


'''simple docstring'''



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	={
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Any   =					'''xlm'''
					lowerCAmelCase      :Any   =					{
					    '''hidden_size''': '''emb_dim''',
					    '''num_attention_heads''': '''n_heads''',
					    '''num_hidden_layers''': '''n_layers''',
					    '''n_words''': '''vocab_size''',  # For backward compatibility
					}




					def __init__(      self							,							_lowerCamelCase=3_0145							,							_lowerCamelCase=2048							,							_lowerCamelCase=12							,							_lowerCamelCase=16							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=True							,							_lowerCamelCase=False							,							_lowerCamelCase=False							,							_lowerCamelCase=False							,							_lowerCamelCase=1							,							_lowerCamelCase=True							,							_lowerCamelCase=512							,							_lowerCamelCase=2048**-0.5							,							_lowerCamelCase=1e-1_2							,							_lowerCamelCase=0.02							,							_lowerCamelCase=0							,							_lowerCamelCase=1							,							_lowerCamelCase=2							,							_lowerCamelCase=3							,							_lowerCamelCase=5							,							_lowerCamelCase=True							,							_lowerCamelCase="first"							,							_lowerCamelCase=True							,							_lowerCamelCase=None							,							_lowerCamelCase=True							,							_lowerCamelCase=0.1							,							_lowerCamelCase=5							,							_lowerCamelCase=5							,							_lowerCamelCase=0							,							_lowerCamelCase=0							,							_lowerCamelCase=2							,							_lowerCamelCase=0							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Tuple        =     vocab_size
								UpperCAmelCase__  :					Tuple        =     emb_dim
								UpperCAmelCase__  :					Optional[Any]        =     n_layers
								UpperCAmelCase__  :					List[str]        =     n_heads
								UpperCAmelCase__  :					List[str]        =     dropout
								UpperCAmelCase__  :					Optional[int]        =     attention_dropout
								UpperCAmelCase__  :					Tuple        =     gelu_activation
								UpperCAmelCase__  :					Optional[Any]        =     sinusoidal_embeddings
								UpperCAmelCase__  :					int        =     causal
								UpperCAmelCase__  :					Union[str, Any]        =     asm
								UpperCAmelCase__  :					Optional[Any]        =     n_langs
								UpperCAmelCase__  :					List[Any]        =     use_lang_emb
								UpperCAmelCase__  :					Union[str, Any]        =     layer_norm_eps
								UpperCAmelCase__  :					List[str]        =     bos_index
								UpperCAmelCase__  :					List[Any]        =     eos_index
								UpperCAmelCase__  :					int        =     pad_index
								UpperCAmelCase__  :					str        =     unk_index
								UpperCAmelCase__  :					Dict        =     mask_index
								UpperCAmelCase__  :					str        =     is_encoder
								UpperCAmelCase__  :					Dict        =     max_position_embeddings
								UpperCAmelCase__  :					Any        =     embed_init_std
								UpperCAmelCase__  :					List[Any]        =     init_std
								UpperCAmelCase__  :					List[str]        =     summary_type
								UpperCAmelCase__  :					Union[str, Any]        =     summary_use_proj
								UpperCAmelCase__  :					Any        =     summary_activation
								UpperCAmelCase__  :					List[str]        =     summary_proj_to_labels
								UpperCAmelCase__  :					Union[str, Any]        =     summary_first_dropout
								UpperCAmelCase__  :					str        =     start_n_top
								UpperCAmelCase__  :					str        =     end_n_top
								UpperCAmelCase__  :					Tuple        =     mask_token_id
								UpperCAmelCase__  :					Union[str, Any]        =     lang_id

								if "n_words" in kwargs:
											UpperCAmelCase__  :					List[str]        =     kwargs["""n_words"""]

								super().__init__(pad_token_id=_lowerCamelCase							,							bos_token_id=_lowerCamelCase							,							**_lowerCamelCase)


class 		_snake_case					(     a__	):




					@property
					def 				snake_case__						(      self):
								if self.task == "multiple-choice":
											UpperCAmelCase__  :					Any        =     {0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
											UpperCAmelCase__  :					List[Any]        =     {0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								        ("""token_type_ids""", dynamic_axis),
								    ])



'''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


__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[str]        =     R"""\w+[.]\d+"""
			UpperCAmelCase__  :					List[Any]        =     re.findall(UpperCamelCase__ ,      UpperCamelCase__					)
			for pat in pats:
						UpperCAmelCase__  :					str        =     key.replace(UpperCamelCase__ ,      """_""".join(pat.split("""."""					)					)					)
			return key







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     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)
			):
						UpperCAmelCase__  :					Union[str, Any]        =     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:
						UpperCAmelCase__  :					str        =     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:
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""embedding""",)
						return renamed_pt_tuple_key, pt_tensor

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

			# linear layer
			UpperCAmelCase__  :					Optional[int]        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight":
						UpperCAmelCase__  :					Any        =     pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

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

			# old PyTorch layer norm bias
			UpperCAmelCase__  :					Dict        =     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__=4_2					):
			# Step 1: Convert pytorch tensor to numpy
			UpperCAmelCase__  :					str        =     {k: v.numpy() for k, v in pt_state_dict.items()}

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

			UpperCAmelCase__  :					Union[str, Any]        =     flatten_dict(UpperCamelCase__					)
			UpperCAmelCase__  :					int        =     {}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						UpperCAmelCase__  :					str        =     rename_key(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     tuple(renamed_pt_key.split("""."""					)					)

						# Correctly rename weight parameters
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     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
						UpperCAmelCase__  :					str        =     jnp.asarray(UpperCamelCase__					)

			return unflatten_dict(UpperCamelCase__					)