

"""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 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		List[str]    							=      '''hf-internal-testing/tiny-random-t5'''
						lowerCAmelCase__      :		Optional[Any]    							=      AutoTokenizer.from_pretrained(lowercase_						)
						lowerCAmelCase__      :		int    							=      AutoModelForSeqaSeqLM.from_pretrained(lowercase_						)

						lowerCAmelCase__      :		str    							=      tokenizer('''This is me'''  ,return_tensors='''pt'''						)

						lowerCAmelCase__      :		Any    							=      model.to_bettertransformer()

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

						lowerCAmelCase__      :		Optional[Any]    							=      model.generate(**lowercase_						)

						lowerCAmelCase__      :		Optional[int]    							=      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(lowercase_						)

							lowerCAmelCase__      :		str    							=      AutoModelForSeqaSeqLM.from_pretrained(lowercase_						)

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

							lowerCAmelCase__      :		Dict    							=      model_reloaded.generate(**lowercase_						)
							self.assertTrue(torch.allclose(lowercase_  ,lowercase_						)						)







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		Tuple    							=      '''hf-internal-testing/tiny-random-t5'''
						lowerCAmelCase__      :		Union[str, Any]    							=      AutoModelForSeqaSeqLM.from_pretrained(lowercase_						)

						lowerCAmelCase__      :		int    							=      model.to_bettertransformer()

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

							lowerCAmelCase__      :		List[Any]    							=      model.reverse_bettertransformer()
							model.save_pretrained(lowercase_						)







"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__UpperCamelCase    : Tuple				 =						logging.get_logger(__name__)

__UpperCamelCase    : Optional[int]				 =						{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}







class 							SCREAMING_SNAKE_CASE   (						a_       ,				a_			):






					"""simple docstring"""



					lowercase__												=			"bit"
					lowercase__												=			["preactivation", "bottleneck"]
					lowercase__												=			["SAME", "VALID"]







					def __init__(			self     :							int  ,lowercase_     :							Optional[int]=3  ,lowercase_     :							int=6_4  ,lowercase_     :							Optional[int]=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]  ,lowercase_     :							Union[str, Any]=[3, 4, 6, 3]  ,lowercase_     :							List[Any]="preactivation"  ,lowercase_     :							Dict="relu"  ,lowercase_     :							Dict=None  ,lowercase_     :							Optional[Any]=3_2  ,lowercase_     :							Any=0.0  ,lowercase_     :							Optional[Any]=False  ,lowercase_     :							List[Any]=3_2  ,lowercase_     :							Optional[Any]=1  ,lowercase_     :							Optional[int]=None  ,lowercase_     :							int=None  ,**lowercase_     :							List[str]  ,):
						super().__init__(**lowercase_						)
						if layer_type not in self.layer_types:
							raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
						if global_padding is not None:
							if global_padding.upper() in self.supported_padding:
								lowerCAmelCase__      :		Optional[Any]    							=      global_padding.upper()
							else:
								raise ValueError(F'Padding strategy {global_padding} not supported'						)
						lowerCAmelCase__      :		int    							=      num_channels
						lowerCAmelCase__      :		int    							=      embedding_size
						lowerCAmelCase__      :		Any    							=      hidden_sizes
						lowerCAmelCase__      :		int    							=      depths
						lowerCAmelCase__      :		Union[str, Any]    							=      layer_type
						lowerCAmelCase__      :		Optional[int]    							=      hidden_act
						lowerCAmelCase__      :		int    							=      global_padding
						lowerCAmelCase__      :		List[Any]    							=      num_groups
						lowerCAmelCase__      :		List[Any]    							=      drop_path_rate
						lowerCAmelCase__      :		Union[str, Any]    							=      embedding_dynamic_padding
						lowerCAmelCase__      :		int    							=      output_stride
						lowerCAmelCase__      :		Optional[Any]    							=      width_factor

						lowerCAmelCase__      :		List[str]    							=      ['''stem'''] + [F'stage{idx}' for idx in range(1  ,len(lowercase_						) + 1						)]
						lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      get_aligned_output_features_output_indices(
						    out_features=lowercase_  ,out_indices=lowercase_  ,stage_names=self.stage_names						)




