






'''simple docstring'''







import os

try:
   from .build_directory_md import good_file_paths
except ImportError:
   from build_directory_md import good_file_paths  # type: ignore

_A			:    Union[str, Any]					  =list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

_A			:    Tuple					  =[file for file in filepaths if file != file.lower()]
if upper_files:
   print(F'{len(upper_files)} files contain uppercase characters:')
   print('''\n'''.join(upper_files) + '''\n''')

_A			:    List[Any]					  =[file for file in filepaths if ''' ''' in file]
if space_files:
   print(F'{len(space_files)} files contain space characters:')
   print('''\n'''.join(space_files) + '''\n''')

_A			:    List[Any]					  =[file for file in filepaths if '''-''' in file]
if hyphen_files:
   print(F'{len(hyphen_files)} files contain hyphen characters:')
   print('''\n'''.join(hyphen_files) + '''\n''')

_A			:    str					  =[file for file in filepaths if os.sep not in file]
if nodir_files:
   print(F'{len(nodir_files)} files are not in a directory:')
   print('''\n'''.join(nodir_files) + '''\n''')

_A			:    Dict					  =len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
   import sys

   sys.exit(bad_files)









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A			:    Any					  ={
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A			:    str					  =[
			    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TrOCRForCausalLM''',
			    '''TrOCRPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
			from .processing_trocr import TrOCRProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
			import sys

			_A			:    Dict					  =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

