'''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


__UpperCAmelCase     							=logging.get_logger(__name__)






def __lowerCAmelCase (							UpperCamelCase__				)	->    Any:
    __lowerCamelCase		=  r'''\w+[.]\d+'''
    __lowerCamelCase		=  re.findall(UpperCamelCase__				,  UpperCamelCase__				)
    for pat in pats:
        __lowerCamelCase		=  key.replace(UpperCamelCase__				,  '''_'''.join(pat.split('''.'''				)				)				)
    return key






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)	->    Dict:
    __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 (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__=42				)	->    Optional[Any]:
    # Step 1: Convert pytorch tensor to numpy
    __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(UpperCamelCase__				)				)

    __lowerCamelCase		=  flatten_dict(UpperCamelCase__				)
    __lowerCamelCase		=  {}

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

        # Correctly rename weight parameters
        __lowerCamelCase	,  __lowerCamelCase		=  rename_key_and_reshape_tensor(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				)

        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(UpperCamelCase__				)

    return unflatten_dict(UpperCamelCase__				)



"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase  :      str							      =		{
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      str							      =		[
							    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LongT5EncoderModel''',
							    '''LongT5ForConditionalGeneration''',
							    '''LongT5Model''',
							    '''LongT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''FlaxLongT5ForConditionalGeneration''',
							    '''FlaxLongT5Model''',
							    '''FlaxLongT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_longta import (
														    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LongTaEncoderModel,
														    LongTaForConditionalGeneration,
														    LongTaModel,
														    LongTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_longta import (
														    FlaxLongTaForConditionalGeneration,
														    FlaxLongTaModel,
														    FlaxLongTaPreTrainedModel,
														)


else:
							import sys

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




