

import os
from collections.abc import Iterator

def 						UpperCamelCase					(lowercase_: str = "."					)     ->      Iterator[str]:
	for dir_path, dir_names, filenames in os.walk(lowercase_					):
		A__							:    int        =		[d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
		for filename in filenames:
			if filename == "__init__.py":
				continue
			if os.path.splitext(lowercase_					)[1] in (".py", ".ipynb"):
				yield os.path.join(lowercase_    ,      lowercase_					).lstrip("""./"""					)

def 						UpperCamelCase					(lowercase_: Dict					)     ->      int:
	return f"""{i * '  '}*""" if i else "\n##"

def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: str					)     ->      str:
	A__							:    List[Any]        =		old_path.split(os.sep					)
	for i, new_part in enumerate(new_path.split(os.sep					)					):
		if (i + 1 > len(lowercase_					) or old_parts[i] != new_part) and new_part:
			print(f"""{md_prefix(lowercase_					)} {new_part.replace('_'    ,      ' '					).title()}"""					)
	return new_path

def 						UpperCamelCase					(lowercase_: str = "."					)     ->      None:
	A__							:    Any        =		""""""
	for filepath in sorted(good_file_paths(lowercase_					)					):
		A__   ,   A__							:    Tuple        =		os.path.split(lowercase_					)
		if filepath != old_path:
			A__							:    Tuple        =		print_path(lowercase_    ,      lowercase_					)
		A__							:    Union[str, Any]        =		(filepath.count(os.sep					) + 1) if filepath else 0
		A__							:    str        =		f"""{filepath}/{filename}""".replace(""" """    ,      """%20"""					)
		A__							:    int        =		os.path.splitext(filename.replace("""_"""    ,      """ """					).title()					)[0]
		print(f"""{md_prefix(lowercase_					)} [{filename}]({url})"""					)


if __name__ == "__main__":
	print_directory_md('.')






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class     _a						(__magic_name__		,   __magic_name__		,   __magic_name__				):


					'''simple docstring'''






					@register_to_config
					def __init__( self    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__ = False    ,       ):
						super().__init__()

						A__							:    List[str]        =		nn.Embedding(A__    ,       A__			)

						A__							:    Any        =		nn.Embedding(A__    ,       A__			)
						A__							:    Optional[int]        =		False

						A__							:    Optional[Any]        =		nn.Dropout(p=A__			)

						A__							:    int        =		TaConfig(
						    vocab_size=A__    ,       d_model=A__    ,       num_heads=A__    ,       d_kv=A__    ,       d_ff=A__    ,       dropout_rate=A__    ,       feed_forward_proj=A__    ,       is_decoder=A__    ,       is_encoder_decoder=A__    ,       )

						A__							:    Dict        =		nn.ModuleList()
						for lyr_num in range(A__			):
							A__							:    str        =		TaBlock(A__			)
							self.encoders.append(A__			)

						A__							:    Tuple        =		TaLayerNorm(A__			)
						A__							:    str        =		nn.Dropout(p=A__			)






					def   __A			( self    ,       A__    ,       A__			):
						A__							:    List[str]        =		self.token_embedder(A__			)

						A__							:    Optional[Any]        =		encoder_input_tokens.shape[1]
						A__							:    Optional[int]        =		torch.arange(A__    ,       device=encoder_input_tokens.device			)
						x += self.position_encoding(A__			)

						A__							:    Optional[Any]        =		self.dropout_pre(A__			)

						# inverted the attention mask
						A__							:    Tuple        =		encoder_input_tokens.size()
						A__							:    Union[str, Any]        =		self.get_extended_attention_mask(A__    ,       A__			)

						for lyr in self.encoders:
							A__							:    List[str]        =		lyr(A__    ,       A__			)[0]
						A__							:    List[Any]        =		self.layer_norm(A__			)

						return self.dropout_post(A__			), encoder_inputs_mask



