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 							UpperCamelCase				(    lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






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

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[str]:




       '''simple docstring'''






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

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

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

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

       # old PyTorch layer norm bias
       _A=	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				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_=42   )		->						Tuple:




       '''simple docstring'''






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

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

       _A=	flatten_dict(lowerCAmelCase_   )
       _A=	{}

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

              # Correctly rename weight parameters
              _A,			_A=	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
              _A=	jnp.asarray(lowerCAmelCase_   )

       return unflatten_dict(lowerCAmelCase_   )
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      lowerCAmelCase						(					unittest.TestCase			):



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxBertModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxRobertaModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'bert-base is not a local folder and is not a valid model identifier'       ):
                   _A=	FlaxAutoModel.from_pretrained('bert-base'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
                   _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__				,      revision='aaaaaa'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'				,      ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'       )





     def 			a__  (					self       ):
            with self.assertRaisesRegex(lowerCAmelCase__				,      'Use `from_pt=True` to load this model'       ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'       )