



'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case_			:     List[Any]  	=  logging.get_logger(__name__)

snake_case_			:     int  	=  {
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """segformer"""





	def __init__(			self :    List[Any]			,lowerCamelCase__ :    Optional[int]=3			,lowerCamelCase__ :    str=4			,lowerCamelCase__ :    Optional[Any]=[2, 2, 2, 2]			,lowerCamelCase__ :    Any=[8, 4, 2, 1]			,lowerCamelCase__ :    Tuple=[32, 64, 160, 256]			,lowerCamelCase__ :    Optional[int]=[7, 3, 3, 3]			,lowerCamelCase__ :    Union[str, Any]=[4, 2, 2, 2]			,lowerCamelCase__ :    str=[1, 2, 5, 8]			,lowerCamelCase__ :    Union[str, Any]=[4, 4, 4, 4]			,lowerCamelCase__ :    Tuple="gelu"			,lowerCamelCase__ :    str=0.0			,lowerCamelCase__ :    Any=0.0			,lowerCamelCase__ :    List[str]=0.1			,lowerCamelCase__ :    Dict=0.0_2			,lowerCamelCase__ :    List[Any]=0.1			,lowerCamelCase__ :    Tuple=1E-6			,lowerCamelCase__ :    List[str]=256			,lowerCamelCase__ :    Tuple=255			,**lowerCamelCase__ :    Optional[int]			,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__			)

							if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
													warnings.warn(
													    'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
													    ' removed, as the behaviour will default to that of reshape_last_stage = True.'			,lowerCamelCase__			,)

							_UpperCamelCase :		str			    =       num_channels
							_UpperCamelCase :		Tuple			    =       num_encoder_blocks
							_UpperCamelCase :		Tuple			    =       depths
							_UpperCamelCase :		Any			    =       sr_ratios
							_UpperCamelCase :		List[Any]			    =       hidden_sizes
							_UpperCamelCase :		List[str]			    =       patch_sizes
							_UpperCamelCase :		Optional[int]			    =       strides
							_UpperCamelCase :		int			    =       mlp_ratios
							_UpperCamelCase :		List[str]			    =       num_attention_heads
							_UpperCamelCase :		List[Any]			    =       hidden_act
							_UpperCamelCase :		Optional[Any]			    =       hidden_dropout_prob
							_UpperCamelCase :		List[str]			    =       attention_probs_dropout_prob
							_UpperCamelCase :		Tuple			    =       classifier_dropout_prob
							_UpperCamelCase :		List[Any]			    =       initializer_range
							_UpperCamelCase :		Optional[Any]			    =       drop_path_rate
							_UpperCamelCase :		Tuple			    =       layer_norm_eps
							_UpperCamelCase :		Tuple			    =       decoder_hidden_size
							_UpperCamelCase :		Optional[int]			    =       kwargs.get('reshape_last_stage'			,lowerCamelCase__			)
							_UpperCamelCase :		List[str]			    =       semantic_loss_ignore_index






class 							lowercase__     (							lowercase       ):
	lowercase__      =      version.parse("""1.11"""       )





	@property
	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]			)





	@property
	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							return 1E-4





	@property
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return 12







'''simple docstring'''
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
					import torch






@require_torch
@require_optimum
@slow
class 							lowercase__     (							unittest.TestCase       ):





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       'hf-internal-testing/tiny-random-t5'
							_UpperCamelCase :		str			    =       AutoTokenizer.from_pretrained(lowerCamelCase__			)
							_UpperCamelCase :		int			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       tokenizer('This is me'			,return_tensors='pt'			)

							_UpperCamelCase :		str			    =       model.to_bettertransformer()

							self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

							_UpperCamelCase :		Optional[Any]			    =       model.generate(**lowerCamelCase__			)

							_UpperCamelCase :		Union[str, Any]			    =       model.reverse_bettertransformer()

							self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

							with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(lowerCamelCase__			)

													_UpperCamelCase :		List[Any]			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

													self.assertFalse(
													    any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

													_UpperCamelCase :		Optional[Any]			    =       model_reloaded.generate(**lowerCamelCase__			)
													self.assertTrue(torch.allclose(lowerCamelCase__			,lowerCamelCase__			)			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       'hf-internal-testing/tiny-random-t5'
							_UpperCamelCase :		List[Any]			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
													with self.assertRaises(lowerCamelCase__			):
																			model.save_pretrained(lowerCamelCase__			)

													_UpperCamelCase :		str			    =       model.reverse_bettertransformer()
													model.save_pretrained(lowerCamelCase__			)


