


import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
				from transformers import LlamaTokenizerFast
except ImportError as e:
				warnings.warn(e)
				warnings.warn(
				    'The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'
				)
				lowerCAmelCase__			:      Dict								=None
lowerCAmelCase__			:      Union[str, Any]								={
    '7B': 1_10_08,
    '13B': 1_38_24,
    '30B': 1_79_20,
    '65B': 2_20_16,
    '70B': 2_86_72,
}
lowerCAmelCase__			:      List[str]								={
    '7B': 1,
    '7Bf': 1,
    '13B': 2,
    '13Bf': 2,
    '30B': 4,
    '65B': 8,
    '70B': 8,
    '70Bf': 8,
}






def 						a__			(				A__,      A__=1,      A__=2_5_6				):
			return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3				)				) + multiple_of - 1) // multiple_of)






def 						a__			(				A__				):
			with open(A__,      'r'				) as f:
						return json.load(A__				)






def 						a__			(				A__,      A__				):
			with open(A__,      'w'				) as f:
						json.dump(A__,      A__				)






def 						a__			(				A__,      A__,      A__,      A__=True				):
			os.makedirs(A__,      exist_ok=A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					os.path.join(A__,      'tmp'				)
			os.makedirs(A__,      exist_ok=A__				)

			SCREAMING_SNAKE_CASE_		:     Any					=					read_json(os.path.join(A__,      'params.json'				)				)
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					NUM_SHARDS[model_size]
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					params['n_layers']
			SCREAMING_SNAKE_CASE_		:     Any					=					params['n_heads']
			SCREAMING_SNAKE_CASE_		:     Any					=					n_heads // num_shards
			SCREAMING_SNAKE_CASE_		:     int					=					params['dim']
			SCREAMING_SNAKE_CASE_		:     str					=					dim // n_heads
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					1_00_00.0
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					1.0 / (base ** (torch.arange(0,      A__,      2				).float() / dims_per_head))

			if "n_kv_heads" in params:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					params['n_kv_heads']  # for GQA / MQA
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					n_heads_per_shard // num_key_value_heads
						SCREAMING_SNAKE_CASE_		:     Tuple					=					dim // num_key_value_heads
			else:  # compatibility with other checkpoints
						SCREAMING_SNAKE_CASE_		:     Dict					=					n_heads
						SCREAMING_SNAKE_CASE_		:     Tuple					=					n_heads_per_shard
						SCREAMING_SNAKE_CASE_		:     int					=					dim

			# permute for sliced rotary
			def permute(A__,      A__=n_heads,      A__=dim,      A__=dim				):
						return w.view(A__,      dima // n_heads // 2,      2,      A__				).transpose(1,      2				).reshape(A__,      A__				)

			print(F'''Fetching all parameters from the checkpoint at {input_base_path}.'''				)
			# Load weights
			if model_size == "7B":
						# Not sharded
						# (The sharded implementation would also work, but this is simpler.)
						SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.load(os.path.join(A__,      'consolidated.00.pth'				),      map_location='cpu'				)
			else:
						# Sharded
						SCREAMING_SNAKE_CASE_		:     str					=					[
						    torch.load(os.path.join(A__,      F'''consolidated.{i:02d}.pth'''				),      map_location='cpu'				)
						    for i in range(A__				)
						]
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					0
			SCREAMING_SNAKE_CASE_		:     str					=					{'weight_map': {}}
			for layer_i in range(A__				):
						SCREAMING_SNAKE_CASE_		:     Any					=					F'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
						if model_size == "7B":
									# Unsharded
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					{
									    F'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
									        loaded[F'''layers.{layer_i}.attention.wq.weight''']				),
									    F'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
									        loaded[F'''layers.{layer_i}.attention.wk.weight''']				),
									    F'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[F'''layers.{layer_i}.attention.wv.weight'''],
									    F'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[F'''layers.{layer_i}.attention.wo.weight'''],
									    F'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w1.weight'''],
									    F'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w2.weight'''],
									    F'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w3.weight'''],
									    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[F'''layers.{layer_i}.attention_norm.weight'''],
									    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[F'''layers.{layer_i}.ffn_norm.weight'''],
									}
						else:
									# Sharded
									# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
									# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
									# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

									SCREAMING_SNAKE_CASE_		:     Dict					=					{
									    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
									        F'''layers.{layer_i}.attention_norm.weight'''
									    ].clone(),
									    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
									        F'''layers.{layer_i}.ffn_norm.weight'''
									    ].clone(),
									}
									SCREAMING_SNAKE_CASE_		:     int					=					permute(
									    torch.cat(
									        [
									            loaded[i][F'''layers.{layer_i}.attention.wq.weight'''].view(A__,      A__,      A__				)
									            for i in range(A__				)
									        ],      dim=0,      ).reshape(A__,      A__				)				)
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					permute(
									    torch.cat(
									        [
									            loaded[i][F'''layers.{layer_i}.attention.wk.weight'''].view(
									                A__,      A__,      A__				)
									            for i in range(A__				)
									        ],      dim=0,      ).reshape(A__,      A__				),      A__,      A__,      A__,      )
									SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.cat(
									    [
									        loaded[i][F'''layers.{layer_i}.attention.wv.weight'''].view(
									            A__,      A__,      A__				)
									        for i in range(A__				)
									    ],      dim=0,      ).reshape(A__,      A__				)

									SCREAMING_SNAKE_CASE_		:     Dict					=					torch.cat(
									    [loaded[i][F'''layers.{layer_i}.attention.wo.weight'''] for i in range(A__				)],      dim=1				)
									SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.cat(
									    [loaded[i][F'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(A__				)],      dim=0				)
									SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					torch.cat(
									    [loaded[i][F'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(A__				)],      dim=1				)
									SCREAMING_SNAKE_CASE_		:     Any					=					torch.cat(
									    [loaded[i][F'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(A__				)],      dim=0				)

						SCREAMING_SNAKE_CASE_		:     str					=					inv_freq
						for k, v in state_dict.items():
									SCREAMING_SNAKE_CASE_		:     Any					=					filename
									param_count += v.numel()
						torch.save(A__,      os.path.join(A__,      A__				)				)

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					F'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
			if model_size == "7B":
						# Unsharded
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{
						    'model.embed_tokens.weight': loaded['tok_embeddings.weight'],
						    'model.norm.weight': loaded['norm.weight'],
						    'lm_head.weight': loaded['output.weight'],
						}
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{
						    'model.norm.weight': loaded[0]['norm.weight'],
						    'model.embed_tokens.weight': torch.cat(
						        [loaded[i]['tok_embeddings.weight'] for i in range(A__				)],      dim=1				),
						    'lm_head.weight': torch.cat([loaded[i]['output.weight'] for i in range(A__				)],      dim=0				),
						}

			for k, v in state_dict.items():
						SCREAMING_SNAKE_CASE_		:     Tuple					=					filename
						param_count += v.numel()
			torch.save(A__,      os.path.join(A__,      A__				)				)

			# Write configs
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{'total_size': param_count * 2}
			write_json(A__,      os.path.join(A__,      'pytorch_model.bin.index.json'				)				)
			SCREAMING_SNAKE_CASE_		:     str					=					params['ffn_dim_multiplier'] if 'ffn_dim_multiplier' in params else 1
			SCREAMING_SNAKE_CASE_		:     Tuple					=					params['multiple_of'] if 'multiple_of' in params else 2_5_6
			SCREAMING_SNAKE_CASE_		:     List[str]					=					LlamaConfig(
			    hidden_size=A__,      intermediate_size=compute_intermediate_size(A__,      A__,      A__				),      num_attention_heads=params['n_heads'],      num_hidden_layers=params['n_layers'],      rms_norm_eps=params['norm_eps'],      num_key_value_heads=A__,      )
			config.save_pretrained(A__				)

			# Make space so we can load the model properly now.
			del state_dict
			del loaded
			gc.collect()

			print('Loading the checkpoint in a Llama model.'				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					LlamaForCausalLM.from_pretrained(A__,      torch_dtype=torch.floataa,      low_cpu_mem_usage=A__				)
			# Avoid saving this as part of the config.
			del model.config._name_or_path

			print('Saving in the Transformers format.'				)
			model.save_pretrained(A__,      safe_serialization=A__				)
			shutil.rmtree(A__				)






def 						a__			(				A__,      A__				):
			# Initialize the tokenizer based on the `spm` model
			SCREAMING_SNAKE_CASE_		:     List[str]					=					LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
			print(F'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer_class(A__				)
			tokenizer.save_pretrained(A__				)






def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					argparse.ArgumentParser()
			parser.add_argument(
			    '--input_dir',      help='Location of LLaMA weights, which contains tokenizer.model and model folders',      )
			parser.add_argument(
			    '--model_size',      choices=['7B', '7Bf', '13B', '13Bf', '30B', '65B', '70B', '70Bf', 'tokenizer_only'],      )
			parser.add_argument(
			    '--output_dir',      help='Location to write HF model and tokenizer',      )
			parser.add_argument('--safe_serialization',      type=A__,      help='Whether or not to save using `safetensors`.'				)
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					parser.parse_args()
			if args.model_size != "tokenizer_only":
						write_model(
						    model_path=args.output_dir,      input_base_path=os.path.join(args.input_dir,      args.model_size				),      model_size=args.model_size,      safe_serialization=args.safe_serialization,      )
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					os.path.join(args.input_dir,      'tokenizer.model'				)
			write_tokenizer(args.output_dir,      A__				)


if __name__ == "__main__":
				main()









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 	__lowercase    :





											"""simple docstring"""







											@staticmethod
											def 					UpperCamelCase__     (      *lowerCAmelCase__			,						**lowerCAmelCase__       ):





														"""simple docstring"""




														pass







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





							"""simple docstring"""







							_UpperCAmelCase      =						MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					pipeline(
										    'zero-shot-object-detection'			,						model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					object_detector(examples[0]			,						threshold=0.0       )

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






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





										"""simple docstring"""




										pass






							@require_torch
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					pipeline(
										    'zero-shot-object-detection'			,						model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

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

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,						decimals=4       )			,						[
										        {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										        {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										        {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										        {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										        {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										        {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										        {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
										        {'score': 0.642, 'label': 'remote', 'box': {'xmin': 6_7, 'ymin': 2_7_4, 'xmax': 9_3, 'ymax': 2_9_7}},
										        {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
										    ]			,						)

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

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,						decimals=4       )			,						[
										        [
										            {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										            {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										            {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 2_0_4, 'ymin': 1_6_7, 'xmax': 2_3_2, 'ymax': 1_9_0}},
										            {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										            {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										            {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 5_7_1, 'ymin': 8_3, 'xmax': 5_9_8, 'ymax': 1_0_3}},
										            {'score': 0.6_456, 'label': 'remote', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
										            {'score': 0.642, 'label': 'remote', 'box': {'xmin': 6_7, 'ymin': 2_7_4, 'xmax': 9_3, 'ymax': 2_9_7}},
										            {'score': 0.6_419, 'label': 'cat', 'box': {'xmin': 4_9_4, 'ymin': 1_0_5, 'xmax': 5_2_1, 'ymax': 1_2_7}},
										        ]
										    ]			,						)






							@require_torch
							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					pipeline('zero-shot-object-detection'       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					object_detector(
										    'http://images.cocodataset.org/val2017/000000039769.jpg'			,						candidate_labels=['cat', 'remote', 'couch']			,						)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,						decimals=4       )			,						[
										        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
										        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
										        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
										        {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
										        {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
										    ]			,						)

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					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(lowerCAmelCase__			,						decimals=4       )			,						[
										        [
										            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
										            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
										            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
										            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
										            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
										        ],
										        [
										            {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
										            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
										            {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
										            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 3_3_5, 'ymin': 7_4, 'xmax': 3_7_1, 'ymax': 1_8_7}},
										            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_4_2, 'ymax': 4_7_6}},
										        ],
										    ]			,						)






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





										"""simple docstring"""




										pass






							@require_torch
							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					0.2
										SCREAMING_SNAKE_CASE_		:     str					=					pipeline('zero-shot-object-detection'       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					object_detector(
										    'http://images.cocodataset.org/val2017/000000039769.jpg'			,						candidate_labels=['cat', 'remote', 'couch']			,						threshold=lowerCAmelCase__			,						)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,						decimals=4       )			,						[
										        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
										        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
										        {'score': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 5_5, 'xmax': 3_1_5, 'ymax': 4_7_2}},
										    ]			,						)






							@require_torch
							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					2
										SCREAMING_SNAKE_CASE_		:     int					=					pipeline('zero-shot-object-detection'       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					object_detector(
										    'http://images.cocodataset.org/val2017/000000039769.jpg'			,						candidate_labels=['cat', 'remote', 'couch']			,						top_k=lowerCAmelCase__			,						)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__			,						decimals=4       )			,						[
										        {'score': 0.2_868, 'label': 'cat', 'box': {'xmin': 3_2_4, 'ymin': 2_0, 'xmax': 6_4_0, 'ymax': 3_7_3}},
										        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_2, 'xmax': 1_7_7, 'ymax': 1_1_5}},
										    ]			,						)





