


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				:				Optional[int]  				= ['gpt2']
__UpperCamelCase				:				str  				= 'gpt2'

if is_tf_available():

       class 					__magic_name__		(   tf.Module):


            def __init__(					self							:   str							,		lowerCamelCase__							:   str	)     ->					str:





             '''simple docstring'''






             super().__init__()
             UpperCamelCase__    :			Any      				=      tokenizer
             UpperCamelCase__    :			str      				=      AutoConfig.from_pretrained(lowerCamelCase__	)
             UpperCamelCase__    :			List[Any]      				=      TFGPTaLMHeadModel.from_config(lowerCamelCase__	)


            @tf.function(input_signature=(tf.TensorSpec((None,)							,		tf.string							,		name='''text'''	),)	)
            def    UpperCAmelCase__		(					self							:   Dict							,		lowerCamelCase__							:   List[str]	)     ->					List[Any]:





             '''simple docstring'''






             UpperCamelCase__    :			Dict      				=      self.tokenizer(lowerCamelCase__	)
             UpperCamelCase__    :			List[Any]      				=      tokenized['''input_ids'''].to_tensor()

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

             UpperCamelCase__    :			Optional[int]      				=      self.model(input_ids=lowerCamelCase__							,		attention_mask=lowerCamelCase__	)['''logits''']

             return outputs

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


     def    UpperCAmelCase__		(					self							:   Dict	)     ->					Tuple:





      '''simple docstring'''






      super().setUp()

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

      UpperCamelCase__    :			List[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''',
      ]
      UpperCamelCase__    :			Union[str, Any]      				=      list(zip(self.test_sentences							,		self.test_sentences[::-1]	)	)


     def    UpperCAmelCase__		(					self							:   Dict	)     ->					int:





      '''simple docstring'''






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

        for key in python_outputs.keys():
         # convert them to numpy to avoid messing with ragged tensors
         UpperCamelCase__    :			Union[str, Any]      				=      python_outputs[key].numpy()
         UpperCamelCase__    :			int      				=      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    UpperCAmelCase__		(					self							:   Optional[Any]	)     ->					Tuple:





      '''simple docstring'''






      for tf_tokenizer in self.tf_tokenizers:
       UpperCamelCase__    :			str      				=      tf.function(lowerCamelCase__	)
       for test_inputs in self.test_sentences:
        UpperCamelCase__    :			Optional[int]      				=      tf.constant(lowerCamelCase__	)
        UpperCamelCase__    :			Tuple      				=      compiled_tokenizer(lowerCamelCase__	)
        UpperCamelCase__    :			Tuple      				=      tf_tokenizer(lowerCamelCase__	)

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


     @slow
     def    UpperCAmelCase__		(					self							:   Optional[int]	)     ->					Union[str, Any]:





      '''simple docstring'''






      for tf_tokenizer in self.tf_tokenizers:
       UpperCamelCase__    :			List[str]      				=      ModelToSave(tokenizer=lowerCamelCase__	)
       UpperCamelCase__    :			Any      				=      tf.convert_to_tensor([self.test_sentences[0]]	)
       UpperCamelCase__    :			str      				=      model.serving(lowerCamelCase__	)  # Build model with some sample inputs
       with TemporaryDirectory() as tempdir:
        UpperCamelCase__    :			Optional[Any]      				=      Path(lowerCamelCase__	) / '''saved.model'''
        tf.saved_model.save(lowerCamelCase__							,		lowerCamelCase__							,		signatures={'''serving_default''': model.serving}	)
        UpperCamelCase__    :			Optional[Any]      				=      tf.saved_model.load(lowerCamelCase__	)
       UpperCamelCase__    :			List[Any]      				=      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    UpperCAmelCase__		(					self							:   List[str]	)     ->					List[str]:





      '''simple docstring'''






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

       UpperCamelCase__    :			Dict      				=      tf_tokenizer.get_config()
       UpperCamelCase__    :			Any      				=      TFGPTaTokenizer.from_config(lowerCamelCase__	)
       UpperCamelCase__    :			Optional[Any]      				=      model_from_config(lowerCamelCase__	)

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


     @slow
     def    UpperCAmelCase__		(					self							:   Tuple	)     ->					Tuple:





      '''simple docstring'''






      for tf_tokenizer in self.tf_tokenizers:
       # for the test to run
       UpperCamelCase__    :			List[Any]      				=      123123

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

        UpperCamelCase__    :			List[Any]      				=      out['''input_ids'''].numpy().shape[1]

        assert out_length == max_length










from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Dict,  lowerCamelCase						:    pyspark.sql.DataFrame,  lowerCamelCase						:    Optional[NamedSplit] = None,  lowerCamelCase						:    Optional[Features] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    str = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    str = "arrow",  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(
								    split=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  keep_in_memory=lowerCamelCase,  streaming=lowerCamelCase,  **lowerCamelCase,  )
								lowercase__    =			load_from_cache_file
								lowercase__    =			file_format
								lowercase__    =			Spark(
								    df=lowerCamelCase,  features=lowerCamelCase,  cache_dir=lowerCamelCase,  working_dir=lowerCamelCase,  **lowerCamelCase,  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								if self.streaming:
												return self.builder.as_streaming_dataset(split=self.split  )
								lowercase__    =			None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
								self.builder.download_and_prepare(
								    download_mode=lowerCamelCase,  file_format=self._file_format,  )
								return self.builder.as_dataset(split=self.split  )




