
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    :    Union[str, 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    :    int       ,				__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    int							):




			"""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    :    Optional[int]       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Union[str, 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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A  =	logging.get_logger(__name__)

_A  =	{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''ibert'''




				def __init__(   self							,							_UpperCamelCase=30_522							,							_UpperCamelCase=768							,							_UpperCamelCase=12							,							_UpperCamelCase=12							,							_UpperCamelCase=3_072							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=1e-1_2							,							_UpperCamelCase=1							,							_UpperCamelCase=0							,							_UpperCamelCase=2							,							_UpperCamelCase="absolute"							,							_UpperCamelCase=False							,							_UpperCamelCase="none"							,							**_UpperCamelCase							,							)							-> List[str]:
							super().__init__(pad_token_id=_UpperCamelCase							,							bos_token_id=_UpperCamelCase							,							eos_token_id=_UpperCamelCase							,							**_UpperCamelCase	)

							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      layer_norm_eps
							lowerCAmelCase_         =      position_embedding_type
							lowerCAmelCase_         =      quant_mode
							lowerCAmelCase_         =      force_dequant





class       _lowerCAmelCase   (      __a			):




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										lowerCAmelCase_         =      {0: "batch", 1: "choice", 2: "sequence"}
							else:
										lowerCAmelCase_         =      {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]	)



