





from math import ceil



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	Optional[Any]					=     list(range(0    ,A_))

    UpperCamelCase__:	Any					=     [item for sublist in list(device_map.values()) for item in sublist]

    # Duplicate check
    UpperCamelCase__:	List[str]					=     []
    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
    UpperCamelCase__:	List[str]					=     [i for i in blocks if i not in device_map_blocks]
    UpperCamelCase__:	Optional[int]					=     [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_ (							A_    ,A_):
    UpperCamelCase__:	Optional[Any]					=     list(range(A_))
    UpperCamelCase__:	Any					=     int(ceil(n_layers / len(A_)))
    UpperCamelCase__:	Union[str, Any]					=     [layers[i : i + n_blocks] for i in range(0    ,A_    ,A_)]

    return dict(zip(A_    ,A_))








from typing import TYPE_CHECKING

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


A__:    List[Any]  	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict  	=	[
       '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''OPTForCausalLM''',
       '''OPTModel''',
       '''OPTPreTrainedModel''',
       '''OPTForSequenceClassification''',
       '''OPTForQuestionAnswering''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[int]  	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    str  	=	[
       '''FlaxOPTForCausalLM''',
       '''FlaxOPTModel''',
       '''FlaxOPTPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_opt import (
          OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OPTForCausalLM,
          OPTForQuestionAnswering,
          OPTForSequenceClassification,
          OPTModel,
          OPTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
   import sys

   A__:    Optional[Any]  	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

