



from math import ceil

def 	lowerCAmelCase__    (				_SCREAMING_SNAKE_CASE							:      Optional[int]					,  _SCREAMING_SNAKE_CASE							:      Tuple     ):
				"""simple docstring"""







				__a	  =		list(range(0					,  __A     )     )

				__a	  =		[item for sublist in list(device_map.values()     ) for item in sublist]

				# Duplicate check
				__a	  =		[]
				for i in device_map_blocks:
								if device_map_blocks.count(__A     ) > 1 and i not in duplicate_blocks:
												duplicate_blocks.append(__A     )
    # Missing blocks
				__a	  =		[i for i in blocks if i not in device_map_blocks]
				__a	  =		[i for i in device_map_blocks if i not in blocks]

				if len(__A     ) != 0:
								raise ValueError(
								    """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
								    """ These attention blocks were specified more than once: """ + str(__A     )     )
				if len(__A     ) != 0:
								raise ValueError(
								    """There are attention blocks for this model that are not specified in the device_map. Add these attention """
								    """blocks to a device on the device_map: """ + str(__A     )     )
				if len(__A     ) != 0:
								raise ValueError(
								    """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
								    + str(__A     )     )

def 	lowerCAmelCase__    (				_SCREAMING_SNAKE_CASE							:      Union[str, Any]					,  _SCREAMING_SNAKE_CASE							:      Dict     ):
				"""simple docstring"""







				__a	  =		list(range(__A     )     )
				__a	  =		int(ceil(n_layers / len(__A     )     )     )
				__a	  =		[layers[i : i + n_blocks] for i in range(0					,  __A					,  __A     )]

				return dict(zip(__A					,  __A     )     )





"""simple docstring"""

from typing import TYPE_CHECKING

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


A_						:    str				=      {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Optional[int]				=      [
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							A_						:    int				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





