




"""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     lowercase_	(					tf.Module							):



        '''simple docstring'''

        def __init__(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Union[str, Any]		):
              super().__init__()
              _A        =       tokenizer
              _A        =       AutoConfig.from_pretrained(_UpperCAmelCase		)
              _A        =       TFGPTaLMHeadModel.from_config(_UpperCAmelCase		)



        @tf.function(input_signature=(tf.TensorSpec((None,)   ,							tf.string   ,							name='text'		),)		)
        def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Dict		):
              _A        =       self.tokenizer(_UpperCAmelCase		)
              _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=_UpperCAmelCase   ,							attention_mask=_UpperCAmelCase		)['logits']

              return outputs





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



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       super().setUp()

       _A        =       [GPTaTokenizer.from_pretrained(_UpperCAmelCase		) for checkpoint in (TOKENIZER_CHECKPOINTS)]
       _A        =       [TFGPTaTokenizer.from_pretrained(_UpperCAmelCase		) 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       lowerCAmelCase_							(			self	:	Dict		):
       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(_UpperCAmelCase   ,							tf.intaa		) == tf_outputs_values		)		)

 @slow
 def       lowerCAmelCase_							(			self	:	Dict		):
       for tf_tokenizer in self.tf_tokenizers:
             _A        =       tf.function(_UpperCAmelCase		)
             for test_inputs in self.test_sentences:
                   _A        =       tf.constant(_UpperCAmelCase		)
                   _A        =       compiled_tokenizer(_UpperCAmelCase		)
                   _A        =       tf_tokenizer(_UpperCAmelCase		)

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

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

             _A        =       tf_tokenizer.get_config()
             _A        =       TFGPTaTokenizer.from_config(_UpperCAmelCase		)
             _A        =       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	:	Optional[int]		):
       for tf_tokenizer in self.tf_tokenizers:
             # for the test to run
             _A        =       123_123

             for max_length in [3, 5, 1_024]:
                   _A        =       tf.convert_to_tensor([self.test_sentences[0]]		)
                   _A        =       tf_tokenizer(_UpperCAmelCase   ,							max_length=_UpperCAmelCase		)

                   _A        =       out['input_ids'].numpy().shape[1]

                   assert out_length == max_length







"""simple docstring"""

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


a         =      HUGGINGFACE_HUB_CACHE


a         =      '''config.json'''
a         =      '''diffusion_pytorch_model.bin'''
a         =      '''diffusion_flax_model.msgpack'''
a         =      '''model.onnx'''
a         =      '''diffusion_pytorch_model.safetensors'''
a         =      '''weights.pb'''
a         =      '''https://huggingface.co'''
a         =      default_cache_path
a         =      '''diffusers_modules'''
a         =      os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
a         =      ['''fp16''', '''non-ema''']
a         =      '''.self_attn'''

