
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


_A     =  logging.get_logger(__name__)






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			r"\w+[.]\d+"
			lowerCAmelCase_      			=			re.findall(__lowerCAmelCase					,							__lowerCAmelCase       )
			for pat in pats:
						lowerCAmelCase_      			=			key.replace(__lowerCAmelCase					,							"_".join(pat.split("."       )       )       )
			return key






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Dict       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			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)
			):
						lowerCAmelCase_      			=			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:
						lowerCAmelCase_      			=			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:
						lowerCAmelCase_      			=			pt_tuple_key[:-1] + ("embedding",)
						return renamed_pt_tuple_key, pt_tensor

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

			# linear layer
			lowerCAmelCase_      			=			pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight":
						lowerCAmelCase_      			=			pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

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

			# old PyTorch layer norm bias
			lowerCAmelCase_      			=			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      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Optional[Any]=42       ):




			"""simple docstring"""


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

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

			lowerCAmelCase_      			=			flatten_dict(__lowerCAmelCase       )
			lowerCAmelCase_      			=			{}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						lowerCAmelCase_      			=			rename_key(__lowerCAmelCase       )
						lowerCAmelCase_      			=			tuple(renamed_pt_key.split("."       )       )

						# Correctly rename weight parameters
						lowerCAmelCase_			,						lowerCAmelCase_      			=			rename_key_and_reshape_tensor(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase       )

						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
						lowerCAmelCase_      			=			jnp.asarray(__lowerCAmelCase       )

			return unflatten_dict(__lowerCAmelCase       )





from typing import TYPE_CHECKING

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


_A     =  {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Wav2Vec2ForAudioFrameClassification",
			    "Wav2Vec2ForCTC",
			    "Wav2Vec2ForMaskedLM",
			    "Wav2Vec2ForPreTraining",
			    "Wav2Vec2ForSequenceClassification",
			    "Wav2Vec2ForXVector",
			    "Wav2Vec2Model",
			    "Wav2Vec2PreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFWav2Vec2ForCTC",
			    "TFWav2Vec2Model",
			    "TFWav2Vec2PreTrainedModel",
			    "TFWav2Vec2ForSequenceClassification",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "FlaxWav2Vec2ForCTC",
			    "FlaxWav2Vec2ForPreTraining",
			    "FlaxWav2Vec2Model",
			    "FlaxWav2Vec2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
			from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
			from .processing_wavaveca import WavaVecaProcessor
			from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavaveca import (
						    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavaVecaForAudioFrameClassification,
						    WavaVecaForCTC,
						    WavaVecaForMaskedLM,
						    WavaVecaForPreTraining,
						    WavaVecaForSequenceClassification,
						    WavaVecaForXVector,
						    WavaVecaModel,
						    WavaVecaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFWavaVecaForCTC,
						    TFWavaVecaForSequenceClassification,
						    TFWavaVecaModel,
						    TFWavaVecaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    FlaxWavaVecaForCTC,
						    FlaxWavaVecaForPreTraining,
						    FlaxWavaVecaModel,
						    FlaxWavaVecaPreTrainedModel,
						)


else:
			import sys

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



