
'''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    :							Dict      =					["gpt2"]
lowercase    :							Any      =					"gpt2"

if is_tf_available():


					class 						__UpperCAmelCase      (					tf.Module						):


												def __init__(       self    ,   lowerCAmelCase_  ):





													"""simple docstring"""






													super().__init__()
													_snake_case   						=		tokenizer
													_snake_case   						=		AutoConfig.from_pretrained(lowerCAmelCase_  )
													_snake_case   						=		TFGPTaLMHeadModel.from_config(lowerCAmelCase_  )




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





													"""simple docstring"""






													_snake_case   						=		self.tokenizer(lowerCAmelCase_  )
													_snake_case   						=		tokenized['input_ids'].to_tensor()

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

													_snake_case   						=		self.model(input_ids=lowerCAmelCase_    ,   attention_mask=lowerCAmelCase_  )['logits']

													return outputs


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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								super().setUp()

								_snake_case   						=		[GPTaTokenizer.from_pretrained(lowerCAmelCase_  ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								_snake_case   						=		[TFGPTaTokenizer.from_pretrained(lowerCAmelCase_  ) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers  ) == len(self.tf_tokenizers  )

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

										for key in python_outputs.keys():
											# convert them to numpy to avoid messing with ragged tensors
											_snake_case   						=		python_outputs[key].numpy()
											_snake_case   						=		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  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

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


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for tf_tokenizer in self.tf_tokenizers:
									_snake_case   						=		ModelToSave(tokenizer=lowerCAmelCase_  )
									_snake_case   						=		tf.convert_to_tensor([self.test_sentences[0]]  )
									_snake_case   						=		model.serving(lowerCAmelCase_  )  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
										_snake_case   						=		Path(lowerCAmelCase_  ) / 'saved.model'
										tf.saved_model.save(lowerCAmelCase_    ,   lowerCAmelCase_    ,   signatures={'serving_default': model.serving}  )
										_snake_case   						=		tf.saved_model.load(lowerCAmelCase_  )
									_snake_case   						=		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  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

									_snake_case   						=		tf_tokenizer.get_config()
									_snake_case   						=		TFGPTaTokenizer.from_config(lowerCAmelCase_  )
									_snake_case   						=		model_from_config(lowerCAmelCase_  )

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




							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									_snake_case   						=		12_31_23

									for max_length in [3, 5, 10_24]:
										_snake_case   						=		tf.convert_to_tensor([self.test_sentences[0]]  )
										_snake_case   						=		tf_tokenizer(lowerCAmelCase_    ,   max_length=lowerCAmelCase_  )

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

										assert out_length == max_length





"""simple docstring"""





import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard



@slow
@require_flax
class 						snake_case_(				unittest.TestCase	):






     def    lowerCamelCase__   (		self     :    int ):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      FlaxStableDiffusionPipeline.from_pretrained(
               '''stabilityai/stable-diffusion-2'''    , revision='''bf16'''    , dtype=jnp.bfloataa    , )

           lowerCAmelCase    :		Optional[int]				=      '''A painting of a squirrel eating a burger'''
           lowerCAmelCase    :		List[str]				=      jax.device_count()
           lowerCAmelCase    :		Optional[int]				=      num_samples * [prompt]
           lowerCAmelCase    :		Any				=      sd_pipe.prepare_inputs(UpperCamelCase_ )

           lowerCAmelCase    :		Optional[int]				=      replicate(UpperCamelCase_ )
           lowerCAmelCase    :		Union[str, Any]				=      shard(UpperCamelCase_ )

           lowerCAmelCase    :		Optional[int]				=      jax.random.PRNGKey(0 )
           lowerCAmelCase    :		Optional[Any]				=      jax.random.split(UpperCamelCase_    , jax.device_count() )

           lowerCAmelCase    :		str				=      sd_pipe(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , num_inference_steps=2_5    , jit=UpperCamelCase_ )[0]
           assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

           lowerCAmelCase    :		str				=      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
           lowerCAmelCase    :		List[str]				=      images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

           lowerCAmelCase    :		Dict				=      jnp.asarray(jax.device_get(image_slice.flatten() ) )
           lowerCAmelCase    :		List[str]				=      jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512] )
           print(F'''output_slice: {output_slice}''' )
           assert jnp.abs(output_slice - expected_slice ).max() < 1E-2






     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           lowerCAmelCase    :		Union[str, Any]				=      '''stabilityai/stable-diffusion-2'''
           lowerCAmelCase,					lowerCAmelCase    :		Dict				=      FlaxDPMSolverMultistepScheduler.from_pretrained(UpperCamelCase_    , subfolder='''scheduler''' )
           lowerCAmelCase,					lowerCAmelCase    :		int				=      FlaxStableDiffusionPipeline.from_pretrained(
               UpperCamelCase_    , scheduler=UpperCamelCase_    , revision='''bf16'''    , dtype=jnp.bfloataa    , )
           lowerCAmelCase    :		List[Any]				=      scheduler_params

           lowerCAmelCase    :		List[Any]				=      '''A painting of a squirrel eating a burger'''
           lowerCAmelCase    :		Any				=      jax.device_count()
           lowerCAmelCase    :		int				=      num_samples * [prompt]
           lowerCAmelCase    :		int				=      sd_pipe.prepare_inputs(UpperCamelCase_ )

           lowerCAmelCase    :		Dict				=      replicate(UpperCamelCase_ )
           lowerCAmelCase    :		Tuple				=      shard(UpperCamelCase_ )

           lowerCAmelCase    :		int				=      jax.random.PRNGKey(0 )
           lowerCAmelCase    :		Optional[int]				=      jax.random.split(UpperCamelCase_    , jax.device_count() )

           lowerCAmelCase    :		Tuple				=      sd_pipe(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , num_inference_steps=2_5    , jit=UpperCamelCase_ )[0]
           assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

           lowerCAmelCase    :		Any				=      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
           lowerCAmelCase    :		str				=      images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

           lowerCAmelCase    :		Optional[int]				=      jnp.asarray(jax.device_get(image_slice.flatten() ) )
           lowerCAmelCase    :		Tuple				=      jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297] )
           print(F'''output_slice: {output_slice}''' )
           assert jnp.abs(output_slice - expected_slice ).max() < 1E-2


