


"""simple docstring"""



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class _lowerCAmelCase							:







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__=99		,      UpperCamelCase__=13		,      UpperCamelCase__=7		,      UpperCamelCase__=9		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__=False		,      UpperCamelCase__=32		,      UpperCamelCase__=5		,      UpperCamelCase__=4		,      UpperCamelCase__=37		,      UpperCamelCase__=8		,      UpperCamelCase__=0.1		,      UpperCamelCase__=0.002		,      UpperCamelCase__=1		,      UpperCamelCase__=0		,      UpperCamelCase__=0		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      )      ->						List[Any]:

       '''simple docstring'''




       snake_case       : str    =       parent
       snake_case       : Optional[int]    =       batch_size
       snake_case       : Optional[Any]    =       encoder_seq_length
       snake_case       : List[str]    =       decoder_seq_length
       # For common tests
       snake_case       : Any    =       self.decoder_seq_length
       snake_case       : str    =       is_training
       snake_case       : Optional[int]    =       use_attention_mask
       snake_case       : Tuple    =       use_labels
       snake_case       : Dict    =       vocab_size
       snake_case       : List[Any]    =       hidden_size
       snake_case       : int    =       num_hidden_layers
       snake_case       : Any    =       num_attention_heads
       snake_case       : Union[str, Any]    =       d_ff
       snake_case       : Dict    =       relative_attention_num_buckets
       snake_case       : Tuple    =       dropout_rate
       snake_case       : int    =       initializer_factor
       snake_case       : Dict    =       eos_token_id
       snake_case       : Dict    =       pad_token_id
       snake_case       : Any    =       decoder_start_token_id
       snake_case       : Any    =       None
       snake_case       : Dict    =       decoder_layers







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       return TaConfig.from_pretrained("google/umt5-base"		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      )      ->						Any:

       '''simple docstring'''




       if attention_mask is None:
           snake_case       : List[Any]    =       input_ids.ne(config.pad_token_id		)
       if decoder_attention_mask is None:
           snake_case       : Optional[Any]    =       decoder_input_ids.ne(config.pad_token_id		)
       if head_mask is None:
           snake_case       : Union[str, Any]    =       torch.ones(config.num_hidden_layers		,      config.num_attention_heads		,      device=UpperCamelCase__		)
       if decoder_head_mask is None:
           snake_case       : str    =       torch.ones(config.num_decoder_layers		,      config.num_attention_heads		,      device=UpperCamelCase__		)
       if cross_attn_head_mask is None:
           snake_case       : Tuple    =       torch.ones(
               config.num_decoder_layers		,      config.num_attention_heads		,      device=UpperCamelCase__		)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       snake_case       : Any    =       ids_tensor([self.batch_size, self.encoder_seq_length]		,      self.vocab_size		)
       snake_case       : Optional[int]    =       ids_tensor([self.batch_size, self.decoder_seq_length]		,      self.vocab_size		)

       # we need to clamp the input ids here to avoid having pad token in between
       # this is because for NllbMoe the position_ids are prepared such that
       # all pad tokens have pos id = 2 and rest are between 2..seq_length
       # and the seq_length here is seq_length - num_pad_tokens
       # but when using past, there is no way of knowing if the past input ids had
       # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
       # position_ids being off by num_pad_tokens in past input
       snake_case       : str    =       input_ids.clamp(self.pad_token_id + 1		)
       snake_case       : Any    =       decoder_input_ids.clamp(self.pad_token_id + 1		)

       snake_case       : List[Any]    =       self.get_config()
       snake_case       : Optional[Any]    =       config.num_attention_heads
       snake_case       : Any    =       self.prepare_inputs_dict(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
       return config, input_dict







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case   ,snake_case       : Union[str, Any]    =       self.prepare_config_and_inputs()
       return config, inputs_dict







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       return TaConfig(
           vocab_size=166		,      d_model=self.hidden_size		,      d_ff=self.d_ff		,      d_kv=self.hidden_size // self.num_attention_heads		,      num_layers=self.num_hidden_layers		,      num_decoder_layers=self.decoder_layers		,      num_heads=self.num_attention_heads		,      relative_attention_num_buckets=self.relative_attention_num_buckets		,      dropout_rate=self.dropout_rate		,      initializer_factor=self.initializer_factor		,      eos_token_id=self.eos_token_id		,      bos_token_id=self.pad_token_id		,      pad_token_id=self.pad_token_id		,      decoder_start_token_id=self.decoder_start_token_id		,      )







   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       return TaConfig(
           vocab_size=self.vocab_size		,      d_model=self.hidden_size		,      d_ff=self.d_ff		,      d_kv=self.hidden_size // self.num_attention_heads		,      num_layers=self.num_hidden_layers		,      num_decoder_layers=self.decoder_layers		,      num_heads=self.num_attention_heads		,      relative_attention_num_buckets=self.relative_attention_num_buckets		,      dropout_rate=self.dropout_rate		,      initializer_factor=self.initializer_factor		,      eos_token_id=self.eos_token_id		,      bos_token_id=self.pad_token_id		,      pad_token_id=self.pad_token_id		,      decoder_start_token_id=self.decoder_start_token_id		,      )







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      )      ->						Tuple:

       '''simple docstring'''




       snake_case       : List[str]    =       UMTaModel(config=UpperCamelCase__		)
       model.to(UpperCamelCase__		)
       model.eval()
       snake_case       : str    =       model(
           input_ids=UpperCamelCase__		,      decoder_input_ids=UpperCamelCase__		,      attention_mask=UpperCamelCase__		,      decoder_attention_mask=UpperCamelCase__		,      )
       snake_case       : Dict    =       model(input_ids=UpperCamelCase__		,      decoder_input_ids=UpperCamelCase__		)
       snake_case       : Tuple    =       result.last_hidden_state
       snake_case       : List[Any]    =       result.past_key_values
       snake_case       : List[str]    =       result.encoder_last_hidden_state

       self.parent.assertEqual(encoder_output.size()		,      (self.batch_size, self.encoder_seq_length, self.hidden_size)		)
       self.parent.assertEqual(decoder_output.size()		,      (self.batch_size, self.decoder_seq_length, self.hidden_size)		)
       # There should be `num_layers` key value embeddings stored in decoder_past
       self.parent.assertEqual(len(UpperCamelCase__		)		,      config.num_layers		)
       # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
       self.parent.assertEqual(len(decoder_past[0]		)		,      4		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      )      ->						List[str]:

       '''simple docstring'''




       snake_case       : List[str]    =       UMTaModel(config=UpperCamelCase__		).get_decoder().to(UpperCamelCase__		).eval()
       # first forward pass
       snake_case       : str    =       model(UpperCamelCase__		,      use_cache=UpperCamelCase__		)
       snake_case       : Optional[int]    =       model(UpperCamelCase__		)
       snake_case       : Union[str, Any]    =       model(UpperCamelCase__		,      use_cache=UpperCamelCase__		)

       self.parent.assertTrue(len(UpperCamelCase__		) == len(UpperCamelCase__		)		)
       self.parent.assertTrue(len(UpperCamelCase__		) == len(UpperCamelCase__		) + 1		)

       snake_case   ,snake_case       : Any    =       outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       snake_case       : List[str]    =       ids_tensor((self.batch_size, 1)		,      config.vocab_size		)

       # append to next input_ids and
       snake_case       : Dict    =       torch.cat([input_ids, next_tokens]		,      dim=-1		)

       snake_case       : int    =       model(UpperCamelCase__		)["last_hidden_state"]
       snake_case       : Tuple    =       model(UpperCamelCase__		,      past_key_values=UpperCamelCase__		)["last_hidden_state"]

       # select random slice
       snake_case       : Union[str, Any]    =       ids_tensor((1,)		,      output_from_past.shape[-1]		).item()
       snake_case       : Optional[Any]    =       output_from_no_past[:, -1, random_slice_idx].detach()
       snake_case       : Any    =       output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(UpperCamelCase__		,      UpperCamelCase__		,      atol=1e-3		)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      )      ->						str:

       '''simple docstring'''




       snake_case       : str    =       UMTaModel(config=UpperCamelCase__		).to(UpperCamelCase__		).half().eval()
       snake_case       : str    =       model(**UpperCamelCase__		)["last_hidden_state"]
       self.parent.assertFalse(torch.isnan(UpperCamelCase__		).any().item()		)







@require_torch
class _lowerCAmelCase							( snake_case_       ,				snake_case_       ,				snake_case_       ,				unittest.TestCase      ):
   __UpperCAmelCase					:			int				    =    (
       (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
   )
   __UpperCAmelCase					:			Union[str, Any]				    =    (UMTaForConditionalGeneration,) if is_torch_available() else ()
   __UpperCAmelCase					:			str				    =    (
       {
           '''conversational''': UMTaForConditionalGeneration,
           '''feature-extraction''': UMTaModel,
           '''summarization''': UMTaForConditionalGeneration,
           '''text2text-generation''': UMTaForConditionalGeneration,
           '''translation''': UMTaForConditionalGeneration,
           '''question-answering''': UMTaForQuestionAnswering,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase					:			List[str]				    =    True
   __UpperCAmelCase					:			int				    =    False
   __UpperCAmelCase					:			int				    =    False
   __UpperCAmelCase					:			List[Any]				    =    True
   __UpperCAmelCase					:			List[str]				    =    True
   # The small UMT5 model needs higher percentages for CPU/MP tests
   __UpperCAmelCase					:			Tuple				    =    [0.8, 0.9]







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Dict    =       UMTaModelTester(self		)







   @unittest.skip("Test has a segmentation fault on torch 1.8.0"		)
   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       snake_case       : List[Any]    =       self.model_tester.prepare_config_and_inputs()
       snake_case       : Optional[Any]    =       UMTaModel(config_and_inputs[0]		).to(UpperCamelCase__		)
       with tempfile.TemporaryDirectory() as tmpdirname:
           torch.onnx.export(
               UpperCamelCase__		,      (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,      F'{tmpdirname}/t5_test.onnx'		,      export_params=UpperCamelCase__		,      opset_version=9		,      input_names=["input_ids", "decoder_input_ids"]		,      )







   @unittest.skipIf(torch_device == "cpu"		,      "Cant do half precision"		)
   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       snake_case       : List[Any]    =       ["encoder_attentions", "decoder_attentions", "cross_attentions"]
       snake_case       : Tuple    =       self.model_tester.prepare_config_and_inputs()
       snake_case       : Tuple    =       config_and_inputs[0]
       snake_case       : Optional[int]    =       UMTaForConditionalGeneration(UpperCamelCase__		).eval()
       model.to(UpperCamelCase__		)

       snake_case       : Tuple    =       {
           "head_mask": torch.zeros(config.num_layers		,      config.num_heads		,      device=UpperCamelCase__		),
           "decoder_head_mask": torch.zeros(config.num_decoder_layers		,      config.num_heads		,      device=UpperCamelCase__		),
           "cross_attn_head_mask": torch.zeros(config.num_decoder_layers		,      config.num_heads		,      device=UpperCamelCase__		),
       }

       for attn_name, (name, mask) in zip(UpperCamelCase__		,      head_masking.items()		):
           snake_case       : List[str]    =       {name: mask}
           # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
           if name == "head_mask":
               snake_case       : Union[str, Any]    =       torch.ones(
                   config.num_decoder_layers		,      config.num_heads		,      device=UpperCamelCase__		)

           snake_case       : List[Any]    =       model.generate(
               config_and_inputs[1]["input_ids"]		,      num_beams=1		,      max_length=3		,      output_attentions=UpperCamelCase__		,      return_dict_in_generate=UpperCamelCase__		,      **UpperCamelCase__		,      )
           # We check the state of decoder_attentions and cross_attentions just from the last step
           snake_case       : Dict    =       out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
           self.assertEqual(sum([w.sum().item() for w in attn_weights]		)		,      0.0		)







   @unittest.skip("Does not work on the tiny model as we keep hitting edge cases."		)
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       pass







@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase							( unittest.TestCase      ):







   @slow
   @unittest.skip(
       "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"		)
   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       snake_case       : Optional[Any]    =       UMTaForConditionalGeneration.from_pretrained("google/umt5-small"		,      return_dict=UpperCamelCase__		).to(UpperCamelCase__		)
       snake_case       : Any    =       AutoTokenizer.from_pretrained("google/umt5-small"		,      use_fast=UpperCamelCase__		,      legacy=UpperCamelCase__		)
       snake_case       : Optional[Any]    =       [
           "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
           "No se como puedo <extra_id_0>.",
           "This is the reason why we <extra_id_0> them.",
           "The <extra_id_0> walks in <extra_id_1>, seats",
           "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
       ]
       snake_case       : str    =       tokenizer(UpperCamelCase__		,      return_tensors="pt"		,      padding=UpperCamelCase__		).input_ids
       # fmt: off
       snake_case       : Dict    =       torch.tensor(
           [
               [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
               [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
           ]		)
       # fmt: on
       torch.testing.assert_allclose(UpperCamelCase__		,      UpperCamelCase__		)

       snake_case       : Optional[Any]    =       model.generate(input_ids.to(UpperCamelCase__		)		)
       snake_case       : Optional[int]    =       [
           "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
           "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
           "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
           "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
           "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
       ]
       snake_case       : Any    =       tokenizer.batch_decode(UpperCamelCase__		)
       self.assertEqual(UpperCamelCase__		,      UpperCamelCase__		)









"""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


__snake_case       =       ["""gpt2"""]
__snake_case       =       """gpt2"""

if is_tf_available():







  class _lowerCAmelCase							( tf.Module      ):







     def __init__(      self		,      UpperCamelCase__		)      ->						Optional[Any]:

         '''simple docstring'''




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







     @tf.function(input_signature=(tf.TensorSpec((None,)		,      tf.string		,      name="text"		),)		)
     def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Optional[int]:

         '''simple docstring'''




         snake_case       : List[Any]    =       self.tokenizer(UpperCamelCase__		)
         snake_case       : Tuple    =       tokenized["input_ids"].to_tensor()

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

         snake_case       : int    =       self.model(input_ids=UpperCamelCase__		,      attention_mask=UpperCamelCase__		)["logits"]

         return outputs







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







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       super().setUp()

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

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







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




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

               for key in python_outputs.keys():
                   # convert them to numpy to avoid messing with ragged tensors
                   snake_case       : Any    =       python_outputs[key].numpy()
                   snake_case       : 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(UpperCamelCase__		,      tf.intaa		) == tf_outputs_values		)		)







   @slow
   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




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

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







   @slow
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




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

       '''simple docstring'''




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

           snake_case       : Dict    =       tf_tokenizer.get_config()
           snake_case       : Union[str, Any]    =       TFGPTaTokenizer.from_config(UpperCamelCase__		)
           snake_case       : Optional[Any]    =       model_from_config(UpperCamelCase__		)

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







   @slow
   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       for tf_tokenizer in self.tf_tokenizers:
           # for the test to run
           snake_case       : List[str]    =       12_3123

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

               snake_case       : str    =       out["input_ids"].numpy().shape[1]

               assert out_length == max_length





