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 							__UpperCAmelCase     (							UpperCAmelCase							)->							Dict:



			"""simple docstring"""







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






def 							__UpperCAmelCase     (							UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase							)->							Optional[Any]:



			"""simple docstring"""







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

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

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

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

			# old PyTorch layer norm bias
			lowercase       		=		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     (							UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=42							)->							Optional[Any]:



			"""simple docstring"""







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

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

			lowercase       		=		flatten_dict(UpperCamelCase__							)
			lowercase       		=		{}

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

						# Correctly rename weight parameters
						lowercase		,lowercase       		=		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
						lowercase       		=		jnp.asarray(UpperCamelCase__							)

			return unflatten_dict(UpperCamelCase__							)








from typing import TYPE_CHECKING

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


_UpperCAmelCase						:  Tuple									=			{
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  Tuple									=			[
					    """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """M2M100ForConditionalGeneration""",
					    """M2M100Model""",
					    """M2M100PreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
					from .tokenization_mam_aaa import MaMaaaTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mam_aaa import (
										    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaMaaaForConditionalGeneration,
										    MaMaaaModel,
										    MaMaaaPreTrainedModel,
										)


else:
					import sys

					_UpperCAmelCase						:  Any									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




