

'''simple docstring'''



import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


UpperCAmelCase					:			Optional[Any]       =	{
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

UpperCAmelCase					:			Any       =	logging.get_logger(__name__)







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = """maskformer"""
 lowerCAmelCase__       = {"""hidden_size""": """mask_feature_size"""}
 lowerCAmelCase__       = ["""resnet""", """swin"""]
 lowerCAmelCase__       = ["""detr"""]
 def __init__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any] = 256   ,				__SCREAMING_SNAKE_CASE   :			str = 256   ,				__SCREAMING_SNAKE_CASE   :			List[Any] = 0.1   ,				__SCREAMING_SNAKE_CASE   :			Any = False   ,				__SCREAMING_SNAKE_CASE   :			int = None   ,				__SCREAMING_SNAKE_CASE   :			Any = None   ,				__SCREAMING_SNAKE_CASE   :			Dict = 0.02   ,				__SCREAMING_SNAKE_CASE   :			int = 1.0   ,				__SCREAMING_SNAKE_CASE   :			int = 1.0   ,				__SCREAMING_SNAKE_CASE   :			str = 1.0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any] = 20.0   ,				__SCREAMING_SNAKE_CASE   :			Tuple = None   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]   ,				)    ->					Optional[int]:


      """simple docstring"""




      if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           __SCREAMING_SNAKE_CASE      = SwinConfig(
               image_size=384   ,				in_channels=3   ,				patch_size=4   ,				embed_dim=128   ,				depths=[2, 2, 18, 2]   ,				num_heads=[4, 8, 16, 32]   ,				window_size=12   ,				drop_path_rate=0.3   ,				out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   ,				)

      if isinstance(__UpperCAmelCase   ,				__UpperCAmelCase     ):
           __SCREAMING_SNAKE_CASE      = backbone_config.pop("""model_type"""     )
           __SCREAMING_SNAKE_CASE      = CONFIG_MAPPING[backbone_model_type]
           __SCREAMING_SNAKE_CASE      = config_class.from_dict(__UpperCAmelCase     )

      # 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 MaskFormer. '
               f'Supported model types: {",".join(self.backbones_supported     )}'     )

      if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           __SCREAMING_SNAKE_CASE      = DetrConfig()
      else:
           # verify that the decoder is supported
           __SCREAMING_SNAKE_CASE      = (
               decoder_config.pop("""model_type"""     ) if isinstance(__UpperCAmelCase   ,				__UpperCAmelCase     ) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
                raise ValueError(
                    f'Transformer Decoder {decoder_type} not supported, please use one of'
                    f' {",".join(self.decoders_supported     )}'     )
           if isinstance(__UpperCAmelCase   ,				__UpperCAmelCase     ):
                __SCREAMING_SNAKE_CASE      = CONFIG_MAPPING[decoder_type]
                __SCREAMING_SNAKE_CASE      = config_class.from_dict(__UpperCAmelCase     )

      __SCREAMING_SNAKE_CASE      = backbone_config
      __SCREAMING_SNAKE_CASE      = decoder_config
      # main feature dimension for the model
      __SCREAMING_SNAKE_CASE      = fpn_feature_size
      __SCREAMING_SNAKE_CASE      = mask_feature_size
      # initializer
      __SCREAMING_SNAKE_CASE      = init_std
      __SCREAMING_SNAKE_CASE      = init_xavier_std
      # Hungarian matcher && loss
      __SCREAMING_SNAKE_CASE      = cross_entropy_weight
      __SCREAMING_SNAKE_CASE      = dice_weight
      __SCREAMING_SNAKE_CASE      = mask_weight
      __SCREAMING_SNAKE_CASE      = use_auxiliary_loss
      __SCREAMING_SNAKE_CASE      = no_object_weight
      __SCREAMING_SNAKE_CASE      = output_auxiliary_logits

      __SCREAMING_SNAKE_CASE      = self.decoder_config.encoder_attention_heads
      __SCREAMING_SNAKE_CASE      = self.decoder_config.num_hidden_layers
      super().__init__(**__UpperCAmelCase     )
 @classmethod
 def  UpperCAmelCase__						(						cls   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			str   ,				**__SCREAMING_SNAKE_CASE   :			Dict     )    ->					int:


      """simple docstring"""




      return cls(
          backbone_config=__UpperCAmelCase   ,				decoder_config=__UpperCAmelCase   ,				**__UpperCAmelCase   ,				)


 def  UpperCAmelCase__						(						self   :			int     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = copy.deepcopy(self.__dict__     )
      __SCREAMING_SNAKE_CASE      = self.backbone_config.to_dict()
      __SCREAMING_SNAKE_CASE      = self.decoder_config.to_dict()
      __SCREAMING_SNAKE_CASE      = self.__class__.model_type
      return output













"""simple docstring"""





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_keras_nlp_available():
  from transformers.models.gpta import TFGPTaTokenizer


__A											=						["""gpt2"""]
__A											=						"""gpt2"""

if is_tf_available():






  class    _lowerCAmelCase  (	tf.Module			):
         """simple docstring"""




         def __init__(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           super().__init__()
           lowerCAmelCase__     :List[str]											=				tokenizer
           lowerCAmelCase__     :Optional[int]											=				AutoConfig.from_pretrained(__UpperCAmelCase )
           lowerCAmelCase__     :Optional[Any]											=				TFGPTaLMHeadModel.from_config(__UpperCAmelCase )






         @tf.function(input_signature=(tf.TensorSpec((None,)       ,					tf.string       ,					name='text' ),) )
         def 	snake_case		(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           lowerCAmelCase__     :str											=				self.tokenizer(__UpperCAmelCase )
           lowerCAmelCase__     :int											=				tokenized['input_ids'].to_tensor()

           lowerCAmelCase__     :Optional[Any]											=				tf.cast(input_ids_dense > 0       ,					tf.intaa )
           # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

           lowerCAmelCase__     :int											=				self.model(input_ids=__UpperCAmelCase       ,					attention_mask=__UpperCAmelCase )['logits']

           return outputs






@require_tf
@require_keras_nlp
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         super().setUp()

         lowerCAmelCase__     :List[str]											=				[GPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
         lowerCAmelCase__     :Union[str, Any]											=				[TFGPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS]
         assert len(self.tokenizers ) == len(self.tf_tokenizers )

         lowerCAmelCase__     :int											=				[
             'This is a straightforward English test sentence.',
             'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
             'Now we\'re going to add some Chinese: 一 二 三 一二三',
             'And some much more rare Chinese: 齉 堃 齉堃',
             'Je vais aussi écrire en français pour tester les accents',
             'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
         ]
         lowerCAmelCase__     :str											=				list(zip(self.test_sentences       ,					self.test_sentences[::-1] ) )






       def 	snake_case		(       self ):



         '''simple docstring'''

         for tokenizer, tf_tokenizer in zip(self.tokenizers       ,					self.tf_tokenizers ):
           for test_inputs in self.test_sentences:
             lowerCAmelCase__     :int											=				tokenizer([test_inputs]       ,					return_tensors='tf' )
             lowerCAmelCase__     :Optional[int]											=				tf_tokenizer([test_inputs] )

             for key in python_outputs.keys():
               # convert them to numpy to avoid messing with ragged tensors
               lowerCAmelCase__     :Optional[int]											=				python_outputs[key].numpy()
               lowerCAmelCase__     :List[str]											=				tf_outputs[key].numpy()

               self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
               self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase       ,					tf.intaa ) == tf_outputs_values ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :Optional[int]											=				tf.function(__UpperCAmelCase )
           for test_inputs in self.test_sentences:
             lowerCAmelCase__     :Any											=				tf.constant(__UpperCAmelCase )
             lowerCAmelCase__     :int											=				compiled_tokenizer(__UpperCAmelCase )
             lowerCAmelCase__     :Any											=				tf_tokenizer(__UpperCAmelCase )

             for key in eager_outputs.keys():
               self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :List[str]											=				ModelToSave(tokenizer=__UpperCAmelCase )
           lowerCAmelCase__     :Union[str, Any]											=				tf.convert_to_tensor([self.test_sentences[0]] )
           lowerCAmelCase__     :List[str]											=				model.serving(__UpperCAmelCase )  # Build model with some sample inputs
           with TemporaryDirectory() as tempdir:
             lowerCAmelCase__     :Union[str, Any]											=				Path(__UpperCAmelCase ) / 'saved.model'
             tf.saved_model.save(__UpperCAmelCase       ,					__UpperCAmelCase       ,					signatures={'serving_default': model.serving} )
             lowerCAmelCase__     :str											=				tf.saved_model.load(__UpperCAmelCase )
           lowerCAmelCase__     :Dict											=				loaded_model.signatures['serving_default'](__UpperCAmelCase )['output_0']
           # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
           self.assertTrue(tf.reduce_all(out == loaded_output ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :List[str]											=				tf.convert_to_tensor([self.test_sentences[0]] )
           lowerCAmelCase__     :List[str]											=				tf_tokenizer(__UpperCAmelCase )  # Build model with some sample inputs

           lowerCAmelCase__     :Union[str, Any]											=				tf_tokenizer.get_config()
           lowerCAmelCase__     :Tuple											=				TFGPTaTokenizer.from_config(__UpperCAmelCase )
           lowerCAmelCase__     :Tuple											=				model_from_config(__UpperCAmelCase )

           for key in from_config_output.keys():
             self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           # for the test to run
           lowerCAmelCase__     :int											=				1_2_3_1_2_3

           for max_length in [3, 5, 1_0_2_4]:
             lowerCAmelCase__     :Tuple											=				tf.convert_to_tensor([self.test_sentences[0]] )
             lowerCAmelCase__     :List[str]											=				tf_tokenizer(__UpperCAmelCase       ,					max_length=__UpperCAmelCase )

             lowerCAmelCase__     :Union[str, Any]											=				out['input_ids'].numpy().shape[1]

             assert out_length == max_length
