
import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_A		     =    object()

# For specifying empty leaf dict `{}`
_A		     =    object()




def  lowerCamelCase__ (  a__     :     List[Any]		,					a__     :     str   )						->		Any:
       UpperCamelCase_											=				tuple((re.compile(x + """$"""   ) for x in qs)   )
       for i in range(len(__lowerCAmelCase   ) - len(__lowerCAmelCase   ) + 1   ):
              UpperCamelCase_											=				[x.match(__lowerCAmelCase   ) for x, y in zip(__lowerCAmelCase		,					ks[i:]   )]
              if matches and all(__lowerCAmelCase   ):
                     return True
       return False




def  lowerCamelCase__ (  a__     :     Any   )						->		Union[str, Any]:
       def replace(a__     :     Tuple		,					a__     :     str   ):
              for rule, replacement in rules:
                     if _match(__lowerCAmelCase		,					__lowerCAmelCase   ):
                            return replacement
              return val

       return replace




def  lowerCamelCase__ (  )						->		Optional[int]:
       return [
           # embeddings
           (("transformer", "wpe", "embedding"), P("""mp"""		,					__lowerCAmelCase   )),
           (("transformer", "wte", "embedding"), P("""mp"""		,					__lowerCAmelCase   )),
           # atention
           (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__lowerCAmelCase		,					"""mp"""   )),
           (("attention", "out_proj", "kernel"), P("""mp"""		,					__lowerCAmelCase   )),
           (("attention", "out_proj", "bias"), None),
           # mlp
           (("mlp", "c_fc", "kernel"), P(__lowerCAmelCase		,					"""mp"""   )),
           (("mlp", "c_fc", "bias"), P("""mp"""   )),
           (("mlp", "c_proj", "kernel"), P("""mp"""		,					__lowerCAmelCase   )),
           (("mlp", "c_proj", "bias"), None),
           # layer norms
           ((r"ln_\d+", "bias"), None),
           ((r"\d+", r"ln_\d+", "scale"), None),
           (("ln_f", "bias"), None),
           (("ln_f", "scale"), None),
       ]







def  lowerCamelCase__ (  a__     :     str   )						->		List[Any]:
       UpperCamelCase_											=				_get_partition_rules()
       UpperCamelCase_											=				_replacement_rules(__lowerCAmelCase   )
       UpperCamelCase_											=				{k: _unmatched for k in flatten_dict(__lowerCAmelCase   )}
       UpperCamelCase_											=				{k: replace(__lowerCAmelCase		,					__lowerCAmelCase   ) for k, v in initd.items()}
       assert _unmatched not in result.values(), "Incomplete partition spec."
       return freeze(unflatten_dict(__lowerCAmelCase   )   )







from typing import TYPE_CHECKING

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


snake_case							:       str     			=      {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Any     			=      [
						    '''RagModel''',
						    '''RagPreTrainedModel''',
						    '''RagSequenceForGeneration''',
						    '''RagTokenForGeneration''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Optional[int]     			=      [
						    '''TFRagModel''',
						    '''TFRagPreTrainedModel''',
						    '''TFRagSequenceForGeneration''',
						    '''TFRagTokenForGeneration''',
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

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




