




'''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


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


def      UpperCamelCase_(	snake_case							:       Any     ):

							'''simple docstring'''






							snake_case_						=      R'''\w+[.]\d+'''
							snake_case_						=      re.findall(_lowercase							,				_lowercase     )
							for pat in pats:
														snake_case_						=      key.replace(_lowercase							,				"_".join(pat.split("."     )     )     )
							return key


def      UpperCamelCase_(	snake_case							:       str							,				snake_case							:       str							,				snake_case							:       List[Any]     ):

							'''simple docstring'''






							snake_case_						=      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)
							):
														snake_case_						=      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:
														snake_case_						=      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:
														snake_case_						=      pt_tuple_key[:-1] + ('''embedding''',)
														return renamed_pt_tuple_key, pt_tensor

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

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

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

							# old PyTorch layer norm bias
							snake_case_						=      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      UpperCamelCase_(	snake_case							:       Optional[int]							,				snake_case							:       Optional[Any]							,				snake_case							:       Optional[Any]=4_2     ):

							'''simple docstring'''






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

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

							snake_case_						=      flatten_dict(_lowercase     )
							snake_case_						=      {}

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

														# Correctly rename weight parameters
														snake_case_						=      rename_key_and_reshape_tensor(_lowercase							,				_lowercase							,				_lowercase     )

														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
														snake_case_						=      jnp.asarray(_lowercase     )

							return unflatten_dict(_lowercase     )





from typing import TYPE_CHECKING

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


__UpperCamelCase		:       List[Any]									=     {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     ['GLPNFeatureExtractor']
					__UpperCamelCase		:       List[str]									=     ['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

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





