"""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


__lowerCAmelCase					: Dict        =["""gpt2"""]
__lowerCAmelCase					: Union[str, Any]        ="""gpt2"""

if is_tf_available():





       class 				_A		( tf.Module			):

             def __init__(		self	, __lowerCAmelCase		):







                   """simple docstring"""



                   super().__init__()
                   lowercase														=			tokenizer
                   lowercase														=			AutoConfig.from_pretrained(__lowerCAmelCase		)
                   lowercase														=			TFGPTaLMHeadModel.from_config(__lowerCAmelCase		)







             @tf.function(input_signature=(tf.TensorSpec((None,)	, tf.string	, name="""text"""		),)		)
             def        A__ (		self	, __lowerCAmelCase		):







                   """simple docstring"""



                   lowercase														=			self.tokenizer(__lowerCAmelCase		)
                   lowercase														=			tokenized["""input_ids"""].to_tensor()

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

                   lowercase														=			self.model(input_ids=__lowerCAmelCase	, attention_mask=__lowerCAmelCase		)["""logits"""]

                   return outputs





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

      def        A__ (		self		):







            """simple docstring"""



            super().setUp()

            lowercase														=			[GPTaTokenizer.from_pretrained(__lowerCAmelCase		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
            lowercase														=			[TFGPTaTokenizer.from_pretrained(__lowerCAmelCase		) for checkpoint in TOKENIZER_CHECKPOINTS]
            assert len(self.tokenizers		) == len(self.tf_tokenizers		)

            lowercase														=			[
                """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														=			list(zip(self.test_sentences	, self.test_sentences[::-1]		)		)

      def        A__ (		self		):







            """simple docstring"""



            for tokenizer, tf_tokenizer in zip(self.tokenizers	, self.tf_tokenizers		):
                  for test_inputs in self.test_sentences:
                        lowercase														=			tokenizer([test_inputs]	, return_tensors="""tf"""		)
                        lowercase														=			tf_tokenizer([test_inputs]		)

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

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

      @slow
      def        A__ (		self		):







            """simple docstring"""



            for tf_tokenizer in self.tf_tokenizers:
                  lowercase														=			tf.function(__lowerCAmelCase		)
                  for test_inputs in self.test_sentences:
                        lowercase														=			tf.constant(__lowerCAmelCase		)
                        lowercase														=			compiled_tokenizer(__lowerCAmelCase		)
                        lowercase														=			tf_tokenizer(__lowerCAmelCase		)

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

      @slow
      def        A__ (		self		):







            """simple docstring"""



            for tf_tokenizer in self.tf_tokenizers:
                  lowercase														=			ModelToSave(tokenizer=__lowerCAmelCase		)
                  lowercase														=			tf.convert_to_tensor([self.test_sentences[0]]		)
                  lowercase														=			model.serving(__lowerCAmelCase		)  # Build model with some sample inputs
                  with TemporaryDirectory() as tempdir:
                        lowercase														=			Path(__lowerCAmelCase		) / """saved.model"""
                        tf.saved_model.save(__lowerCAmelCase	, __lowerCAmelCase	, signatures={"""serving_default""": model.serving}		)
                        lowercase														=			tf.saved_model.load(__lowerCAmelCase		)
                  lowercase														=			loaded_model.signatures["""serving_default"""](__lowerCAmelCase		)["""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		):







            """simple docstring"""



            for tf_tokenizer in self.tf_tokenizers:
                  lowercase														=			tf.convert_to_tensor([self.test_sentences[0]]		)
                  lowercase														=			tf_tokenizer(__lowerCAmelCase		)  # Build model with some sample inputs

                  lowercase														=			tf_tokenizer.get_config()
                  lowercase														=			TFGPTaTokenizer.from_config(__lowerCAmelCase		)
                  lowercase														=			model_from_config(__lowerCAmelCase		)

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







      @slow
      def        A__ (		self		):







            """simple docstring"""



            for tf_tokenizer in self.tf_tokenizers:
                  # for the test to run
                  lowercase														=			12_3123

                  for max_length in [3, 5, 1024]:
                        lowercase														=			tf.convert_to_tensor([self.test_sentences[0]]		)
                        lowercase														=			tf_tokenizer(__lowerCAmelCase	, max_length=__lowerCAmelCase		)

                        lowercase														=			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 				_A		( unittest.TestCase			):

      def __init__(		self	, __lowerCAmelCase	, __lowerCAmelCase=13	, __lowerCAmelCase=7	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=True	, __lowerCAmelCase=99	, __lowerCAmelCase=32	, __lowerCAmelCase=5	, __lowerCAmelCase=4	, __lowerCAmelCase=37	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.1	, __lowerCAmelCase=512	, __lowerCAmelCase=16	, __lowerCAmelCase=2	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=4	, ):







            """simple docstring"""



            lowercase														=			parent
            lowercase														=			batch_size
            lowercase														=			seq_length
            lowercase														=			is_training
            lowercase														=			use_attention_mask
            lowercase														=			use_token_type_ids
            lowercase														=			use_labels
            lowercase														=			vocab_size
            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			max_position_embeddings
            lowercase														=			type_vocab_size
            lowercase														=			type_sequence_label_size
            lowercase														=			initializer_range
            lowercase														=			num_choices

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size		)

            lowercase														=			None
            if self.use_attention_mask:
                  lowercase														=			random_attention_mask([self.batch_size, self.seq_length]		)

            lowercase														=			None
            if self.use_token_type_ids:
                  lowercase														=			ids_tensor([self.batch_size, self.seq_length]	, self.type_vocab_size		)

            lowercase														=			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=__lowerCAmelCase	, initializer_range=self.initializer_range	, )

            return config, input_ids, token_type_ids, attention_mask

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            lowercase							,							lowercase							,							lowercase							,							lowercase														=			config_and_inputs
            lowercase														=			{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
            return config, inputs_dict







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.prepare_config_and_inputs()
            lowercase							,							lowercase							,							lowercase							,							lowercase														=			config_and_inputs

            lowercase														=			True
            lowercase														=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
            lowercase														=			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 				_A		( lowerCAmelCase						,					unittest.TestCase			):
      snake_case__    :     Optional[int]													=			True

      snake_case__    :     List[str]													=			(
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			FlaxRobertaModelTester(self		)







      @slow
      def        A__ (		self		):







            """simple docstring"""



            for model_class_name in self.all_model_classes:
                  lowercase														=			model_class_name.from_pretrained("""roberta-base"""	, from_pt=__lowerCAmelCase		)
                  lowercase														=			model(np.ones((1, 1)		)		)
                  self.assertIsNotNone(__lowerCAmelCase		)



