






'''simple docstring'''
import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
snake_case_			:     List[str]  	=  object()

# For specifying empty leaf dict `{}`
snake_case_			:     Optional[Any]  	=  object()






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		Any			    =       tuple((re.compile(x + '$'							) for x in qs)							)
						for i in range(len(__lowerCamelCase							) - len(__lowerCamelCase							) + 1							):
												_UpperCamelCase :		List[str]			    =       [x.match(__lowerCamelCase							) for x, y in zip(__lowerCamelCase  ,  ks[i:]							)]
												if matches and all(__lowerCamelCase							):
																		return True
						return False






def A__   (					UpperCAmelCase_							):
						def replace(UpperCAmelCase_  ,  UpperCAmelCase_							):
												for rule, replacement in rules:
																		if _match(__lowerCamelCase  ,  __lowerCamelCase							):
																								return replacement
												return val

						return replace






def A__   (					):
						return [
						    # embeddings
						    (("transformer", "wpe", "embedding"), P('mp'  ,  __lowerCamelCase							)),
						    (("transformer", "wte", "embedding"), P('mp'  ,  __lowerCamelCase							)),
						    # atention
						    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__lowerCamelCase  ,  'mp'							)),
						    (("attention", "out_proj", "kernel"), P('mp'  ,  __lowerCamelCase							)),
						    (("attention", "out_proj", "bias"), None),
						    # mlp
						    (("mlp", "c_fc", "kernel"), P(__lowerCamelCase  ,  'mp'							)),
						    (("mlp", "c_fc", "bias"), P('mp'							)),
						    (("mlp", "c_proj", "kernel"), P('mp'  ,  __lowerCamelCase							)),
						    (("mlp", "c_proj", "bias"), None),
						    # layer norms
						    ((r"ln_\d+", "bias"), None),
						    ((r"\d+", r"ln_\d+", "scale"), None),
						    (("ln_f", "bias"), None),
						    (("ln_f", "scale"), None),
						]






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		List[str]			    =       _get_partition_rules()
						_UpperCamelCase :		Any			    =       _replacement_rules(__lowerCamelCase							)
						_UpperCamelCase :		Any			    =       {k: _unmatched for k in flatten_dict(__lowerCamelCase							)}
						_UpperCamelCase :		Dict			    =       {k: replace(__lowerCamelCase  ,  __lowerCamelCase							) for k, v in initd.items()}
						assert _unmatched not in result.values(), "Incomplete partition spec."
						return freeze(unflatten_dict(__lowerCamelCase							)							)







'''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__			)


