


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCAmelCase_      : Optional[int]  					=       logging.get_logger(__name__)







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		int:

       """simple docstring"""

       a_					:		Tuple							=  R'\w+[.]\d+'
       a_					:		List[Any]							=  re.findall(__A  ,    __A				)
       for pat in pats:
              a_					:		Union[str, Any]							=  key.replace(__A  ,    '_'.join(pat.split('.'				)				)				)
       return key







def        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			Optional[Any]  ,    __A :			Optional[Any]				)	->		str:

       """simple docstring"""

       a_					:		Dict							=  pt_tuple_key[:-1] + ('scale',)
       if (
           any('norm' in str_ for str_ in pt_tuple_key				)
           and (pt_tuple_key[-1] == "bias")
           and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
           and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
       ):
              a_					:		Any							=  pt_tuple_key[:-1] + ('scale',)
              return renamed_pt_tuple_key, pt_tensor
       elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
              a_					:		Tuple							=  pt_tuple_key[:-1] + ('scale',)
              return renamed_pt_tuple_key, pt_tensor

       # embedding
       if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
              a_					:		List[Any]							=  pt_tuple_key[:-1] + ('embedding',)
              return renamed_pt_tuple_key, pt_tensor

       # conv layer
       a_					:		int							=  pt_tuple_key[:-1] + ('kernel',)
       if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
              a_					:		Union[str, Any]							=  pt_tensor.transpose(2  ,    3  ,    1  ,    0				)
              return renamed_pt_tuple_key, pt_tensor

       # linear layer
       a_					:		Any							=  pt_tuple_key[:-1] + ('kernel',)
       if pt_tuple_key[-1] == "weight":
              a_					:		Union[str, Any]							=  pt_tensor.T
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm weight
       a_					:		Optional[Any]							=  pt_tuple_key[:-1] + ('weight',)
       if pt_tuple_key[-1] == "gamma":
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm bias
       a_					:		List[str]							=  pt_tuple_key[:-1] + ('bias',)
       if pt_tuple_key[-1] == "beta":
              return renamed_pt_tuple_key, pt_tensor

       return pt_tuple_key, pt_tensor







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]  ,    __A :			Dict  ,    __A :			Any=42				)	->		List[Any]:

       """simple docstring"""

       a_					:		str							=  {k: v.numpy() for k, v in pt_state_dict.items()}

       # Step 2: Since the model is stateless, get random Flax params
       a_					:		int							=  flax_model.init_weights(PRNGKey(__A				)				)

       a_					:		Tuple							=  flatten_dict(__A				)
       a_					:		Dict							=  {}

       # Need to change some parameters name to match Flax names
       for pt_key, pt_tensor in pt_state_dict.items():
              a_					:		Optional[int]							=  rename_key(__A				)
              a_					:		List[Any]							=  tuple(renamed_pt_key.split('.'				)				)

              # Correctly rename weight parameters
              a_							,		a_					:		Optional[Any]							=  rename_key_and_reshape_tensor(__A  ,    __A  ,    __A				)

              if flax_key in random_flax_state_dict:
                     if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                            raise ValueError(
                                F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                                F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""				)

        # also add unexpected weight so that warning is thrown
              a_					:		Optional[int]							=  jnp.asarray(__A				)

       return unflatten_dict(__A				)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE       :      List[str]												= {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= ["Speech2TextTokenizer"]

try:
 if not is_speech_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= ["Speech2TextFeatureExtractor"]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= [
     "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFSpeech2TextForConditionalGeneration",
     "TFSpeech2TextModel",
     "TFSpeech2TextPreTrainedModel",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      str												= [
     "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "Speech2TextForConditionalGeneration",
     "Speech2TextModel",
     "Speech2TextPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
 from .processing_speech_to_text import SpeechaTextProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speech_to_text import SpeechaTextTokenizer

 try:
  if not is_speech_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_speech_to_text import (
      TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFSpeechaTextForConditionalGeneration,
      TFSpeechaTextModel,
      TFSpeechaTextPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text import (
      SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextForConditionalGeneration,
      SpeechaTextModel,
      SpeechaTextPreTrainedModel,
  )

else:
 import sys

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


