
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class      __A(    unittest.TestCase			):

  """simple docstring"""







  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""						)

       UpperCamelCase__				=    tf.convert_to_tensor(
           [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]	,      dtype=tf.intaa	,      )  # J'aime le camembert !"

       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)["last_hidden_state"]
       UpperCamelCase__				=    tf.TensorShape((1, 10, 7_68)						)
       self.assertEqual(output.shape	,      SCREAMING_SNAKE_CASE_						)
       # compare the actual values for a slice.
       UpperCamelCase__				=    tf.convert_to_tensor(
           [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]	,      dtype=tf.floataa	,      )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()	,      expected_slice.numpy()	,      atol=1E-4						)						)







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_						     =					logging.get_logger(__name__)






def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	str	,		__a      :	Dict	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    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__				=    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__				=    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__				=    pt_tuple_key[:-1] + ("""embedding""",)
          return renamed_pt_tuple_key, pt_tensor

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

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

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

     # old PyTorch layer norm bias
     UpperCamelCase__				=    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 						__magic_name__			(   __a      :	List[Any]	,		__a      :	List[Any]	,		__a      :	Optional[int]=42						):



     '''simple docstring'''




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

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

     UpperCamelCase__				=    flatten_dict(__a						)
     UpperCamelCase__				=    {}

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

          # Correctly rename weight parameters
          UpperCamelCase__		,	UpperCamelCase__				=    rename_key_and_reshape_tensor(__a	,		__a	,		__a						)

          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__				=    jnp.asarray(__a						)

     return unflatten_dict(__a						)





