

"""simple docstring"""


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


a					:Optional[int]    = logging.get_logger(__name__)

a					:Tuple    = {
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}

class      __a							(_UpperCAmelCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = """trocr"""
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = ["""past_key_values"""]
     _SCREAMING_SNAKE_CASE						:str       = {
         """num_attention_heads""": """decoder_attention_heads""",
         """hidden_size""": """d_model""",
         """num_hidden_layers""": """decoder_layers""",
     }

     def __init__(						self	,    _a=50_265	,    _a=1_024	,    _a=12	,    _a=16	,    _a=4_096	,    _a="gelu"	,    _a=512	,    _a=0.1	,    _a=0.0	,    _a=0.0	,    _a=2	,    _a=0.02	,    _a=0.0	,    _a=True	,    _a=False	,    _a=True	,    _a=True	,    _a=1	,    _a=0	,    _a=2	,    **_a	,    )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      int      		=	d_model
           SCREAMING_SNAKE_CASE__   :      Any      		=	decoder_layers
           SCREAMING_SNAKE_CASE__   :      str      		=	decoder_attention_heads
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	decoder_ffn_dim
           SCREAMING_SNAKE_CASE__   :      str      		=	activation_function
           SCREAMING_SNAKE_CASE__   :      Dict      		=	max_position_embeddings
           SCREAMING_SNAKE_CASE__   :      Dict      		=	dropout
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	attention_dropout
           SCREAMING_SNAKE_CASE__   :      Dict      		=	activation_dropout
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	init_std
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	decoder_layerdrop
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	use_cache
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scale_embedding
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	use_learned_position_embeddings
           SCREAMING_SNAKE_CASE__   :      Dict      		=	layernorm_embedding

           super().__init__(
               pad_token_id=lowercase__	,    bos_token_id=lowercase__	,    eos_token_id=lowercase__	,    decoder_start_token_id=lowercase__	,    **lowercase__	,    )







"""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      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]	,    dtype=tf.intaa	,    )  # J'aime le camembert !"

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




