





'''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,
)


lowercase__		:     Tuple			=							{'configuration_xglm': ['XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XGLMConfig']}

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

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

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


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


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__		:     List[str]			=							[
       '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

   lowercase__		:     Optional[Any]			=							_LazyModule(__name__, globals()['__file__'], _import_structure)






"""simple docstring"""




def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   len(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   len(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   (
			    first_str_length if first_str_length > second_str_length else second_str_length
			)
			SCREAMING_SNAKE_CASE__										=   []
			for char_count in range(snake_case__     ):
						if char_count < first_str_length:
									output_list.append(first_str[char_count]     )
						if char_count < second_str_length:
									output_list.append(second_str[char_count]     )
			return "".join(snake_case__     )


if __name__ == "__main__":
						print(alternative_string_arrange("AB", "XYZ"), end=" ")


