



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
						from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments



@require_torch
class 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Tuple):
									for model_result in results.values():
											for batch_size, sequence_length in zip(model_result['''bs''']				,				model_result['''ss''']):
													lowerCAmelCase_			:     List[str]									= model_result['''result'''][batch_size][sequence_length]
													self.assertIsNotNone(A_)




							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Any									= '''sshleifer/tiny-gpt2'''
									lowerCAmelCase_			:     Union[str, Any]									= PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				multi_process=A_				,				)
									lowerCAmelCase_			:     Tuple									= PyTorchBenchmark(A_)
									lowerCAmelCase_			:     List[Any]									= benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result)
									self.check_results_dict_not_empty(results.memory_inference_result)




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     int									= '''sgugger/tiny-distilbert-classification'''
									lowerCAmelCase_			:     Tuple									= PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				multi_process=A_				,				only_pretrain_model=A_				,				)
									lowerCAmelCase_			:     Union[str, Any]									= PyTorchBenchmark(A_)
									lowerCAmelCase_			:     Tuple									= benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result)
									self.check_results_dict_not_empty(results.memory_inference_result)




							def    UpperCAmelCase__			(       self     :       Optional[Any]):
									lowerCAmelCase_			:     Optional[Any]									= '''sshleifer/tiny-gpt2'''
									lowerCAmelCase_			:     List[Any]									= PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				torchscript=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				multi_process=A_				,				)
									lowerCAmelCase_			:     int									= PyTorchBenchmark(A_)
									lowerCAmelCase_			:     Dict									= benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result)
									self.check_results_dict_not_empty(results.memory_inference_result)




							@unittest.skipIf(torch_device == '''cpu'''				,				'''Cant do half precision''')
							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Union[str, Any]									= '''sshleifer/tiny-gpt2'''
									lowerCAmelCase_			:     Optional[Any]									= PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				fpaa=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				multi_process=A_				,				)
									lowerCAmelCase_			:     Optional[Any]									= PyTorchBenchmark(A_)
									lowerCAmelCase_			:     Union[str, Any]									= benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result)
									self.check_results_dict_not_empty(results.memory_inference_result)




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     Dict									= '''sshleifer/tiny-gpt2'''
									lowerCAmelCase_			:     Union[str, Any]									= AutoConfig.from_pretrained(A_)
									# set architectures equal to `None`
									lowerCAmelCase_			:     Optional[int]									= None
									lowerCAmelCase_			:     Dict									= PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				multi_process=A_				,				)
									lowerCAmelCase_			:     Any									= PyTorchBenchmark(A_				,				configs=[config])
									lowerCAmelCase_			:     str									= benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result)
									self.check_results_dict_not_empty(results.memory_inference_result)




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     Optional[int]									= '''sshleifer/tiny-gpt2'''
									lowerCAmelCase_			:     List[str]									= PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				multi_process=A_				,				)
									lowerCAmelCase_			:     int									= PyTorchBenchmark(A_)
									lowerCAmelCase_			:     Optional[Any]									= benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result)
									self.check_results_dict_not_empty(results.memory_train_result)




							@unittest.skipIf(torch_device == '''cpu'''				,				'''Can\'t do half precision''')
							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     Dict									= '''sshleifer/tiny-gpt2'''
									lowerCAmelCase_			:     Tuple									= PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				fpaa=A_				,				multi_process=A_				,				)
									lowerCAmelCase_			:     List[str]									= PyTorchBenchmark(A_)
									lowerCAmelCase_			:     Tuple									= benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result)
									self.check_results_dict_not_empty(results.memory_train_result)




							def    UpperCAmelCase__			(       self     :       Any):
									lowerCAmelCase_			:     Tuple									= '''sshleifer/tiny-gpt2'''
									lowerCAmelCase_			:     int									= AutoConfig.from_pretrained(A_)
									lowerCAmelCase_			:     str									= PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				multi_process=A_				,				)
									lowerCAmelCase_			:     str									= PyTorchBenchmark(A_				,				configs=[config])
									lowerCAmelCase_			:     str									= benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result)
									self.check_results_dict_not_empty(results.memory_inference_result)




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     List[Any]									= '''sshleifer/tinier_bart'''
									lowerCAmelCase_			:     int									= AutoConfig.from_pretrained(A_)
									lowerCAmelCase_			:     List[str]									= PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				multi_process=A_				,				)
									lowerCAmelCase_			:     List[Any]									= PyTorchBenchmark(A_				,				configs=[config])
									lowerCAmelCase_			:     Optional[Any]									= benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result)
									self.check_results_dict_not_empty(results.memory_inference_result)




							def    UpperCAmelCase__			(       self     :       Union[str, Any]):
									lowerCAmelCase_			:     List[str]									= '''sshleifer/tiny-gpt2'''
									lowerCAmelCase_			:     Tuple									= AutoConfig.from_pretrained(A_)
									lowerCAmelCase_			:     Any									= PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				multi_process=A_				,				)
									lowerCAmelCase_			:     Optional[Any]									= PyTorchBenchmark(A_				,				configs=[config])
									lowerCAmelCase_			:     Union[str, Any]									= benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result)
									self.check_results_dict_not_empty(results.memory_train_result)




							def    UpperCAmelCase__			(       self     :       Dict):
									lowerCAmelCase_			:     Any									= '''sshleifer/tinier_bart'''
									lowerCAmelCase_			:     int									= AutoConfig.from_pretrained(A_)
									lowerCAmelCase_			:     Union[str, Any]									= PyTorchBenchmarkArguments(
									    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				multi_process=A_				,				)
									lowerCAmelCase_			:     Union[str, Any]									= PyTorchBenchmark(A_				,				configs=[config])
									lowerCAmelCase_			:     Dict									= benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result)
									self.check_results_dict_not_empty(results.memory_train_result)




							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     int									= '''sshleifer/tiny-gpt2'''
									with tempfile.TemporaryDirectory() as tmp_dir:
											lowerCAmelCase_			:     Optional[Any]									= PyTorchBenchmarkArguments(
											    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				save_to_csv=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				inference_time_csv_file=os.path.join(A_				,				'''inf_time.csv''')				,				train_memory_csv_file=os.path.join(A_				,				'''train_mem.csv''')				,				inference_memory_csv_file=os.path.join(A_				,				'''inf_mem.csv''')				,				train_time_csv_file=os.path.join(A_				,				'''train_time.csv''')				,				env_info_csv_file=os.path.join(A_				,				'''env.csv''')				,				multi_process=A_				,				)
											lowerCAmelCase_			:     List[str]									= PyTorchBenchmark(A_)
											benchmark.run()
											self.assertTrue(Path(os.path.join(A_				,				'''inf_time.csv''')).exists())
											self.assertTrue(Path(os.path.join(A_				,				'''train_time.csv''')).exists())
											self.assertTrue(Path(os.path.join(A_				,				'''inf_mem.csv''')).exists())
											self.assertTrue(Path(os.path.join(A_				,				'''train_mem.csv''')).exists())
											self.assertTrue(Path(os.path.join(A_				,				'''env.csv''')).exists())







							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     str									= '''sshleifer/tiny-gpt2'''

									def _check_summary_is_not_empty(A_     :       Optional[Any]):
											self.assertTrue(hasattr(A_				,				'''sequential'''))
											self.assertTrue(hasattr(A_				,				'''cumulative'''))
											self.assertTrue(hasattr(A_				,				'''current'''))
											self.assertTrue(hasattr(A_				,				'''total'''))

									with tempfile.TemporaryDirectory() as tmp_dir:
											lowerCAmelCase_			:     Tuple									= PyTorchBenchmarkArguments(
											    models=[MODEL_ID]				,				training=A_				,				inference=A_				,				sequence_lengths=[8]				,				batch_sizes=[1]				,				log_filename=os.path.join(A_				,				'''log.txt''')				,				log_print=A_				,				trace_memory_line_by_line=A_				,				multi_process=A_				,				)
											lowerCAmelCase_			:     Tuple									= PyTorchBenchmark(A_)
											lowerCAmelCase_			:     Union[str, Any]									= benchmark.run()
											_check_summary_is_not_empty(result.inference_summary)
											_check_summary_is_not_empty(result.train_summary)
											self.assertTrue(Path(os.path.join(A_				,				'''log.txt''')).exists())










import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A__  :  Optional[Any]								=					{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

A__  :  Dict								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''mask2former'''
							_a			      = ['''swin''']
							_a			      = {'''hidden_size''': '''hidden_dim'''}




							def __init__(       self     :       Any				,				A_     :       Optional[Dict] = None				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 1_0_2_4				,				A_     :       str = "relu"				,				A_     :       int = 6				,				A_     :       int = 1_0				,				A_     :       int = 8				,				A_     :       float = 0.0				,				A_     :       int = 2_0_4_8				,				A_     :       bool = False				,				A_     :       bool = False				,				A_     :       int = 4				,				A_     :       int = 2_5_5				,				A_     :       int = 1_0_0				,				A_     :       float = 0.1				,				A_     :       float = 2.0				,				A_     :       float = 5.0				,				A_     :       float = 5.0				,				A_     :       int = 1_2_5_4_4				,				A_     :       float = 3.0				,				A_     :       float = 0.75				,				A_     :       float = 0.02				,				A_     :       float = 1.0				,				A_     :       bool = True				,				A_     :       List[int] = [4, 8, 1_6, 3_2]				,				A_     :       bool = None				,				**A_     :       Dict				,				):
									if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''')
											lowerCAmelCase_			:     int									= CONFIG_MAPPING['''swin'''](
											    image_size=2_2_4				,				in_channels=3				,				patch_size=4				,				embed_dim=9_6				,				depths=[2, 2, 1_8, 2]				,				num_heads=[3, 6, 1_2, 2_4]				,				window_size=7				,				drop_path_rate=0.3				,				use_absolute_embeddings=A_				,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,				)

									if isinstance(A_				,				A_):
											lowerCAmelCase_			:     List[Any]									= backbone_config.pop('''model_type''')
											lowerCAmelCase_			:     Optional[Any]									= CONFIG_MAPPING[backbone_model_type]
											lowerCAmelCase_			:     List[Any]									= config_class.from_dict(A_)

									# verify that the backbone is supported
									if backbone_config.model_type not in self.backbones_supported:
											logger.warning_once(
											    F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
											    F"""Supported model types: {",".join(self.backbones_supported)}""")

									lowerCAmelCase_			:     List[Any]									= backbone_config
									lowerCAmelCase_			:     str									= feature_size
									lowerCAmelCase_			:     Optional[Any]									= mask_feature_size
									lowerCAmelCase_			:     int									= hidden_dim
									lowerCAmelCase_			:     int									= encoder_feedforward_dim
									lowerCAmelCase_			:     Optional[int]									= activation_function
									lowerCAmelCase_			:     Any									= encoder_layers
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers
									lowerCAmelCase_			:     Optional[Any]									= num_attention_heads
									lowerCAmelCase_			:     Optional[int]									= dropout
									lowerCAmelCase_			:     List[str]									= dim_feedforward
									lowerCAmelCase_			:     Optional[Any]									= pre_norm
									lowerCAmelCase_			:     List[str]									= enforce_input_projection
									lowerCAmelCase_			:     Tuple									= common_stride
									lowerCAmelCase_			:     Optional[Any]									= ignore_value
									lowerCAmelCase_			:     Optional[Any]									= num_queries
									lowerCAmelCase_			:     int									= no_object_weight
									lowerCAmelCase_			:     Tuple									= class_weight
									lowerCAmelCase_			:     int									= mask_weight
									lowerCAmelCase_			:     Dict									= dice_weight
									lowerCAmelCase_			:     str									= train_num_points
									lowerCAmelCase_			:     Dict									= oversample_ratio
									lowerCAmelCase_			:     Tuple									= importance_sample_ratio
									lowerCAmelCase_			:     List[str]									= init_std
									lowerCAmelCase_			:     List[str]									= init_xavier_std
									lowerCAmelCase_			:     Optional[Any]									= use_auxiliary_loss
									lowerCAmelCase_			:     List[Any]									= feature_strides
									lowerCAmelCase_			:     int									= output_auxiliary_logits
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers

									super().__init__(**A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       List[str]				,				A_     :       PretrainedConfig				,				**A_     :       List[Any]):
									return cls(
									    backbone_config=A_				,				**A_				,				)







							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Dict									= self.backbone_config.to_dict()
									lowerCAmelCase_			:     Optional[int]									= self.__class__.model_type
									return output





