"""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 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			"""hf-internal-testing/tiny-random-t5"""
										A__       :     Optional[Any]								=			AutoTokenizer.from_pretrained(snake_case_							)
										A__       :     Any								=			AutoModelForSeqaSeqLM.from_pretrained(snake_case_							)

										A__       :     Any								=			tokenizer("""This is me"""   ,      return_tensors="""pt"""							)

										A__       :     str								=			model.to_bettertransformer()

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

										A__       :     List[str]								=			model.generate(**snake_case_							)

										A__       :     List[str]								=			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(snake_case_							)

																	A__       :     int								=			AutoModelForSeqaSeqLM.from_pretrained(snake_case_							)

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

																	A__       :     int								=			model_reloaded.generate(**snake_case_							)
																	self.assertTrue(torch.allclose(snake_case_   ,      snake_case_							)							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			"""hf-internal-testing/tiny-random-t5"""
										A__       :     List[Any]								=			AutoModelForSeqaSeqLM.from_pretrained(snake_case_							)

										A__       :     Dict								=			model.to_bettertransformer()

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

																	A__       :     Optional[Any]								=			model.reverse_bettertransformer()
																	model.save_pretrained(snake_case_							)






"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'glpn'





			def __init__(			self   ,      snake_case_=3   ,      snake_case_=4   ,      snake_case_=[2, 2, 2, 2]   ,      snake_case_=[8, 4, 2, 1]   ,      snake_case_=[32, 64, 160, 256]   ,      snake_case_=[7, 3, 3, 3]   ,      snake_case_=[4, 2, 2, 2]   ,      snake_case_=[1, 2, 5, 8]   ,      snake_case_=[4, 4, 4, 4]   ,      snake_case_="gelu"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.02   ,      snake_case_=0.1   ,      snake_case_=1E-6   ,      snake_case_=64   ,      snake_case_=10   ,      snake_case_=-1   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)

										A__       :     str								=			num_channels
										A__       :     List[Any]								=			num_encoder_blocks
										A__       :     List[str]								=			depths
										A__       :     int								=			sr_ratios
										A__       :     Optional[Any]								=			hidden_sizes
										A__       :     int								=			patch_sizes
										A__       :     str								=			strides
										A__       :     int								=			mlp_ratios
										A__       :     Optional[int]								=			num_attention_heads
										A__       :     Optional[Any]								=			hidden_act
										A__       :     Optional[Any]								=			hidden_dropout_prob
										A__       :     Optional[Any]								=			attention_probs_dropout_prob
										A__       :     List[str]								=			initializer_range
										A__       :     Dict								=			drop_path_rate
										A__       :     Any								=			layer_norm_eps
										A__       :     Any								=			decoder_hidden_size
										A__       :     Optional[Any]								=			max_depth
										A__       :     List[Any]								=			head_in_index





