


'''simple docstring'''



import copy

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


__A		=logging.get_logger(__name__)


class 						_snake_case   (       a__		):
					lowerCAmelCase :Optional[int]						=   '''upernet'''




					def __init__( self							,							_lowerCamelCase=None							,							_lowerCamelCase=512							,							_lowerCamelCase=0.02							,							_lowerCamelCase=[1, 2, 3, 6]							,							_lowerCamelCase=True							,							_lowerCamelCase=0.4							,							_lowerCamelCase=384							,							_lowerCamelCase=256							,							_lowerCamelCase=1							,							_lowerCamelCase=False							,							_lowerCamelCase=255							,							**_lowerCamelCase							,							):
								super().__init__(**_lowerCamelCase)

								if backbone_config is None:
											logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""")
											UpperCAmelCase__					:   List[str]       				=       CONFIG_MAPPING["""resnet"""](out_features=["""stage1""", """stage2""", """stage3""", """stage4"""])
								elif isinstance(_lowerCamelCase							,							_lowerCamelCase):
											UpperCAmelCase__					:   Dict       				=       backbone_config.get("""model_type""")
											UpperCAmelCase__					:   Dict       				=       CONFIG_MAPPING[backbone_model_type]
											UpperCAmelCase__					:   List[Any]       				=       config_class.from_dict(_lowerCamelCase)

								UpperCAmelCase__					:   List[Any]       				=       backbone_config
								UpperCAmelCase__					:   Dict       				=       hidden_size
								UpperCAmelCase__					:   List[Any]       				=       initializer_range
								UpperCAmelCase__					:   Optional[Any]       				=       pool_scales
								UpperCAmelCase__					:   Optional[Any]       				=       use_auxiliary_head
								UpperCAmelCase__					:   Optional[Any]       				=       auxiliary_loss_weight
								UpperCAmelCase__					:   List[Any]       				=       auxiliary_in_channels
								UpperCAmelCase__					:   Optional[Any]       				=       auxiliary_channels
								UpperCAmelCase__					:   Optional[int]       				=       auxiliary_num_convs
								UpperCAmelCase__					:   int       				=       auxiliary_concat_input
								UpperCAmelCase__					:   Optional[int]       				=       loss_ignore_index




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Any       				=       copy.deepcopy(self.__dict__)
								UpperCAmelCase__					:   Tuple       				=       self.backbone_config.to_dict()
								UpperCAmelCase__					:   Dict       				=       self.__class__.model_type
								return output



'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
		import tensorflow as tf

		from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM


@require_tf
@require_sentencepiece
@require_tokenizers
class 						_snake_case   (       unittest.TestCase		):




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""")
								UpperCAmelCase__					:   str       				=       AutoTokenizer.from_pretrained("""google/mt5-small""")

								UpperCAmelCase__					:   Optional[Any]       				=       tokenizer("""Hello there"""							,							return_tensors="""tf""").input_ids
								UpperCAmelCase__					:   Tuple       				=       tokenizer("""Hi I am"""							,							return_tensors="""tf""").input_ids

								UpperCAmelCase__					:   List[Any]       				=       model(_lowerCamelCase							,							labels=_lowerCamelCase).loss
								UpperCAmelCase__					:   Tuple       				=       -tf.math.reduce_mean(_lowerCamelCase).numpy()

								UpperCAmelCase__					:   Optional[int]       				=       -21.228168
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)