



'''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


snake_case_					:      str    			=    logging.get_logger(__name__)






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Any												=  R'\w+[.]\d+'
						_UpperCamelCase   :						Dict												=  re.findall(UpperCAmelCase_    ,    UpperCAmelCase_				)
						for pat in pats:
												_UpperCamelCase   :						Union[str, Any]												=  key.replace(UpperCAmelCase_    ,    '_'.join(pat.split('.'				)				)				)
						return key






def A__       (	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   :						Dict												=  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   :						Optional[int]												=  pt_tuple_key[:-1] + ('embedding',)
												return renamed_pt_tuple_key, pt_tensor

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

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

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

						# old PyTorch layer norm bias
						_UpperCamelCase   :						Tuple												=  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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=4_2				):
						# Step 1: Convert pytorch tensor to numpy
						_UpperCamelCase   :						Optional[Any]												=  {k: v.numpy() for k, v in pt_state_dict.items()}

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

						_UpperCamelCase   :						List[str]												=  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   :						Dict												=  rename_key(UpperCAmelCase_				)
												_UpperCamelCase   :						List[str]												=  tuple(renamed_pt_key.split('.'				)				)

												# Correctly rename weight parameters
												_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  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   :						int												=  jnp.asarray(UpperCAmelCase_				)

						return unflatten_dict(UpperCAmelCase_				)







'''simple docstring'''
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
					import jax

					from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
					from transformers.models.bert.modeling_flax_bert import FlaxBertModel
					from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel






@require_flax
class 				lowercase__      (				unittest.TestCase  ):





	@slow
	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
													with self.subTest(lowerCamelCase__					):
																			_UpperCamelCase   :						Optional[int]												=  AutoConfig.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)

																			_UpperCamelCase   :						Dict												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)





	@slow
	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							for model_name in ["roberta-base", "roberta-large"]:
													with self.subTest(lowerCamelCase__					):
																			_UpperCamelCase   :						Any												=  AutoConfig.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)

																			_UpperCamelCase   :						Any												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)





	@slow
	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
													_UpperCamelCase   :						Optional[int]												=  AutoTokenizer.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						List[Any]												=  FlaxBertModel.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Optional[int]												=  tokenizer('Do you support jax jitted function?'					,return_tensors=TensorType.JAX					)

													@jax.jit
													def eval(**lowerCamelCase__   :							Union[str, Any]					):
																			return model(**lowerCamelCase__					)

													eval(**lowerCamelCase__					).block_until_ready()





	@slow
	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							for model_name in ["roberta-base", "roberta-large"]:
													_UpperCamelCase   :						Optional[int]												=  AutoTokenizer.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Tuple												=  FlaxRobertaModel.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Optional[Any]												=  tokenizer('Do you support jax jitted function?'					,return_tensors=TensorType.JAX					)

													@jax.jit
													def eval(**lowerCamelCase__   :							Union[str, Any]					):
																			return model(**lowerCamelCase__					)

													eval(**lowerCamelCase__					).block_until_ready()





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,'bert-base is not a local folder and is not a valid model identifier'					):
													_UpperCamelCase   :						int												=  FlaxAutoModel.from_pretrained('bert-base'					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'					):
													_UpperCamelCase   :						Tuple												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					,revision='aaaaaa'					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'					,):
													_UpperCamelCase   :						List[Any]												=  FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							with self.assertRaisesRegex(lowerCamelCase__					,'Use `from_pt=True` to load this model'					):
													_UpperCamelCase   :						Tuple												=  FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'					)


