



'''simple docstring'''






import copy
from typing import Dict, Optional

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


A_				:					str							=      logging.get_logger(__name__)

A_				:					Dict							=      {
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}



class 				lowercase			(    _lowercase							):


 """simple docstring"""


 UpperCAmelCase		      =    """oneformer"""
 UpperCAmelCase		      =    {"""hidden_size""": """hidden_dim"""}







 def __init__(				self    ,a_ = None    ,a_ = 255    ,a_ = 150    ,a_ = 0.1    ,a_ = 2.0    ,a_ = 5.0    ,a_ = 5.0    ,a_ = 0.5    ,a_ = 0.07    ,a_ = 12_544    ,a_ = 3.0    ,a_ = 0.75    ,a_ = 0.02    ,a_ = 1.0    ,a_ = 1E-0_5    ,a_ = False    ,a_ = True    ,a_ = True    ,a_ = [4, 8, 16, 32]    ,a_ = 77    ,a_ = 256    ,a_ = 77    ,a_ = 6    ,a_ = 49_408    ,a_ = 2    ,a_ = 16    ,a_ = 256    ,a_ = 256    ,a_ = 256    ,a_ = 1_024    ,a_ = "GN"    ,a_ = 6    ,a_ = 10    ,a_ = True    ,a_ = 8    ,a_ = 0.1    ,a_ = 2_048    ,a_ = False    ,a_ = False    ,a_ = 2    ,a_ = 4    ,**a_    ,)						->  Tuple:



        if backbone_config is None:
               logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""				)
               _UpperCAmelCase    :		Union[str, Any]     	=					CONFIG_MAPPING["""swin"""](
                   image_size=224    ,in_channels=3    ,patch_size=4    ,embed_dim=96    ,depths=[2, 2, 6, 2]    ,num_heads=[3, 6, 12, 24]    ,window_size=7    ,drop_path_rate=0.3    ,use_absolute_embeddings=snake_case__    ,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]    ,)
        elif isinstance(snake_case__    ,snake_case__				):
               _UpperCAmelCase    :		Dict     	=					backbone_config.get("""model_type"""				)
               _UpperCAmelCase    :		Optional[int]     	=					CONFIG_MAPPING[backbone_model_type]
               _UpperCAmelCase    :		Optional[int]     	=					config_class.from_dict(snake_case__				)

        _UpperCAmelCase    :		Dict     	=					backbone_config

        _UpperCAmelCase    :		Optional[int]     	=					ignore_value
        _UpperCAmelCase    :		Optional[Any]     	=					num_queries
        _UpperCAmelCase    :		List[Any]     	=					no_object_weight
        _UpperCAmelCase    :		List[str]     	=					class_weight
        _UpperCAmelCase    :		List[str]     	=					mask_weight
        _UpperCAmelCase    :		str     	=					dice_weight
        _UpperCAmelCase    :		str     	=					contrastive_weight
        _UpperCAmelCase    :		List[Any]     	=					contrastive_temperature
        _UpperCAmelCase    :		Any     	=					train_num_points
        _UpperCAmelCase    :		List[str]     	=					oversample_ratio
        _UpperCAmelCase    :		List[str]     	=					importance_sample_ratio
        _UpperCAmelCase    :		Union[str, Any]     	=					init_std
        _UpperCAmelCase    :		Tuple     	=					init_xavier_std
        _UpperCAmelCase    :		Union[str, Any]     	=					layer_norm_eps
        _UpperCAmelCase    :		Optional[Any]     	=					is_training
        _UpperCAmelCase    :		Dict     	=					use_auxiliary_loss
        _UpperCAmelCase    :		Optional[Any]     	=					output_auxiliary_logits
        _UpperCAmelCase    :		List[str]     	=					strides
        _UpperCAmelCase    :		Any     	=					task_seq_len
        _UpperCAmelCase    :		str     	=					text_encoder_width
        _UpperCAmelCase    :		Optional[int]     	=					text_encoder_context_length
        _UpperCAmelCase    :		Dict     	=					text_encoder_num_layers
        _UpperCAmelCase    :		Dict     	=					text_encoder_vocab_size
        _UpperCAmelCase    :		int     	=					text_encoder_proj_layers
        _UpperCAmelCase    :		List[str]     	=					text_encoder_n_ctx
        _UpperCAmelCase    :		List[Any]     	=					conv_dim
        _UpperCAmelCase    :		Union[str, Any]     	=					mask_dim
        _UpperCAmelCase    :		Union[str, Any]     	=					hidden_dim
        _UpperCAmelCase    :		Dict     	=					encoder_feedforward_dim
        _UpperCAmelCase    :		Any     	=					norm
        _UpperCAmelCase    :		Any     	=					encoder_layers
        _UpperCAmelCase    :		Any     	=					decoder_layers
        _UpperCAmelCase    :		int     	=					use_task_norm
        _UpperCAmelCase    :		Union[str, Any]     	=					num_attention_heads
        _UpperCAmelCase    :		int     	=					dropout
        _UpperCAmelCase    :		int     	=					dim_feedforward
        _UpperCAmelCase    :		Dict     	=					pre_norm
        _UpperCAmelCase    :		List[str]     	=					enforce_input_proj
        _UpperCAmelCase    :		Tuple     	=					query_dec_layers
        _UpperCAmelCase    :		List[Any]     	=					common_stride
        _UpperCAmelCase    :		int     	=					decoder_layers

        super().__init__(**snake_case__				)







 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase    :		str     	=					copy.deepcopy(self.__dict__				)
        _UpperCAmelCase    :		str     	=					self.backbone_config.to_dict()
        _UpperCAmelCase    :		List[str]     	=					self.__class__.model_type
        return output





"""simple docstring"""
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch




class  _snake_case	(    unittest.TestCase							):






							@patch("""sys.argv"""    ,			["""fakeprogrampath""", """env"""]     )
							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												with CaptureStd() as cs:
																	transformers.commands.transformers_cli.main()
												self.assertIn("""Python version"""    ,			cs.out     )
												self.assertIn("""Platform"""    ,			cs.out     )
												self.assertIn("""Using distributed or parallel set-up in script?"""    ,			cs.out     )






							@is_pt_tf_cross_test
							@patch(
							    """sys.argv"""    ,			["""fakeprogrampath""", """pt-to-tf""", """--model-name""", """hf-internal-testing/tiny-random-gptj""", """--no-pr"""]     )
							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												shutil.rmtree("""/tmp/hf-internal-testing/tiny-random-gptj"""    ,			ignore_errors=snake_case__     )  # cleans potential past runs
												transformers.commands.transformers_cli.main()

												# The original repo has no TF weights -- if they exist, they were created by the CLI
												self.assertTrue(os.path.exists("""/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"""     )     )






							@require_torch
							@patch("""sys.argv"""    ,			["""fakeprogrampath""", """download""", """hf-internal-testing/tiny-random-gptj""", """--cache-dir""", """/tmp"""]     )
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												# # remove any previously downloaded model to start clean
												shutil.rmtree("""/tmp/models--hf-internal-testing--tiny-random-gptj"""    ,			ignore_errors=snake_case__     )

												# run the command
												transformers.commands.transformers_cli.main()

												# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/refs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"""     )     )






							@require_torch
							@patch(
							    """sys.argv"""    ,			[
							        """fakeprogrampath""",
							        """download""",
							        """hf-internal-testing/test_dynamic_model_with_tokenizer""",
							        """--trust-remote-code""",
							        """--cache-dir""",
							        """/tmp""",
							    ]    ,			)
							def 				__lowercase	(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												# # remove any previously downloaded model to start clean
												shutil.rmtree("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"""    ,			ignore_errors=snake_case__     )

												# run the command
												transformers.commands.transformers_cli.main()

												# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"""     )     )
												self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"""     )     )
												self.assertTrue(
												    os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"""     )     )
