
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_       :						Union[str, Any]     					=	logging.get_logger(__name__)

lowercase_       :						List[str]     					=	{
    '''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
    '''tiiuae/falcon-7b''': '''https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json''',
}
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
					A__      =			"""falcon"""
					A__      =			["""past_key_values"""]
					def __init__(     self  ,     snake_case__=65024  ,     snake_case__=4544  ,     snake_case__=32  ,     snake_case__=71  ,     snake_case__=1E-5  ,     snake_case__=0.02  ,     snake_case__=True  ,     snake_case__=0.0  ,     snake_case__=0.0  ,     snake_case__=None  ,     snake_case__=False  ,     snake_case__=False  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=False  ,     snake_case__=11  ,     snake_case__=11  ,     **snake_case__  ,     ):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				List[str]							    = vocab_size
							# Backward compatibility with n_embed kwarg
							_SCREAMING_SNAKE_CASE   :				str							    = kwargs.pop("n_embed"  ,     snake_case__				)
							_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = hidden_size if n_embed is None else n_embed
							_SCREAMING_SNAKE_CASE   :				str							    = num_hidden_layers
							_SCREAMING_SNAKE_CASE   :				List[str]							    = num_attention_heads
							_SCREAMING_SNAKE_CASE   :				Any							    = layer_norm_epsilon
							_SCREAMING_SNAKE_CASE   :				Optional[int]							    = initializer_range
							_SCREAMING_SNAKE_CASE   :				int							    = use_cache
							_SCREAMING_SNAKE_CASE   :				Optional[int]							    = hidden_dropout
							_SCREAMING_SNAKE_CASE   :				Dict							    = attention_dropout

							_SCREAMING_SNAKE_CASE   :				Any							    = bos_token_id
							_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = eos_token_id
							_SCREAMING_SNAKE_CASE   :				List[Any]							    = num_attention_heads if num_kv_heads is None else num_kv_heads
							_SCREAMING_SNAKE_CASE   :				Optional[int]							    = alibi
							_SCREAMING_SNAKE_CASE   :				Dict							    = new_decoder_architecture
							_SCREAMING_SNAKE_CASE   :				Any							    = multi_query  # Ignored when new_decoder_architecture is True
							_SCREAMING_SNAKE_CASE   :				Any							    = parallel_attn
							_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = bias

							super().__init__(bos_token_id=snake_case__  ,     eos_token_id=snake_case__  ,     **snake_case__				)
					@property
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							return self.hidden_size // self.num_attention_heads




					@property
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							return not self.alibi





"""simple docstring"""
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 			UpperCamelCase      (							unittest.TestCase  ):
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = TFCamembertModel.from_pretrained("jplu/tf-camembert-base"				)

       _SCREAMING_SNAKE_CASE   :				str							    = tf.convert_to_tensor(
           [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]  ,     dtype=tf.intaa  ,     )  # J'aime le camembert !"

       _SCREAMING_SNAKE_CASE   :				str							    = model(snake_case__				)["last_hidden_state"]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tf.TensorShape((1, 10, 768)				)
       self.assertEqual(output.shape  ,     snake_case__				)
       # compare the actual values for a slice.
       _SCREAMING_SNAKE_CASE   :				int							    = tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]  ,     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				)				)



