





from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE					=    TypeVar('''KEY''')
_SCREAMING_SNAKE_CASE					=    TypeVar('''VAL''')






@dataclass(frozen=lowerCAmelCase__ ,      slots=lowerCAmelCase__						)
class 							__lowercase       (      Generic[KEY, VAL]						):







	'''simple docstring'''

	a    :				List[str]											=       42
	a    :				Any											=       42






class 							__lowercase       (      _Item						):







	'''simple docstring'''

	def __init__(self )							->							List[str]:







				'''simple docstring'''





				super().__init__(_lowerCamelCase     ,_lowerCamelCase )


	def __bool__(self )							->							int:







				'''simple docstring'''





				return False





_SCREAMING_SNAKE_CASE					=    _DeletedItem()






class 							__lowercase       (      MutableMapping[KEY, VAL]						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase = 8     ,_lowerCamelCase = 0.7_5 )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							initial_block_size
				__lowercase										=							[None] * initial_block_size
				assert 0.0 < capacity_factor < 1.0
				__lowercase										=							capacity_factor
				__lowercase										=							0


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Optional[int]:







				'''simple docstring'''





				return hash(_lowerCamelCase ) % len(self._buckets )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				return (ind + 1) % len(self._buckets )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							self._buckets[ind]
				if not stored:
							__lowercase										=							_Item(_lowerCamelCase     ,_lowerCamelCase )
							self._len += 1
							return True
				elif stored.key == key:
							__lowercase										=							_Item(_lowerCamelCase     ,_lowerCamelCase )
							return True
				else:
							return False


	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							len(self._buckets ) * self._capacity_factor
				return len(self ) >= int(_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							List[Any]:







				'''simple docstring'''





				if len(self._buckets ) <= self._initial_block_size:
							return False
				__lowercase										=							len(self._buckets ) * self._capacity_factor / 2
				return len(self ) < limit


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				__lowercase										=							self._buckets
				__lowercase										=							[None] * new_size
				__lowercase										=							0
				for item in old_buckets:
							if item:
										self._add_item(item.key     ,item.val )


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				self._resize(len(self._buckets ) * 2 )


	def 						_UpperCAmelCase     (self )							->							str:







				'''simple docstring'''





				self._resize(len(self._buckets ) // 2 )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							self._get_bucket_index(_lowerCamelCase )
				for _ in range(len(self._buckets ) ):
							yield ind
							__lowercase										=							self._get_next_ind(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				for ind in self._iterate_buckets(_lowerCamelCase ):
							if self._try_set(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase ):
										break


	def __setitem__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[str]:







				'''simple docstring'''





				if self._is_full():
							self._size_up()

				self._add_item(_lowerCamelCase     ,_lowerCamelCase )


	def __delitem__(self     ,_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				for ind in self._iterate_buckets(_lowerCamelCase ):
							__lowercase										=							self._buckets[ind]
							if item is None:
										raise KeyError(_lowerCamelCase )
							if item is _deleted:
										continue
							if item.key == key:
										__lowercase										=							_deleted
										self._len -= 1
										break
				if self._is_sparse():
							self._size_down()


	def __getitem__(self     ,_lowerCamelCase )							->							Any:







				'''simple docstring'''





				for ind in self._iterate_buckets(_lowerCamelCase ):
							__lowercase										=							self._buckets[ind]
							if item is None:
										break
							if item is _deleted:
										continue
							if item.key == key:
										return item.val
				raise KeyError(_lowerCamelCase )


	def __len__(self )							->							Dict:







				'''simple docstring'''





				return self._len


	def __iter__(self )							->							Union[str, Any]:







				'''simple docstring'''





				yield from (item.key for item in self._buckets if item)


	def __repr__(self )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							' ,'.join(
				    f"{item.key}: {item.val}" for item in self._buckets if item )
				return f"HashMap({val_string})"












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE					=    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    ['''XGLMTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    ['''XGLMTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''XGLMForCausalLM''',
	    '''XGLMModel''',
	    '''XGLMPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''FlaxXGLMForCausalLM''',
	    '''FlaxXGLMModel''',
	    '''FlaxXGLMPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFXGLMForCausalLM''',
	    '''TFXGLMModel''',
	    '''TFXGLMPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm import XGLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm_fast import XGLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xglm import (
		    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXGLMForCausalLM,
		    TFXGLMModel,
		    TFXGLMPreTrainedModel,
		)


else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)





