
'''simple docstring'''







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


_lowercase :  Optional[int]    							=       ["gpt2"]
_lowercase :  Union[str, Any]    							=       "gpt2"

if is_tf_available():






							class  lowerCAmelCase__   (  tf.Module     ):






												def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


																		"""simple docstring"""





																		super().__init__()
																		lowercase_	:     int													=       tokenizer
																		lowercase_	:     List[str]													=       AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE		)
																		lowercase_	:     Optional[int]													=       TFGPTaLMHeadModel.from_config(__SCREAMING_SNAKE_CASE		)






												@tf.function(input_signature=(tf.TensorSpec((None,)				,							tf.string				,							name='''text'''		),)		)
												def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


																		"""simple docstring"""





																		lowercase_	:     Dict													=       self.tokenizer(__SCREAMING_SNAKE_CASE		)
																		lowercase_	:     Dict													=       tokenized['''input_ids'''].to_tensor()

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

																		lowercase_	:     Any													=       self.model(input_ids=__SCREAMING_SNAKE_CASE				,							attention_mask=__SCREAMING_SNAKE_CASE		)['''logits''']

																		return outputs






@require_tf
@require_keras_nlp
class  lowerCAmelCase__   (  unittest.TestCase     ):






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().setUp()

											lowercase_	:     List[str]													=       [GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
											lowercase_	:     Dict													=       [TFGPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE		) for checkpoint in TOKENIZER_CHECKPOINTS]
											assert len(self.tokenizers		) == len(self.tf_tokenizers		)

											lowercase_	:     Optional[Any]													=       [
											    '''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''',
											]
											lowercase_	:     Optional[int]													=       list(zip(self.test_sentences				,							self.test_sentences[::-1]		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tokenizer, tf_tokenizer in zip(self.tokenizers				,							self.tf_tokenizers		):
																	for test_inputs in self.test_sentences:
																							lowercase_	:     str													=       tokenizer([test_inputs]				,							return_tensors='''tf'''		)
																							lowercase_	:     Tuple													=       tf_tokenizer([test_inputs]		)

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

																													self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape		)		)
																													self.assertTrue(tf.reduce_all(tf.cast(__SCREAMING_SNAKE_CASE				,							tf.intaa		) == tf_outputs_values		)		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	lowercase_	:     str													=       tf.function(__SCREAMING_SNAKE_CASE		)
																	for test_inputs in self.test_sentences:
																							lowercase_	:     str													=       tf.constant(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Tuple													=       compiled_tokenizer(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     List[str]													=       tf_tokenizer(__SCREAMING_SNAKE_CASE		)

																							for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	lowercase_	:     Dict													=       ModelToSave(tokenizer=__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Tuple													=       tf.convert_to_tensor([self.test_sentences[0]]		)
																	lowercase_	:     Union[str, Any]													=       model.serving(__SCREAMING_SNAKE_CASE		)  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																							lowercase_	:     Union[str, Any]													=       Path(__SCREAMING_SNAKE_CASE		) / '''saved.model'''
																							tf.saved_model.save(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							signatures={'''serving_default''': model.serving}		)
																							lowercase_	:     List[str]													=       tf.saved_model.load(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     Any													=       loaded_model.signatures['''serving_default'''](__SCREAMING_SNAKE_CASE		)['''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 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	lowercase_	:     Dict													=       tf.convert_to_tensor([self.test_sentences[0]]		)
																	lowercase_	:     Optional[int]													=       tf_tokenizer(__SCREAMING_SNAKE_CASE		)  # Build model with some sample inputs

																	lowercase_	:     Tuple													=       tf_tokenizer.get_config()
																	lowercase_	:     int													=       TFGPTaTokenizer.from_config(__SCREAMING_SNAKE_CASE		)
																	lowercase_	:     List[Any]													=       model_from_config(__SCREAMING_SNAKE_CASE		)

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






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tf_tokenizer in self.tf_tokenizers:
																	# for the test to run
																	lowercase_	:     Union[str, Any]													=       12_31_23

																	for max_length in [3, 5, 10_24]:
																							lowercase_	:     Dict													=       tf.convert_to_tensor([self.test_sentences[0]]		)
																							lowercase_	:     Dict													=       tf_tokenizer(__SCREAMING_SNAKE_CASE				,							max_length=__SCREAMING_SNAKE_CASE		)

																							lowercase_	:     Optional[Any]													=       out['''input_ids'''].numpy().shape[1]

																							assert out_length == max_length





'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowercase :  int    							=       logging.get_logger(__name__)

_lowercase :  List[Any]    							=       {
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}






class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_     ):
					lowerCAmelCase_        =     '''nat'''

					lowerCAmelCase_        =     {
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=4				,							__SCREAMING_SNAKE_CASE=3				,							__SCREAMING_SNAKE_CASE=64				,							__SCREAMING_SNAKE_CASE=[3, 4, 6, 5]				,							__SCREAMING_SNAKE_CASE=[2, 4, 8, 16]				,							__SCREAMING_SNAKE_CASE=7				,							__SCREAMING_SNAKE_CASE=3.0				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE="gelu"				,							__SCREAMING_SNAKE_CASE=0.02				,							__SCREAMING_SNAKE_CASE=1E-5				,							__SCREAMING_SNAKE_CASE=0.0				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       patch_size
											lowercase_	:     List[Any]													=       num_channels
											lowercase_	:     str													=       embed_dim
											lowercase_	:     List[str]													=       depths
											lowercase_	:     str													=       len(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       num_heads
											lowercase_	:     int													=       kernel_size
											lowercase_	:     Union[str, Any]													=       mlp_ratio
											lowercase_	:     Optional[int]													=       qkv_bias
											lowercase_	:     List[Any]													=       hidden_dropout_prob
											lowercase_	:     Optional[int]													=       attention_probs_dropout_prob
											lowercase_	:     List[Any]													=       drop_path_rate
											lowercase_	:     List[Any]													=       hidden_act
											lowercase_	:     int													=       layer_norm_eps
											lowercase_	:     int													=       initializer_range
											# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
											# this indicates the channel dimension after the last stage of the model
											lowercase_	:     Dict													=       int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE		) - 1)		)
											lowercase_	:     Tuple													=       layer_scale_init_value
											lowercase_	:     Union[str, Any]													=       ['''stem'''] + [F'''stage{idx}''' for idx in range(1				,							len(__SCREAMING_SNAKE_CASE		) + 1		)]
											lowercase_						, lowercase_	:     int													=       get_aligned_output_features_output_indices(
											    out_features=__SCREAMING_SNAKE_CASE				,							out_indices=__SCREAMING_SNAKE_CASE				,							stage_names=self.stage_names		)



