






'''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      A     (   tf.Module    ):






       """simple docstring"""





       def __init__(				self      ,  __lowerCAmelCase			):
             super().__init__()
             UpperCamelCase_					: List[str]            =				tokenizer
             UpperCamelCase_					: List[str]            =				AutoConfig.from_pretrained(__lowerCAmelCase			)
             UpperCamelCase_					: Optional[Any]            =				TFGPTaLMHeadModel.from_config(__lowerCAmelCase			)





       @tf.function(input_signature=(tf.TensorSpec((None,)      ,  tf.string      ,  name="""text"""			),)			)
       def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
             UpperCamelCase_					: Optional[Any]            =				self.tokenizer(__lowerCAmelCase			)
             UpperCamelCase_					: List[str]            =				tokenized["""input_ids"""].to_tensor()

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

             UpperCamelCase_					: List[Any]            =				self.model(input_ids=__lowerCAmelCase      ,  attention_mask=__lowerCAmelCase			)["""logits"""]

             return outputs




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






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       super().setUp()

       UpperCamelCase_					: Union[str, Any]            =				[GPTaTokenizer.from_pretrained(__lowerCAmelCase			) for checkpoint in (TOKENIZER_CHECKPOINTS)]
       UpperCamelCase_					: Dict            =				[TFGPTaTokenizer.from_pretrained(__lowerCAmelCase			) for checkpoint in TOKENIZER_CHECKPOINTS]
       assert len(self.tokenizers			) == len(self.tf_tokenizers			)

       UpperCamelCase_					: Dict            =				[
           """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""",
       ]
       UpperCamelCase_					: Tuple            =				list(zip(self.test_sentences      ,  self.test_sentences[::-1]			)			)
 def 	_UpperCAmelCase							(				self			):
       for tokenizer, tf_tokenizer in zip(self.tokenizers      ,  self.tf_tokenizers			):
             for test_inputs in self.test_sentences:
                   UpperCamelCase_					: List[Any]            =				tokenizer([test_inputs]      ,  return_tensors="""tf"""			)
                   UpperCamelCase_					: Optional[Any]            =				tf_tokenizer([test_inputs]			)

                   for key in python_outputs.keys():
                         # convert them to numpy to avoid messing with ragged tensors
                         UpperCamelCase_					: Optional[int]            =				python_outputs[key].numpy()
                         UpperCamelCase_					: List[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(__lowerCAmelCase      ,  tf.intaa			) == tf_outputs_values			)			)
 @slow
 def 	_UpperCAmelCase							(				self			):
       for tf_tokenizer in self.tf_tokenizers:
             UpperCamelCase_					: Any            =				tf.function(__lowerCAmelCase			)
             for test_inputs in self.test_sentences:
                   UpperCamelCase_					: Tuple            =				tf.constant(__lowerCAmelCase			)
                   UpperCamelCase_					: Dict            =				compiled_tokenizer(__lowerCAmelCase			)
                   UpperCamelCase_					: str            =				tf_tokenizer(__lowerCAmelCase			)

                   for key in eager_outputs.keys():
                         self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]			)			)
 @slow
 def 	_UpperCAmelCase							(				self			):
       for tf_tokenizer in self.tf_tokenizers:
             UpperCamelCase_					: Optional[Any]            =				ModelToSave(tokenizer=__lowerCAmelCase			)
             UpperCamelCase_					: Dict            =				tf.convert_to_tensor([self.test_sentences[0]]			)
             UpperCamelCase_					: Optional[Any]            =				model.serving(__lowerCAmelCase			)  # Build model with some sample inputs
             with TemporaryDirectory() as tempdir:
                   UpperCamelCase_					: str            =				Path(__lowerCAmelCase			) / """saved.model"""
                   tf.saved_model.save(__lowerCAmelCase      ,  __lowerCAmelCase      ,  signatures={"""serving_default""": model.serving}			)
                   UpperCamelCase_					: int            =				tf.saved_model.load(__lowerCAmelCase			)
             UpperCamelCase_					: Any            =				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			):
       for tf_tokenizer in self.tf_tokenizers:
             UpperCamelCase_					: Optional[int]            =				tf.convert_to_tensor([self.test_sentences[0]]			)
             UpperCamelCase_					: Tuple            =				tf_tokenizer(__lowerCAmelCase			)  # Build model with some sample inputs

             UpperCamelCase_					: Union[str, Any]            =				tf_tokenizer.get_config()
             UpperCamelCase_					: int            =				TFGPTaTokenizer.from_config(__lowerCAmelCase			)
             UpperCamelCase_					: Tuple            =				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			):
       for tf_tokenizer in self.tf_tokenizers:
             # for the test to run
             UpperCamelCase_					: Optional[int]            =				12_31_23

             for max_length in [3, 5, 10_24]:
                   UpperCamelCase_					: List[str]            =				tf.convert_to_tensor([self.test_sentences[0]]			)
                   UpperCamelCase_					: Tuple            =				tf_tokenizer(__lowerCAmelCase      ,  max_length=__lowerCAmelCase			)

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

                   assert out_length == max_length














'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    UNetaDModel
 __a      :    ScoreSdeVeScheduler
 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       super().__init__()
       self.register_modules(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)





 @torch.no_grad()
 def __call__(				self      ,  __lowerCAmelCase = 1      ,  __lowerCAmelCase = 20_00      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = "pil"      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: str            =				self.unet.config.sample_size
       UpperCamelCase_					: int            =				(batch_size, 3, img_size, img_size)

       UpperCamelCase_					: List[Any]            =				self.unet

       UpperCamelCase_					: Union[str, Any]            =				randn_tensor(__lowerCAmelCase      ,  generator=__lowerCAmelCase			) * self.scheduler.init_noise_sigma
       UpperCamelCase_					: Union[str, Any]            =				sample.to(self.device			)

       self.scheduler.set_timesteps(__lowerCAmelCase			)
       self.scheduler.set_sigmas(__lowerCAmelCase			)

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
             UpperCamelCase_					: Union[str, Any]            =				self.scheduler.sigmas[i] * torch.ones(shape[0]      ,  device=self.device			)

             # correction step
             for _ in range(self.scheduler.config.correct_steps			):
                   UpperCamelCase_					: List[str]            =				self.unet(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
                   UpperCamelCase_					: List[str]            =				self.scheduler.step_correct(__lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			).prev_sample

             # prediction step
             UpperCamelCase_					: List[Any]            =				model(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
             UpperCamelCase_					: Tuple            =				self.scheduler.step_pred(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			)

             UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				output.prev_sample, output.prev_sample_mean

       UpperCamelCase_					: Tuple            =				sample_mean.clamp(0      ,  1			)
       UpperCamelCase_					: str            =				sample.cpu().permute(0      ,  2      ,  3      ,  1			).numpy()
       if output_type == "pil":
             UpperCamelCase_					: Dict            =				self.numpy_to_pil(__lowerCAmelCase			)

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=__lowerCAmelCase			)






