
'''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


UpperCAmelCase_       :   Tuple   =       ['gpt2']
UpperCAmelCase_       :   Optional[Any]   =       'gpt2'

if is_tf_available():

 class  UpperCAmelCase__						(       tf.Module ):



     def __init__(	self      :       Any,__A      :       Dict     ):
           super().__init__()
           _lowerCamelCase				:   List[str]								=   tokenizer
           _lowerCamelCase				:   str								=   AutoConfig.from_pretrained(__A     )
           _lowerCamelCase				:   Optional[Any]								=   TFGPTaLMHeadModel.from_config(__A     )



     @tf.function(input_signature=(tf.TensorSpec((None,),tf.string,name="text"     ),)     )
     def   lowerCamelCase_  (	self      :       List[Any],__A      :       Tuple     ):
           _lowerCamelCase				:   int								=   self.tokenizer(__A     )
           _lowerCamelCase				:   str								=   tokenized["input_ids"].to_tensor()

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

           _lowerCamelCase				:   List[Any]								=   self.model(input_ids=__A,attention_mask=__A     )["logits"]

           return outputs

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



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          super().setUp()

          _lowerCamelCase				:   Any								=   [GPTaTokenizer.from_pretrained(__A     ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
          _lowerCamelCase				:   int								=   [TFGPTaTokenizer.from_pretrained(__A     ) for checkpoint in TOKENIZER_CHECKPOINTS]
          assert len(self.tokenizers     ) == len(self.tf_tokenizers     )

          _lowerCamelCase				:   str								=   [
              "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				:   int								=   list(zip(self.test_sentences,self.test_sentences[::-1]     )     )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          for tokenizer, tf_tokenizer in zip(self.tokenizers,self.tf_tokenizers     ):
                for test_inputs in self.test_sentences:
                      _lowerCamelCase				:   str								=   tokenizer([test_inputs],return_tensors="tf"     )
                      _lowerCamelCase				:   int								=   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   lowerCamelCase_  (	self      :       int     ):
          for tf_tokenizer in self.tf_tokenizers:
                _lowerCamelCase				:   Optional[Any]								=   tf.function(__A     )
                for test_inputs in self.test_sentences:
                      _lowerCamelCase				:   List[Any]								=   tf.constant(__A     )
                      _lowerCamelCase				:   str								=   compiled_tokenizer(__A     )
                      _lowerCamelCase				:   List[Any]								=   tf_tokenizer(__A     )

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



    @slow
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          for tf_tokenizer in self.tf_tokenizers:
                _lowerCamelCase				:   int								=   ModelToSave(tokenizer=__A     )
                _lowerCamelCase				:   List[Any]								=   tf.convert_to_tensor([self.test_sentences[0]]     )
                _lowerCamelCase				:   Union[str, Any]								=   model.serving(__A     )  # Build model with some sample inputs
                with TemporaryDirectory() as tempdir:
                      _lowerCamelCase				:   Optional[int]								=   Path(__A     ) / "saved.model"
                      tf.saved_model.save(__A,__A,signatures={"serving_default": model.serving}     )
                      _lowerCamelCase				:   Tuple								=   tf.saved_model.load(__A     )
                _lowerCamelCase				:   Optional[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   lowerCamelCase_  (	self      :       Any     ):
          for tf_tokenizer in self.tf_tokenizers:
                _lowerCamelCase				:   Any								=   tf.convert_to_tensor([self.test_sentences[0]]     )
                _lowerCamelCase				:   str								=   tf_tokenizer(__A     )  # Build model with some sample inputs

                _lowerCamelCase				:   List[Any]								=   tf_tokenizer.get_config()
                _lowerCamelCase				:   Union[str, Any]								=   TFGPTaTokenizer.from_config(__A     )
                _lowerCamelCase				:   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   lowerCamelCase_  (	self      :       Tuple     ):
          for tf_tokenizer in self.tf_tokenizers:
                # for the test to run
                _lowerCamelCase				:   Any								=   1_2_3_1_2_3

                for max_length in [3, 5, 1_0_2_4]:
                      _lowerCamelCase				:   Union[str, Any]								=   tf.convert_to_tensor([self.test_sentences[0]]     )
                      _lowerCamelCase				:   Optional[Any]								=   tf_tokenizer(__A,max_length=__A     )

                      _lowerCamelCase				:   Optional[Any]								=   out["input_ids"].numpy().shape[1]

                      assert out_length == max_length

'''simple docstring'''






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )

class  UpperCAmelCase__						(       unittest.TestCase ):



    def __init__(	self      :       Union[str, Any],__A      :       Dict,__A      :       List[str]=1_3,__A      :       Any=7,__A      :       str=True,__A      :       Optional[int]=True,__A      :       Optional[Any]=True,__A      :       Any=True,__A      :       List[str]=9_9,__A      :       str=3_2,__A      :       List[str]=5,__A      :       Optional[Any]=4,__A      :       Any=3_7,__A      :       Optional[Any]="gelu",__A      :       List[Any]=0.1,__A      :       Any=0.1,__A      :       Dict=5_1_2,__A      :       Tuple=1_6,__A      :       Tuple=2,__A      :       List[Any]=0.02,__A      :       Any=4,):
          _lowerCamelCase				:   List[Any]								=   parent
          _lowerCamelCase				:   Optional[int]								=   batch_size
          _lowerCamelCase				:   Tuple								=   seq_length
          _lowerCamelCase				:   Tuple								=   is_training
          _lowerCamelCase				:   Union[str, Any]								=   use_attention_mask
          _lowerCamelCase				:   Optional[Any]								=   use_token_type_ids
          _lowerCamelCase				:   List[Any]								=   use_labels
          _lowerCamelCase				:   str								=   vocab_size
          _lowerCamelCase				:   List[str]								=   hidden_size
          _lowerCamelCase				:   Union[str, Any]								=   num_hidden_layers
          _lowerCamelCase				:   Tuple								=   num_attention_heads
          _lowerCamelCase				:   Union[str, Any]								=   intermediate_size
          _lowerCamelCase				:   Optional[Any]								=   hidden_act
          _lowerCamelCase				:   Tuple								=   hidden_dropout_prob
          _lowerCamelCase				:   Optional[Any]								=   attention_probs_dropout_prob
          _lowerCamelCase				:   List[Any]								=   max_position_embeddings
          _lowerCamelCase				:   Union[str, Any]								=   type_vocab_size
          _lowerCamelCase				:   Union[str, Any]								=   type_sequence_label_size
          _lowerCamelCase				:   str								=   initializer_range
          _lowerCamelCase				:   List[Any]								=   num_choices



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

          _lowerCamelCase				:   Dict								=   None
          if self.use_attention_mask:
                _lowerCamelCase				:   Optional[int]								=   random_attention_mask([self.batch_size, self.seq_length]     )

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

          _lowerCamelCase				:   Optional[int]								=   RobertaConfig(
              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=__A,initializer_range=self.initializer_range,)

          return config, input_ids, token_type_ids, attention_mask



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   List[str]								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   str								=   config_and_inputs
          _lowerCamelCase				:   List[Any]								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Any								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Dict								=   config_and_inputs

          _lowerCamelCase				:   int								=   True
          _lowerCamelCase				:   List[Any]								=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],vocab_size=2     )

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )


@require_flax
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		True

    lowerCAmelCase_				    =		(
        (
            FlaxRobertaModel,
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[str]								=   FlaxRobertaModelTester(self     )



    @slow
    def   lowerCamelCase_  (	self      :       Any     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   Union[str, Any]								=   model_class_name.from_pretrained("roberta-base",from_pt=__A     )
                _lowerCamelCase				:   Union[str, Any]								=   model(np.ones((1, 1)     )     )
                self.assertIsNotNone(__A     )