





from math import ceil




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =list(range(0	,     lowercase__	)	)

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

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

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





def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






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

   return dict(zip(lowercase__	,     lowercase__	)	)






'''simple docstring'''
from typing import TYPE_CHECKING

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


lowercase__						       =      {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["EncoderDecoderModel"]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["TFEncoderDecoderModel"]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["FlaxEncoderDecoderModel"]

if TYPE_CHECKING:
						from .configuration_encoder_decoder import EncoderDecoderConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_encoder_decoder import EncoderDecoderModel

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
						import sys

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


