''', '''remote''', '''couch''']				,				threshold=__lowercase				,				)
        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
            ]				,				)







    @require_torch
    @slow
    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Optional[int]    						=	2
        __UpperCamelCase  :Union[str, Any]    						=	pipeline('''zero-shot-object-detection''')

        __UpperCamelCase  :Any    						=	object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg'''				,				candidate_labels=['''cat''', '''remote''', '''couch''']				,				top_k=__lowercase				,				)
        self.assertEqual(
            nested_simplify(__lowercase				,				decimals=4)				,				[
                {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
            ]				,				)





