



'''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


lowercase_									=    ["gpt2"]
lowercase_									=    "gpt2"

if is_tf_available():






  class __A     (    tf.Module		):



   '''simple docstring'''







   def __init__(self					,  A			)       ->	List[Any]:


        """simple docstring"""



        super().__init__()
        _a           =    tokenizer
        _a           =    AutoConfig.from_pretrained(A			)
        _a           =    TFGPTaLMHeadModel.from_config(A			)



   @tf.function(input_signature=(tf.TensorSpec((None,)					,  tf.string					,  name='''text'''			),)			)
   def    a__    (self					,  A			)       ->	Optional[Any]:


        """simple docstring"""



        _a           =    self.tokenizer(A			)
        _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=A					,  attention_mask=A			)['''logits''']

        return outputs






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



 '''simple docstring'''







 def    a__    (self			)       ->	Any:


      """simple docstring"""



      super().setUp()

      _a           =    [GPTaTokenizer.from_pretrained(A			) for checkpoint in (TOKENIZER_CHECKPOINTS)]
      _a           =    [TFGPTaTokenizer.from_pretrained(A			) 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    a__    (self			)       ->	Any:


      """simple docstring"""



      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(A					,  tf.intaa			) == tf_outputs_values			)			)



 @slow
 def    a__    (self			)       ->	int:


      """simple docstring"""



      for tf_tokenizer in self.tf_tokenizers:
           _a           =    tf.function(A			)
           for test_inputs in self.test_sentences:
                _a           =    tf.constant(A			)
                _a           =    compiled_tokenizer(A			)
                _a           =    tf_tokenizer(A			)

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



 @slow
 def    a__    (self			)       ->	Any:


      """simple docstring"""



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


      """simple docstring"""



      for tf_tokenizer in self.tf_tokenizers:
           _a           =    tf.convert_to_tensor([self.test_sentences[0]]			)
           _a           =    tf_tokenizer(A			)  # Build model with some sample inputs

           _a           =    tf_tokenizer.get_config()
           _a           =    TFGPTaTokenizer.from_config(A			)
           _a           =    model_from_config(A			)

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



 @slow
 def    a__    (self			)       ->	Any:


      """simple docstring"""



      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(A					,  max_length=A			)

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

                assert out_length == max_length








'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class __A     (    A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			List[Any]		=			IFInpaintingSuperResolutionPipeline
 __lowerCamelCase       :			Tuple		=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
 __lowerCamelCase       :			Optional[Any]		=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}		)
 __lowerCamelCase       :			str		=			PipelineTesterMixin.required_optional_params - {'latents'}



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


      """simple docstring"""



      return self._get_superresolution_dummy_components()



 def    a__    (self					,  A					,  A=0			)       ->	List[Any]:


      """simple docstring"""



      if str(A			).startswith('''mps'''			):
           _a           =    torch.manual_seed(A			)
      else:
           _a           =    torch.Generator(device=A			).manual_seed(A			)

      _a           =    floats_tensor((1, 3, 16, 16)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)
      _a           =    floats_tensor((1, 3, 32, 32)					,  rng=random.Random(A			)			).to(A			)

      _a           =    {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': image,
          '''original_image''': original_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }

      return inputs



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()					,  reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,  )
 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != '''cuda'''					,  reason='''float16 requires CUDA'''			)
 def    a__    (self			)       ->	str:


      """simple docstring"""



      super().test_save_load_floataa(expected_max_diff=1E-1			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



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


      """simple docstring"""



      self._test_save_load_local()



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2					,  )



