



"""simple docstring"""
import copy
from typing import Dict, List, Optional

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


_lowerCAmelCase    :Dict     =					{
    '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
}

_lowerCAmelCase    :Union[str, Any]     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''mask2former'''
     a__									=['''swin''']
     a__									={'''hidden_size''': '''hidden_dim'''}





     def __init__(					self       ,	A = None       ,	A = 2_5_6       ,	A = 2_5_6       ,	A = 2_5_6       ,	A = 1_0_2_4       ,	A = "relu"       ,	A = 6       ,	A = 1_0       ,	A = 8       ,	A = 0.0       ,	A = 2_0_4_8       ,	A = False       ,	A = False       ,	A = 4       ,	A = 2_5_5       ,	A = 1_0_0       ,	A = 0.1       ,	A = 2.0       ,	A = 5.0       ,	A = 5.0       ,	A = 1_2_5_4_4       ,	A = 3.0       ,	A = 0.75       ,	A = 0.02       ,	A = 1.0       ,	A = True       ,	A = [4, 8, 1_6, 3_2]       ,	A = None       ,	**A       ,	)							->					str:
         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''		)
             _UpperCAmelCase							:  List[Any]											=    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		):
             _UpperCAmelCase							:  int											=    backbone_config.pop('''model_type'''		)
             _UpperCAmelCase							:  Optional[Any]											=    CONFIG_MAPPING[backbone_model_type]
             _UpperCAmelCase							:  int											=    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		)}'		)

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

         super().__init__(**A		)





     @classmethod
     def  __lowerCAmelCase	(					cls       ,	A       ,	**A		)							->					List[str]:
         return cls(
             backbone_config=A       ,	**A       ,	)





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







"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Dict											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[str]											=    -1

         _UpperCAmelCase							:  List[str]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[str]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  List[Any]											=    tokenizer.decode(greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  str											=    TextStreamer(A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  List[str]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[str]											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  List[Any]											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    -1

         _UpperCAmelCase							:  Union[str, Any]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  str											=    tokenizer.decode(greedy_ids[0]		)

         _UpperCAmelCase							:  Union[str, Any]											=    TextIteratorStreamer(A		)
         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Any											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()
         _UpperCAmelCase							:  Any											=    ''''''
         for new_text in streamer:
             streamer_text += new_text

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  str											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Any											=    -1

         _UpperCAmelCase							:  Dict											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  Dict											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  Dict											=    greedy_ids[:, input_ids.shape[1] :]
         _UpperCAmelCase							:  List[str]											=    tokenizer.decode(new_greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Any											=    TextStreamer(A       ,	skip_prompt=A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  Union[str, Any]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
         # with actual models -- the dummy models' tokenizers are not aligned with their models, and
         # `skip_special_tokens=True` has no effect on them
         _UpperCAmelCase							:  int											=    AutoTokenizer.from_pretrained('''distilgpt2'''		)
         _UpperCAmelCase							:  Union[str, Any]											=    AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(A		)
         _UpperCAmelCase							:  Tuple											=    -1

         _UpperCAmelCase							:  int											=    torch.ones((1, 5)       ,	device=A		).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Optional[Any]											=    TextStreamer(A       ,	skip_special_tokens=A		)
             model.generate(A       ,	max_new_tokens=1       ,	do_sample=A       ,	streamer=A		)

         # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
         # re-tokenized, must only contain one token
         _UpperCAmelCase							:  Tuple											=    cs.out[:-1]  # Remove the final "\n"
         _UpperCAmelCase							:  int											=    tokenizer(A       ,	return_tensors='''pt'''		)
         self.assertEqual(streamer_text_tokenized.input_ids.shape       ,	(1, 1)		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Any											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Dict											=    -1

         _UpperCAmelCase							:  str											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    TextIteratorStreamer(A       ,	timeout=0.001		)
         _UpperCAmelCase							:  Union[str, Any]											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Optional[Any]											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()

         # The streamer will timeout after 0.001 seconds, so an exception will be raised
         with self.assertRaises(A		):
             _UpperCAmelCase							:  Optional[Any]											=    ''''''
             for new_text in streamer:
                 streamer_text += new_text


