
"""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_							=					['gpt2']
UpperCamelCase_							=					'gpt2'

if is_tf_available():





      class 				snake_case    (			tf.Module						):







         def __init__(      self    ,				__UpperCAmelCase)					->List[str]:
            super().__init__()
            a_							=			tokenizer
            a_							=			AutoConfig.from_pretrained(__lowerCamelCase)
            a_							=			TFGPTaLMHeadModel.from_config(__lowerCamelCase)







         @tf.function(input_signature=(tf.TensorSpec((None,)    ,				tf.string    ,				name="text"),))
         def 					UpperCAmelCase__     (      self    ,				__UpperCAmelCase)					->str:
            a_							=			self.tokenizer(__lowerCamelCase)
            a_							=			tokenized["input_ids"].to_tensor()

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

            a_							=			self.model(input_ids=__lowerCamelCase    ,				attention_mask=__lowerCamelCase)["logits"]

            return outputs





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







   def 					UpperCAmelCase__     (      self)					->Any:
      super().setUp()

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

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







   def 					UpperCAmelCase__     (      self)					->Tuple:
      for tokenizer, tf_tokenizer in zip(self.tokenizers    ,				self.tf_tokenizers):
         for test_inputs in self.test_sentences:
            a_							=			tokenizer([test_inputs]    ,				return_tensors="tf")
            a_							=			tf_tokenizer([test_inputs])

            for key in python_outputs.keys():
               # convert them to numpy to avoid messing with ragged tensors
               a_							=			python_outputs[key].numpy()
               a_							=			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)					->int:
      for tf_tokenizer in self.tf_tokenizers:
         a_							=			tf.function(__lowerCamelCase)
         for test_inputs in self.test_sentences:
            a_							=			tf.constant(__lowerCamelCase)
            a_							=			compiled_tokenizer(__lowerCamelCase)
            a_							=			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[Any]:
      for tf_tokenizer in self.tf_tokenizers:
         a_							=			ModelToSave(tokenizer=__lowerCamelCase)
         a_							=			tf.convert_to_tensor([self.test_sentences[0]])
         a_							=			model.serving(__lowerCamelCase)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
            a_							=			Path(__lowerCamelCase) / "saved.model"
            tf.saved_model.save(__lowerCamelCase    ,				__lowerCamelCase    ,				signatures={"serving_default": model.serving})
            a_							=			tf.saved_model.load(__lowerCamelCase)
         a_							=			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)					->Optional[int]:
      for tf_tokenizer in self.tf_tokenizers:
         a_							=			tf.convert_to_tensor([self.test_sentences[0]])
         a_							=			tf_tokenizer(__lowerCamelCase)  # Build model with some sample inputs

         a_							=			tf_tokenizer.get_config()
         a_							=			TFGPTaTokenizer.from_config(__lowerCamelCase)
         a_							=			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)					->Dict:
      for tf_tokenizer in self.tf_tokenizers:
         # for the test to run
         a_							=			12_31_23

         for max_length in [3, 5, 10_24]:
            a_							=			tf.convert_to_tensor([self.test_sentences[0]])
            a_							=			tf_tokenizer(__lowerCamelCase    ,				max_length=__lowerCamelCase)

            a_							=			out["input_ids"].numpy().shape[1]

            assert out_length == max_length





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase		:				Optional[Any]       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				str       			=		{'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Dict							 =					"""openai-gpt"""
				SCREAMING_SNAKE_CASE_       :							Tuple							 =					{
				    """max_position_embeddings""": """n_positions""",
				    """hidden_size""": """n_embd""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}





				def __init__(      self				:     str					,      __lowerCamelCase				:     List[str]=4_04_78					,      __lowerCamelCase				:     List[Any]=5_12					,      __lowerCamelCase				:     List[str]=7_68					,      __lowerCamelCase				:     List[str]=12					,      __lowerCamelCase				:     Optional[Any]=12					,      __lowerCamelCase				:     str="gelu"					,      __lowerCamelCase				:     Optional[int]=0.1					,      __lowerCamelCase				:     Any=0.1					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     Any=1e-5					,      __lowerCamelCase				:     Optional[int]=0.02					,      __lowerCamelCase				:     Optional[int]="cls_index"					,      __lowerCamelCase				:     List[Any]=True					,      __lowerCamelCase				:     Optional[int]=None					,      __lowerCamelCase				:     Optional[Any]=True					,      __lowerCamelCase				:     Tuple=0.1					,      **__lowerCamelCase				:     Union[str, Any]					,      )					->     List[str]:
							a    =			vocab_size
							a    =			n_positions
							a    =			n_embd
							a    =			n_layer
							a    =			n_head
							a    =			afn
							a    =			resid_pdrop
							a    =			embd_pdrop
							a    =			attn_pdrop
							a    =			layer_norm_epsilon
							a    =			initializer_range
							a    =			summary_type
							a    =			summary_use_proj
							a    =			summary_activation
							a    =			summary_first_dropout
							a    =			summary_proj_to_labels
							super().__init__(**__lowerCamelCase							)






