




'''simple docstring'''

from math import ceil


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   List[Any]:

					'''simple docstring'''

					snake_case_		     =							list(range(0, lowerCAmelCase__      )      )

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

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

					if len(lowerCAmelCase__      ) != 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(lowerCAmelCase__      )      )
					if len(lowerCAmelCase__      ) != 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(lowerCAmelCase__      )      )
					if len(lowerCAmelCase__      ) != 0:
										raise ValueError(
										    '''The device_map contains more attention blocks than this model has. Remove these from the device_map:'''
										    + str(lowerCAmelCase__      )      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

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

					return dict(zip(lowerCAmelCase__, lowerCAmelCase__      )      )



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A_    							=					{
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """GPTNeoForCausalLM""",
						    """GPTNeoForQuestionAnswering""",
						    """GPTNeoForSequenceClassification""",
						    """GPTNeoForTokenClassification""",
						    """GPTNeoModel""",
						    """GPTNeoPreTrainedModel""",
						    """load_tf_weights_in_gpt_neo""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """FlaxGPTNeoForCausalLM""",
						    """FlaxGPTNeoModel""",
						    """FlaxGPTNeoPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_neo import (
												    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTNeoForCausalLM,
												    GPTNeoForQuestionAnswering,
												    GPTNeoForSequenceClassification,
												    GPTNeoForTokenClassification,
												    GPTNeoModel,
												    GPTNeoPreTrainedModel,
												    load_tf_weights_in_gpt_neo,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



