




"""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 lowerCAmelCase__      (	tf.Module		):






						'''simple docstring'''



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






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

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

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

										return outputs






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






			'''simple docstring'''



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

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

							_lowerCamelCase	:     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""",
							]
							_lowerCamelCase	:     Tuple       =       list(zip(self.test_sentences    , self.test_sentences[::-1]    )    )
			def    A_       (    self    ):
							for tokenizer, tf_tokenizer in zip(self.tokenizers    , self.tf_tokenizers    ):
											for test_inputs in self.test_sentences:
															_lowerCamelCase	:     List[Any]       =       tokenizer([test_inputs]    , return_tensors='tf'    )
															_lowerCamelCase	:     Optional[Any]       =       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[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    A_       (    self    ):
							for tf_tokenizer in self.tf_tokenizers:
											_lowerCamelCase	:     Any       =       tf.function(__lowerCAmelCase    )
											for test_inputs in self.test_sentences:
															_lowerCamelCase	:     Tuple       =       tf.constant(__lowerCAmelCase    )
															_lowerCamelCase	:     Dict       =       compiled_tokenizer(__lowerCAmelCase    )
															_lowerCamelCase	:     str       =       tf_tokenizer(__lowerCAmelCase    )

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

											_lowerCamelCase	:     Union[str, Any]       =       tf_tokenizer.get_config()
											_lowerCamelCase	:     int       =       TFGPTaTokenizer.from_config(__lowerCAmelCase    )
											_lowerCamelCase	:     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    A_       (    self    ):
							for tf_tokenizer in self.tf_tokenizers:
											# for the test to run
											_lowerCamelCase	:     Optional[int]       =       123123

											for max_length in [3, 5, 1024]:
															_lowerCamelCase	:     List[str]       =       tf.convert_to_tensor([self.test_sentences[0]]    )
															_lowerCamelCase	:     Tuple       =       tf_tokenizer(__lowerCAmelCase    , max_length=__lowerCAmelCase    )

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

															assert out_length == max_length







'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: int            =				UNetaDModel(
           block_out_channels=(32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")      ,  up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")      ,  )
       return model





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_uncond_unet
       UpperCamelCase_					: List[str]            =				PNDMScheduler()

       UpperCamelCase_					: Dict            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""      ,  return_dict=__lowerCAmelCase			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: int            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Optional[int]            =				np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2






@slow
@require_torch
class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				"""google/ddpm-cifar10-32"""

       UpperCamelCase_					: List[str]            =				UNetaDModel.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: int            =				PNDMScheduler()

       UpperCamelCase_					: Any            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Tuple            =				image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Union[str, Any]            =				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2






