





"""simple docstring"""





import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )






class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=1_3       ,					__UpperCAmelCase=7       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=9_9       ,					__UpperCAmelCase=3_2       ,					__UpperCAmelCase=5       ,					__UpperCAmelCase=4       ,					__UpperCAmelCase=3_7       ,					__UpperCAmelCase="gelu"       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=0.1       ,					__UpperCAmelCase=5_1_2       ,					__UpperCAmelCase=1_6       ,					__UpperCAmelCase=2       ,					__UpperCAmelCase=0.02       ,					__UpperCAmelCase=4       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				parent
         lowerCAmelCase__     :Dict											=				batch_size
         lowerCAmelCase__     :Union[str, Any]											=				seq_length
         lowerCAmelCase__     :Union[str, Any]											=				is_training
         lowerCAmelCase__     :int											=				use_attention_mask
         lowerCAmelCase__     :Optional[int]											=				use_token_type_ids
         lowerCAmelCase__     :Any											=				use_labels
         lowerCAmelCase__     :Dict											=				vocab_size
         lowerCAmelCase__     :Optional[Any]											=				hidden_size
         lowerCAmelCase__     :Tuple											=				num_hidden_layers
         lowerCAmelCase__     :int											=				num_attention_heads
         lowerCAmelCase__     :List[Any]											=				intermediate_size
         lowerCAmelCase__     :List[Any]											=				hidden_act
         lowerCAmelCase__     :Optional[Any]											=				hidden_dropout_prob
         lowerCAmelCase__     :Tuple											=				attention_probs_dropout_prob
         lowerCAmelCase__     :Any											=				max_position_embeddings
         lowerCAmelCase__     :Optional[Any]											=				type_vocab_size
         lowerCAmelCase__     :int											=				type_sequence_label_size
         lowerCAmelCase__     :Union[str, Any]											=				initializer_range
         lowerCAmelCase__     :str											=				num_choices






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size )

         lowerCAmelCase__     :str											=				None
         if self.use_attention_mask:
           lowerCAmelCase__     :List[str]											=				random_attention_mask([self.batch_size, self.seq_length] )

         lowerCAmelCase__     :List[str]											=				None
         if self.use_token_type_ids:
           lowerCAmelCase__     :Dict											=				ids_tensor([self.batch_size, self.seq_length]       ,					self.type_vocab_size )

         lowerCAmelCase__     :Any											=				RoFormerConfig(
             vocab_size=self.vocab_size       ,					hidden_size=self.hidden_size       ,					num_hidden_layers=self.num_hidden_layers       ,					num_attention_heads=self.num_attention_heads       ,					intermediate_size=self.intermediate_size       ,					hidden_act=self.hidden_act       ,					hidden_dropout_prob=self.hidden_dropout_prob       ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,					max_position_embeddings=self.max_position_embeddings       ,					type_vocab_size=self.type_vocab_size       ,					is_decoder=__UpperCAmelCase       ,					initializer_range=self.initializer_range       ,					)

         return config, input_ids, token_type_ids, attention_mask






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.prepare_config_and_inputs()
         lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				config_and_inputs
         lowerCAmelCase__     :Dict											=				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict






@require_flax
class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Any						    =   True

       __magic_name__	:List[Any]						    =   (
           (
               FlaxRoFormerModel,
               FlaxRoFormerForMaskedLM,
               FlaxRoFormerForSequenceClassification,
               FlaxRoFormerForTokenClassification,
               FlaxRoFormerForMultipleChoice,
               FlaxRoFormerForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				FlaxRoFormerModelTester(self )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for model_class_name in self.all_model_classes:
           lowerCAmelCase__     :Dict											=				model_class_name.from_pretrained('junnyu/roformer_chinese_small'       ,					from_pt=__UpperCAmelCase )
           lowerCAmelCase__     :Union[str, Any]											=				model(np.ones((1, 1) ) )
           self.assertIsNotNone(__UpperCAmelCase )






@require_flax
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
         lowerCAmelCase__     :Union[str, Any]											=				jnp.array([[0, 1, 2, 3, 4, 5]] )
         lowerCAmelCase__     :Tuple											=				model(__UpperCAmelCase )[0]

         lowerCAmelCase__     :Any											=				5_0_0_0_0

         lowerCAmelCase__     :int											=				(1, 6, vocab_size)
         self.assertEqual(output.shape       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				jnp.array(
             [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]] )

         self.assertTrue(jnp.allclose(output[:, :3, :3]       ,					__UpperCAmelCase       ,					atol=1E-4 ) )







"""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


__A											=						["""gpt2"""]
__A											=						"""gpt2"""

if is_tf_available():






  class    _lowerCAmelCase  (	tf.Module			):
         """simple docstring"""




         def __init__(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           super().__init__()
           lowerCAmelCase__     :List[str]											=				tokenizer
           lowerCAmelCase__     :Optional[int]											=				AutoConfig.from_pretrained(__UpperCAmelCase )
           lowerCAmelCase__     :Optional[Any]											=				TFGPTaLMHeadModel.from_config(__UpperCAmelCase )






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



           '''simple docstring'''

           lowerCAmelCase__     :str											=				self.tokenizer(__UpperCAmelCase )
           lowerCAmelCase__     :int											=				tokenized['input_ids'].to_tensor()

           lowerCAmelCase__     :Optional[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=__UpperCAmelCase       ,					attention_mask=__UpperCAmelCase )['logits']

           return outputs






@require_tf
@require_keras_nlp
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         super().setUp()

         lowerCAmelCase__     :List[str]											=				[GPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
         lowerCAmelCase__     :Union[str, Any]											=				[TFGPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS]
         assert len(self.tokenizers ) == len(self.tf_tokenizers )

         lowerCAmelCase__     :int											=				[
             '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__     :str											=				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:
             lowerCAmelCase__     :int											=				tokenizer([test_inputs]       ,					return_tensors='tf' )
             lowerCAmelCase__     :Optional[int]											=				tf_tokenizer([test_inputs] )

             for key in python_outputs.keys():
               # convert them to numpy to avoid messing with ragged tensors
               lowerCAmelCase__     :Optional[int]											=				python_outputs[key].numpy()
               lowerCAmelCase__     :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(__UpperCAmelCase       ,					tf.intaa ) == tf_outputs_values ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :Optional[int]											=				tf.function(__UpperCAmelCase )
           for test_inputs in self.test_sentences:
             lowerCAmelCase__     :Any											=				tf.constant(__UpperCAmelCase )
             lowerCAmelCase__     :int											=				compiled_tokenizer(__UpperCAmelCase )
             lowerCAmelCase__     :Any											=				tf_tokenizer(__UpperCAmelCase )

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

           lowerCAmelCase__     :Union[str, Any]											=				tf_tokenizer.get_config()
           lowerCAmelCase__     :Tuple											=				TFGPTaTokenizer.from_config(__UpperCAmelCase )
           lowerCAmelCase__     :Tuple											=				model_from_config(__UpperCAmelCase )

           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
           lowerCAmelCase__     :int											=				1_2_3_1_2_3

           for max_length in [3, 5, 1_0_2_4]:
             lowerCAmelCase__     :Tuple											=				tf.convert_to_tensor([self.test_sentences[0]] )
             lowerCAmelCase__     :List[str]											=				tf_tokenizer(__UpperCAmelCase       ,					max_length=__UpperCAmelCase )

             lowerCAmelCase__     :Union[str, Any]											=				out['input_ids'].numpy().shape[1]

             assert out_length == max_length
