






'''simple docstring'''



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      '''gpt_neo'''
      UpperCamelCase_             =      ['''past_key_values''']
      UpperCamelCase_             =      {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		int=5_0257  ,   UpperCAmelCase      :		Optional[Any]=2048  ,   UpperCAmelCase      :		str=2048  ,   UpperCAmelCase      :		str=24  ,   UpperCAmelCase      :		Tuple=[[["global", "local"], 12]]  ,   UpperCAmelCase      :		int=16  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   UpperCAmelCase      :		Optional[int]=256  ,   UpperCAmelCase      :		List[str]="gelu_new"  ,   UpperCAmelCase      :		Optional[int]=0.0  ,   UpperCAmelCase      :		Dict=0.0  ,   UpperCAmelCase      :		int=0.0  ,   UpperCAmelCase      :		Optional[Any]=0.1  ,   UpperCAmelCase      :		Optional[Any]=1e-5  ,   UpperCAmelCase      :		Dict=0.0_2  ,   UpperCAmelCase      :		Dict=True  ,   UpperCAmelCase      :		int=5_0256  ,   UpperCAmelCase      :		List[str]=5_0256  ,   **UpperCAmelCase      :		List[str]  ,   )	->	Union[str, Any]:


       '''simple docstring'''

       lowercase       : Tuple						=vocab_size
       lowercase       : Optional[int]						=max_position_embeddings
       lowercase       : Tuple						=hidden_size
       lowercase       : str						=num_layers
       lowercase       : Optional[Any]						=num_heads
       lowercase       : List[Any]						=intermediate_size
       lowercase       : Union[str, Any]						=window_size
       lowercase       : Optional[Any]						=activation_function
       lowercase       : Union[str, Any]						=resid_dropout
       lowercase       : List[str]						=embed_dropout
       lowercase       : int						=attention_dropout
       lowercase       : List[str]						=classifier_dropout
       lowercase       : List[Any]						=layer_norm_epsilon
       lowercase       : int						=initializer_range
       lowercase       : List[Any]						=use_cache

       lowercase       : Union[str, Any]						=bos_token_id
       lowercase       : Optional[int]						=eos_token_id

       lowercase       : Tuple						=attention_types
       lowercase       : List[str]						=self.expand_attention_types_params(UpperCAmelCase		)

       if len(self.attention_layers		) != self.num_layers:
        raise ValueError(
            '''Configuration for convolutional module is incorrect. '''
            '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
            f'but is `len(config.attention_layers) = {len(self.attention_layers		)}`, '
            f'`config.num_layers = {self.num_layers}`. '
            '''`config.attention_layers` is prepared using `config.attention_types`. '''
            '''Please verify the value of `config.attention_types` argument.'''		)

       super().__init__(bos_token_id=UpperCAmelCase  ,   eos_token_id=UpperCAmelCase  ,   **UpperCAmelCase		)






      @staticmethod
      def 					A__   (			UpperCAmelCase      :		Dict		)	->	int:


       '''simple docstring'''

       lowercase       : int						=[]
       for item in attention_types:
        for _ in range(item[1]		):
         attentions.extend(item[0]		)
       return attentions





def 				lowercase_					(     __A			:							Any       ,   __A			:							Tuple       ,   __A			:							Tuple       ,   __A			:							Dict      )							->   List[str]:



 """simple docstring"""




 import torch

 lowercase       : str						=input.size()
 lowercase       : List[str]						=len(__A      )
 lowercase       : Optional[Any]						=shape[dimension]

 lowercase       : Optional[Any]						=torch.arange(0       ,   __A       ,   __A      )
 lowercase       : List[str]						=torch.div(sizedim - size       ,   __A       ,   rounding_mode='''floor'''      ) + 1
 lowercase       : Optional[int]						=torch.arange(__A      ) + low_indices[:min_length][:, None]

 lowercase       : List[Any]						=[slice(__A      )] * rank
 lowercase       : Dict						=indices
 lowercase       : str						=input[s]

 lowercase       : str						=list(range(0       ,   rank + 1      )      )
 perm.append(perm.pop(dimension + 1      )      )

 return sliced.permute(__A      )


def 				lowercase_					(     __A			:							Tuple       ,   __A			:							int      )							->   List[str]:



 """simple docstring"""




 import torch

 lowercase       : Any						=torch.arange(1       ,   __A      )
 lowercase       : Union[str, Any]						=torch.remainder(__A       ,   __A      )
 lowercase       : Any						=remainders == 0
 lowercase       : List[str]						=candidates[divisor_indices]
 lowercase       : Optional[int]						=torch.max(__A      )
 return largest_divisor, torch.div(__A       ,   __A       ,   rounding_mode='''floor'''      )





class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      @property
      def 					A__   (			self      :		List[str]		)	->	Mapping[str, Mapping[int, str]]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
       if self.use_past:
        self.fill_with_past_key_values_(UpperCAmelCase  ,   direction='''inputs'''		)
        lowercase       : List[str]						={0: '''batch''', 1: '''past_sequence + sequence'''}
       else:
        lowercase       : Dict						={0: '''batch''', 1: '''sequence'''}

       return common_inputs






      @property
      def 					A__   (			self      :		int		)	->	int:


       '''simple docstring'''

       return self._config.num_heads






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		PreTrainedTokenizer  ,   UpperCAmelCase      :		int = -1  ,   UpperCAmelCase      :		int = -1  ,   UpperCAmelCase      :		bool = False  ,   UpperCAmelCase      :		Optional[TensorType] = None  ,   )	->	Mapping[str, Any]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=super(UpperCAmelCase  ,   self		).generate_dummy_inputs(
           UpperCAmelCase  ,   batch_size=UpperCAmelCase  ,   seq_length=UpperCAmelCase  ,   is_pair=UpperCAmelCase  ,   framework=UpperCAmelCase		)

       # We need to order the input in the way they appears in the forward()
       lowercase       : List[Any]						=OrderedDict({'''input_ids''': common_inputs['''input_ids''']}		)

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
        else:
         import torch

         lowercase	,		lowercase       : Tuple						=common_inputs['''input_ids'''].shape
         # Not using the same length for past_key_values
         lowercase       : Optional[Any]						=seqlen + 2
         lowercase       : List[str]						=(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         lowercase       : Optional[Any]						=[
             (torch.zeros(UpperCAmelCase		), torch.zeros(UpperCAmelCase		)) for _ in range(self.num_layers		)
         ]

       lowercase       : Any						=common_inputs['''attention_mask''']
       if self.use_past:
        lowercase       : List[Any]						=ordered_inputs['''attention_mask'''].dtype
        lowercase       : List[str]						=torch.cat(
            [ordered_inputs['''attention_mask'''], torch.ones(UpperCAmelCase  ,   UpperCAmelCase  ,   dtype=UpperCAmelCase		)]  ,   dim=1		)

       return ordered_inputs






      @property
      def 					A__   (			self      :		str		)	->	int:


       '''simple docstring'''

       return 13










"""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


__A											=						["""gpt2"""]
__A											=						"""gpt2"""

if is_tf_available():






  class    _lowerCAmelCase  (	tf.Module			):
         """simple docstring"""




         def __init__(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           super().__init__()
           lowerCAmelCase__     :List[str]											=				tokenizer
           lowerCAmelCase__     :Optional[int]											=				AutoConfig.from_pretrained(__UpperCAmelCase )
           lowerCAmelCase__     :Optional[Any]											=				TFGPTaLMHeadModel.from_config(__UpperCAmelCase )






         @tf.function(input_signature=(tf.TensorSpec((None,)       ,					tf.string       ,					name='text' ),) )
         def 	snake_case		(       self       ,					__UpperCAmelCase ):



           '''simple docstring'''

           lowerCAmelCase__     :str											=				self.tokenizer(__UpperCAmelCase )
           lowerCAmelCase__     :int											=				tokenized['input_ids'].to_tensor()

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

           lowerCAmelCase__     :int											=				self.model(input_ids=__UpperCAmelCase       ,					attention_mask=__UpperCAmelCase )['logits']

           return outputs






@require_tf
@require_keras_nlp
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         super().setUp()

         lowerCAmelCase__     :List[str]											=				[GPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
         lowerCAmelCase__     :Union[str, Any]											=				[TFGPTaTokenizer.from_pretrained(__UpperCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS]
         assert len(self.tokenizers ) == len(self.tf_tokenizers )

         lowerCAmelCase__     :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',
         ]
         lowerCAmelCase__     :str											=				list(zip(self.test_sentences       ,					self.test_sentences[::-1] ) )






       def 	snake_case		(       self ):



         '''simple docstring'''

         for tokenizer, tf_tokenizer in zip(self.tokenizers       ,					self.tf_tokenizers ):
           for test_inputs in self.test_sentences:
             lowerCAmelCase__     :int											=				tokenizer([test_inputs]       ,					return_tensors='tf' )
             lowerCAmelCase__     :Optional[int]											=				tf_tokenizer([test_inputs] )

             for key in python_outputs.keys():
               # convert them to numpy to avoid messing with ragged tensors
               lowerCAmelCase__     :Optional[int]											=				python_outputs[key].numpy()
               lowerCAmelCase__     :List[str]											=				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 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           lowerCAmelCase__     :Optional[int]											=				tf.function(__UpperCAmelCase )
           for test_inputs in self.test_sentences:
             lowerCAmelCase__     :Any											=				tf.constant(__UpperCAmelCase )
             lowerCAmelCase__     :int											=				compiled_tokenizer(__UpperCAmelCase )
             lowerCAmelCase__     :Any											=				tf_tokenizer(__UpperCAmelCase )

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






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

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



         '''simple docstring'''

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

           lowerCAmelCase__     :Union[str, Any]											=				tf_tokenizer.get_config()
           lowerCAmelCase__     :Tuple											=				TFGPTaTokenizer.from_config(__UpperCAmelCase )
           lowerCAmelCase__     :Tuple											=				model_from_config(__UpperCAmelCase )

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






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         for tf_tokenizer in self.tf_tokenizers:
           # for the test to run
           lowerCAmelCase__     :int											=				1_2_3_1_2_3

           for max_length in [3, 5, 1_0_2_4]:
             lowerCAmelCase__     :Tuple											=				tf.convert_to_tensor([self.test_sentences[0]] )
             lowerCAmelCase__     :List[str]											=				tf_tokenizer(__UpperCAmelCase       ,					max_length=__UpperCAmelCase )

             lowerCAmelCase__     :Union[str, Any]											=				out['input_ids'].numpy().shape[1]

             assert out_length == max_length
