


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						{
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any      =  "open-llama"






    def __init__(			self					,   A_=100000					,   A_=4096					,   A_=11008					,   A_=32					,   A_=32					,   A_="silu"					,   A_=2048					,   A_=0.02					,   A_=1E-6					,   A_=True					,   A_=0					,   A_=1					,   A_=2					,   A_=False					,   A_=True					,   A_=0.1					,   A_=0.1					,   A_=True					,   A_=True					,   A_=None					,   **A_					,   )  ->       List[Any]:
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=rms_norm_eps
         __UpperCamelCase    							=use_cache
         __UpperCamelCase    							=kwargs.pop(
             'use_memorry_efficient_attention'					,   A_    )
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_dropout_prob
         __UpperCamelCase    							=use_stable_embedding
         __UpperCamelCase    							=shared_input_output_embedding
         __UpperCamelCase    							=rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=A_					,   bos_token_id=A_					,   eos_token_id=A_					,   tie_word_embeddings=A_					,   **A_					,   )






    def _a					(			self    )  ->       List[str]:
         if self.rope_scaling is None:
              return

         if not isinstance(self.rope_scaling					,   A_    ) or len(self.rope_scaling    ) != 2:
              raise ValueError(
                  '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
                  f'got {self.rope_scaling}'    )
         __UpperCamelCase    							=self.rope_scaling.get('type'					,   A_    )
         __UpperCamelCase    							=self.rope_scaling.get('factor'					,   A_    )
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
              raise ValueError(
                  f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'    )
         if rope_scaling_factor is None or not isinstance(A_					,   A_    ) or rope_scaling_factor <= 1.0:
              raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'    )











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 						lowercase							(   unittest.TestCase		):

     @slow
     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""				)

           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]		,     dtype=tf.intaa		,     )  # J'aime le camembert !"

           _A							:     List[str]			=						model(_a				)["""last_hidden_state"""]
           _A							:     Union[str, Any]			=						tf.TensorShape((1, 10, 768)				)
           self.assertEqual(output.shape		,     _a				)
           # compare the actual values for a slice.
           _A							:     List[Any]			=						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				)				)

