"""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


_lowerCamelCase   :				str     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    r"\w+[.]\d+"
					SCREAMING_SNAKE_CASE           =    re.findall(lowerCAmelCase__ ,	lowerCAmelCase__					)
					for pat in pats:
										SCREAMING_SNAKE_CASE           =    key.replace(lowerCAmelCase__ ,	"_".join(pat.split("."					)					)					)
					return key





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							List[Any]					):
					SCREAMING_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)
					):
										SCREAMING_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:
										SCREAMING_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:
										SCREAMING_SNAKE_CASE           =    pt_tuple_key[:-1] + ("embedding",)
										return renamed_pt_tuple_key, pt_tensor

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

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

					# old PyTorch layer norm weight
					SCREAMING_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
					SCREAMING_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 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Union[str, Any]=4_2					):
					SCREAMING_SNAKE_CASE           =    {k: v.numpy() for k, v in pt_state_dict.items()}

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

					SCREAMING_SNAKE_CASE           =    flatten_dict(lowerCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    {}

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

										# Correctly rename weight parameters
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    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
										SCREAMING_SNAKE_CASE           =    jnp.asarray(lowerCAmelCase__					)

					return unflatten_dict(lowerCAmelCase__					)





import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
							import jax.numpy as jnp

							from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class   lowercase      (       unittest.TestCase       ):




							@slow
							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    "The dog is cute and lives in the garden house"
												SCREAMING_SNAKE_CASE           =    jnp.array([tokenizer.encode(_UpperCamelCase )] )

												SCREAMING_SNAKE_CASE           =    (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
												SCREAMING_SNAKE_CASE           =    jnp.array(
												    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )

												SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase )["last_hidden_state"]
												self.assertEqual(output.shape		,							_UpperCamelCase )
												# compare the actual values for a slice of last dim
												self.assertTrue(jnp.allclose(output[:, :, -1]		,							_UpperCamelCase		,							atol=1e-3 ) )




