





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
    import tensorflow as tf

if is_keras_nlp_available():
    from transformers.models.gpta import TFGPTaTokenizer


_UpperCAmelCase       : Any						     =       ["""gpt2"""]
_UpperCAmelCase       : Optional[Any]						     =       """gpt2"""

if is_tf_available():

    class      lowerCAmelCase							(      tf.Module    ):
     def __init__(   self						:   Any    ,  UpperCAmelCase						:   List[Any]			)    ->			Tuple:
         super().__init__()
         lowerCamelCase__					:   Union[str, Any]      				=	tokenizer
         lowerCamelCase__					:   str      				=	AutoConfig.from_pretrained(_a			)
         lowerCamelCase__					:   int      				=	TFGPTaLMHeadModel.from_config(_a			)







     @tf.function(input_signature=(tf.TensorSpec((None,)    ,  tf.string    ,  name='text'			),)			)
     def   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   List[str]			)    ->			Optional[Any]:
         lowerCamelCase__					:   Union[str, Any]      				=	self.tokenizer(_a			)
         lowerCamelCase__					:   str      				=	tokenized["""input_ids"""].to_tensor()

         lowerCamelCase__					:   List[Any]      				=	tf.cast(input_ids_dense > 0    ,  tf.intaa			)
         # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

         lowerCamelCase__					:   int      				=	self.model(input_ids=_a    ,  attention_mask=_a			)["""logits"""]

         return outputs

@require_tf
@require_keras_nlp
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Optional[Any]			)    ->			Optional[Any]:
     super().setUp()

     lowerCamelCase__					:   str      				=	[GPTaTokenizer.from_pretrained(_a			) for checkpoint in (TOKENIZER_CHECKPOINTS)]
     lowerCamelCase__					:   str      				=	[TFGPTaTokenizer.from_pretrained(_a			) for checkpoint in TOKENIZER_CHECKPOINTS]
     assert len(self.tokenizers			) == len(self.tf_tokenizers			)

     lowerCamelCase__					:   Tuple      				=	[
         """This is a straightforward English test sentence.""",
         """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
         """Now we're going to add some Chinese: 一 二 三 一二三""",
         """And some much more rare Chinese: 齉 堃 齉堃""",
         """Je vais aussi écrire en français pour tester les accents""",
         """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
     ]
     lowerCamelCase__					:   Any      				=	list(zip(self.test_sentences    ,  self.test_sentences[::-1]			)			)
 def   A_			(   self						:   Any			)    ->			Optional[int]:




     for tokenizer, tf_tokenizer in zip(self.tokenizers    ,  self.tf_tokenizers			):
         for test_inputs in self.test_sentences:
             lowerCamelCase__					:   List[str]      				=	tokenizer([test_inputs]    ,  return_tensors='tf'			)
             lowerCamelCase__					:   Optional[Any]      				=	tf_tokenizer([test_inputs]			)

             for key in python_outputs.keys():
                 # convert them to numpy to avoid messing with ragged tensors
                 lowerCamelCase__					:   str      				=	python_outputs[key].numpy()
                 lowerCamelCase__					:   List[Any]      				=	tf_outputs[key].numpy()

                 self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape			)			)
                 self.assertTrue(tf.reduce_all(tf.cast(_a    ,  tf.intaa			) == tf_outputs_values			)			)
 @slow
 def   A_			(   self						:   Union[str, Any]			)    ->			Optional[Any]:




     for tf_tokenizer in self.tf_tokenizers:
         lowerCamelCase__					:   Any      				=	tf.function(_a			)
         for test_inputs in self.test_sentences:
             lowerCamelCase__					:   int      				=	tf.constant(_a			)
             lowerCamelCase__					:   Any      				=	compiled_tokenizer(_a			)
             lowerCamelCase__					:   Tuple      				=	tf_tokenizer(_a			)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]			)			)
 @slow
 def   A_			(   self						:   Any			)    ->			List[str]:




     for tf_tokenizer in self.tf_tokenizers:
         lowerCamelCase__					:   Tuple      				=	ModelToSave(tokenizer=_a			)
         lowerCamelCase__					:   List[str]      				=	tf.convert_to_tensor([self.test_sentences[0]]			)
         lowerCamelCase__					:   Any      				=	model.serving(_a			)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             lowerCamelCase__					:   Dict      				=	Path(_a			) / """saved.model"""
             tf.saved_model.save(_a    ,  _a    ,  signatures={'serving_default': model.serving}			)
             lowerCamelCase__					:   Dict      				=	tf.saved_model.load(_a			)
         lowerCamelCase__					:   Union[str, Any]      				=	loaded_model.signatures["""serving_default"""](_a			)["""output_0"""]
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertTrue(tf.reduce_all(out == loaded_output			)			)
 @slow
 def   A_			(   self						:   List[Any]			)    ->			Optional[Any]:




     for tf_tokenizer in self.tf_tokenizers:
         lowerCamelCase__					:   List[Any]      				=	tf.convert_to_tensor([self.test_sentences[0]]			)
         lowerCamelCase__					:   Optional[Any]      				=	tf_tokenizer(_a			)  # Build model with some sample inputs

         lowerCamelCase__					:   Tuple      				=	tf_tokenizer.get_config()
         lowerCamelCase__					:   Tuple      				=	TFGPTaTokenizer.from_config(_a			)
         lowerCamelCase__					:   Optional[int]      				=	model_from_config(_a			)

         for key in from_config_output.keys():
             self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]			)			)







 @slow
 def   A_			(   self						:   Any			)    ->			List[Any]:




     for tf_tokenizer in self.tf_tokenizers:
         # for the test to run
         lowerCamelCase__					:   Any      				=	123123

         for max_length in [3, 5, 1024]:
             lowerCamelCase__					:   Any      				=	tf.convert_to_tensor([self.test_sentences[0]]			)
             lowerCamelCase__					:   Optional[int]      				=	tf_tokenizer(_a    ,  max_length=_a			)

             lowerCamelCase__					:   int      				=	out["""input_ids"""].numpy().shape[1]

             assert out_length == max_length












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


_UpperCAmelCase       : Dict			     =       logging.get_logger(__name__)

_UpperCAmelCase       : Union[str, Any]			     =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	"""donut-swin"""

 UpperCAmelCase__									=	{
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }
 def __init__(   self						:   Tuple    ,  UpperCAmelCase						:   List[str]=224    ,  UpperCAmelCase						:   Optional[Any]=4    ,  UpperCAmelCase						:   Union[str, Any]=3    ,  UpperCAmelCase						:   Union[str, Any]=96    ,  UpperCAmelCase						:   int=[2, 2, 6, 2]    ,  UpperCAmelCase						:   Dict=[3, 6, 12, 24]    ,  UpperCAmelCase						:   Any=7    ,  UpperCAmelCase						:   Optional[int]=4.0    ,  UpperCAmelCase						:   List[Any]=True    ,  UpperCAmelCase						:   Any=0.0    ,  UpperCAmelCase						:   Optional[Any]=0.0    ,  UpperCAmelCase						:   int=0.1    ,  UpperCAmelCase						:   List[str]="gelu"    ,  UpperCAmelCase						:   List[str]=False    ,  UpperCAmelCase						:   str=0.0_2    ,  UpperCAmelCase						:   str=1e-5    ,  **UpperCAmelCase						:   Tuple    ,  )    ->			int:
     super().__init__(**UpperCAmelCase			)

     lowerCamelCase__					:   Optional[Any]      				=	image_size
     lowerCamelCase__					:   Optional[int]      				=	patch_size
     lowerCamelCase__					:   int      				=	num_channels
     lowerCamelCase__					:   int      				=	embed_dim
     lowerCamelCase__					:   Optional[Any]      				=	depths
     lowerCamelCase__					:   Optional[int]      				=	len(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[int]      				=	num_heads
     lowerCamelCase__					:   Tuple      				=	window_size
     lowerCamelCase__					:   Dict      				=	mlp_ratio
     lowerCamelCase__					:   str      				=	qkv_bias
     lowerCamelCase__					:   Tuple      				=	hidden_dropout_prob
     lowerCamelCase__					:   str      				=	attention_probs_dropout_prob
     lowerCamelCase__					:   str      				=	drop_path_rate
     lowerCamelCase__					:   int      				=	hidden_act
     lowerCamelCase__					:   str      				=	use_absolute_embeddings
     lowerCamelCase__					:   List[Any]      				=	layer_norm_eps
     lowerCamelCase__					:   Optional[Any]      				=	initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     lowerCamelCase__					:   Optional[int]      				=	int(embed_dim * 2 ** (len(UpperCAmelCase			) - 1)			)





