
'''simple docstring'''



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


_lowercase    =		logging.get_logger(__name__)


def A				(__lowerCamelCase				:Dict		):
						_lowerCAmelCase  						=			r'\w+[.]\d+'
						_lowerCAmelCase  						=			re.findall(_A			,     _A		)
						for pat in pats:
												_lowerCAmelCase  						=			key.replace(_A			,     """_""".join(pat.split("""."""		)		)		)
						return key


def A				(__lowerCamelCase				:Tuple			,     __lowerCamelCase				:Any			,     __lowerCamelCase				:Optional[int]		):
						_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 A				(__lowerCamelCase				:List[str]			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:str=42		):
						_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(_A		)		)

						_lowerCAmelCase  						=			flatten_dict(_A		)
						_lowerCAmelCase  						=			{}

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

												# Correctly rename weight parameters
												_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			jnp.asarray(_A		)

						return unflatten_dict(_A		)


'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase    =		{"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		["""DeiTFeatureExtractor"""]
						_lowercase    =		["""DeiTImageProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """DeiTForImageClassification""",
						    """DeiTForImageClassificationWithTeacher""",
						    """DeiTForMaskedImageModeling""",
						    """DeiTModel""",
						    """DeiTPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFDeiTForImageClassification""",
						    """TFDeiTForImageClassificationWithTeacher""",
						    """TFDeiTForMaskedImageModeling""",
						    """TFDeiTModel""",
						    """TFDeiTPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deit import DeiTFeatureExtractor
												from .image_processing_deit import DeiTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deit import (
												    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeiTForImageClassification,
												    DeiTForImageClassificationWithTeacher,
												    DeiTForMaskedImageModeling,
												    DeiTModel,
												    DeiTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deit import (
												    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDeiTForImageClassification,
												    TFDeiTForImageClassificationWithTeacher,
												    TFDeiTForMaskedImageModeling,
												    TFDeiTModel,
												    TFDeiTPreTrainedModel,
												)


else:
						import sys

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