



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


__snake_case    :		Tuple  =['gpt2']
__snake_case    :		List[Any]  ='gpt2'

if is_tf_available():



							class 	lowerCamelCase__      (							tf.Module):




									'''simple docstring'''



									def __init__(self		,__lowerCamelCase			)					->      List[str]:

													"""simple docstring"""






													super().__init__()
													lowerCAmelCase__					:    Any												=					tokenizer
													lowerCAmelCase__					:    Tuple												=					AutoConfig.from_pretrained(__lowercase			)
													lowerCAmelCase__					:    Optional[Any]												=					TFGPTaLMHeadModel.from_config(__lowercase			)



									@tf.function(input_signature=(tf.TensorSpec((None,)		,tf.string		,name='''text'''			),)			)
									def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Union[str, Any]:

													"""simple docstring"""






													lowerCAmelCase__					:    Tuple												=					self.tokenizer(__lowercase			)
													lowerCAmelCase__					:    int												=					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__					:    int												=					self.model(input_ids=__lowercase		,attention_mask=__lowercase			)['''logits''']

													return outputs



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




		'''simple docstring'''



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						super().setUp()

						lowerCAmelCase__					:    Tuple												=					[GPTaTokenizer.from_pretrained(__lowercase			) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						lowerCAmelCase__					:    List[str]												=					[TFGPTaTokenizer.from_pretrained(__lowercase			) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers			) == len(self.tf_tokenizers			)

						lowerCAmelCase__					:    List[Any]												=					[
						    '''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__					:    Optional[Any]												=					list(zip(self.test_sentences		,self.test_sentences[::-1]			)			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






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

														for key in python_outputs.keys():
																		# convert them to numpy to avoid messing with ragged tensors
																		lowerCAmelCase__					:    Optional[Any]												=					python_outputs[key].numpy()
																		lowerCAmelCase__					:    Optional[int]												=					tf_outputs[key].numpy()

																		self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape			)			)
																		self.assertTrue(tf.reduce_all(tf.cast(__lowercase		,tf.intaa			) == tf_outputs_values			)			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						for tf_tokenizer in self.tf_tokenizers:
										lowerCAmelCase__					:    Dict												=					tf.function(__lowercase			)
										for test_inputs in self.test_sentences:
														lowerCAmelCase__					:    List[str]												=					tf.constant(__lowercase			)
														lowerCAmelCase__					:    Optional[int]												=					compiled_tokenizer(__lowercase			)
														lowerCAmelCase__					:    str												=					tf_tokenizer(__lowercase			)

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



		@slow
		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






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

						"""simple docstring"""






						for tf_tokenizer in self.tf_tokenizers:
										lowerCAmelCase__					:    str												=					tf.convert_to_tensor([self.test_sentences[0]]			)
										lowerCAmelCase__					:    Dict												=					tf_tokenizer(__lowercase			)  # Build model with some sample inputs

										lowerCAmelCase__					:    Dict												=					tf_tokenizer.get_config()
										lowerCAmelCase__					:    Tuple												=					TFGPTaTokenizer.from_config(__lowercase			)
										lowerCAmelCase__					:    str												=					model_from_config(__lowercase			)

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



		@slow
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						for tf_tokenizer in self.tf_tokenizers:
										# for the test to run
										lowerCAmelCase__					:    Union[str, Any]												=					12_31_23

										for max_length in [3, 5, 10_24]:
														lowerCAmelCase__					:    Optional[int]												=					tf.convert_to_tensor([self.test_sentences[0]]			)
														lowerCAmelCase__					:    Tuple												=					tf_tokenizer(__lowercase		,max_length=__lowercase			)

														lowerCAmelCase__					:    Any												=					out['''input_ids'''].numpy().shape[1]

														assert out_length == max_length






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =KandinskyVaaPriorPipeline
		snake_case_     =["""prompt"""]
		snake_case_     =["""prompt""", """negative_prompt"""]
		snake_case_     =[
		    """num_images_per_prompt""",
		    """generator""",
		    """num_inference_steps""",
		    """latents""",
		    """negative_prompt""",
		    """guidance_scale""",
		    """output_type""",
		    """return_dict""",
		]
		snake_case_     =False



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						return 32



		@property
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						return self.time_input_dim



		@property
		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						return self.time_input_dim * 4



		@property
		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						return 1_00



		@property
		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
						return tokenizer



		@property
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    str												=					CLIPTextConfig(
						    bos_token_id=0		,eos_token_id=2		,hidden_size=self.text_embedder_hidden_size		,projection_dim=self.text_embedder_hidden_size		,intermediate_size=37		,layer_norm_eps=1e-05		,num_attention_heads=4		,num_hidden_layers=5		,pad_token_id=1		,vocab_size=10_00		,)
						return CLIPTextModelWithProjection(__lowerCamelCase			)



		@property
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						torch.manual_seed(0			)

						lowerCAmelCase__					:    List[str]												=					{
						    '''num_attention_heads''': 2,
						    '''attention_head_dim''': 12,
						    '''embedding_dim''': self.text_embedder_hidden_size,
						    '''num_layers''': 1,
						}

						lowerCAmelCase__					:    List[str]												=					PriorTransformer(**__lowerCamelCase			)
						# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
						lowerCAmelCase__					:    int												=					nn.Parameter(torch.ones(model.clip_std.shape			)			)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Any												=					CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size		,image_size=2_24		,projection_dim=self.text_embedder_hidden_size		,intermediate_size=37		,num_attention_heads=4		,num_channels=3		,num_hidden_layers=5		,patch_size=14		,)

						lowerCAmelCase__					:    int												=					CLIPVisionModelWithProjection(__lowerCamelCase			)
						return model



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					CLIPImageProcessor(
						    crop_size=2_24		,do_center_crop=__lowerCamelCase		,do_normalize=__lowerCamelCase		,do_resize=__lowerCamelCase		,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]		,image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]		,resample=3		,size=2_24		,)

						return image_processor



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					self.dummy_prior
						lowerCAmelCase__					:    List[Any]												=					self.dummy_image_encoder
						lowerCAmelCase__					:    Optional[Any]												=					self.dummy_text_encoder
						lowerCAmelCase__					:    Optional[int]												=					self.dummy_tokenizer
						lowerCAmelCase__					:    str												=					self.dummy_image_processor

						lowerCAmelCase__					:    Union[str, Any]												=					UnCLIPScheduler(
						    variance_type='''fixed_small_log'''		,prediction_type='''sample'''		,num_train_timesteps=10_00		,clip_sample=__lowerCamelCase		,clip_sample_range=10.0		,)

						lowerCAmelCase__					:    int												=					{
						    '''prior''': prior,
						    '''image_encoder''': image_encoder,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''scheduler''': scheduler,
						    '''image_processor''': image_processor,
						}

						return components



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      int:

						"""simple docstring"""






						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    Dict												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    Any												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					{
						    '''prompt''': '''horse''',
						    '''generator''': generator,
						    '''guidance_scale''': 4.0,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''np''',
						}
						return inputs



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					'''cpu'''

						lowerCAmelCase__					:    Optional[int]												=					self.get_dummy_components()

						lowerCAmelCase__					:    int												=					self.pipeline_class(**__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					pipe.to(__lowerCamelCase			)

						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					pipe(**self.get_dummy_inputs(__lowerCamelCase			)			)
						lowerCAmelCase__					:    Union[str, Any]												=					output.image_embeds

						lowerCAmelCase__					:    Tuple												=					pipe(
						    **self.get_dummy_inputs(__lowerCamelCase			)		,return_dict=__lowerCamelCase		,)[0]

						lowerCAmelCase__					:    Union[str, Any]												=					image[0, -10:]
						lowerCAmelCase__					:    str												=					image_from_tuple[0, -10:]

						assert image.shape == (1, 32)

						lowerCAmelCase__					:    int												=					np.array(
						    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

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



		@skip_mps
		def 		lowerCAmelCase__				(self			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    List[Any]												=					True
						lowerCAmelCase__					:    Optional[int]												=					False

						self._test_inference_batch_single_identical(
						    test_max_difference=__lowerCamelCase		,relax_max_difference=__lowerCamelCase		,test_mean_pixel_difference=__lowerCamelCase		,)



		@skip_mps
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					torch_device == '''cpu'''
						lowerCAmelCase__					:    int												=					False

						self._test_attention_slicing_forward_pass(
						    test_max_difference=__lowerCamelCase		,test_mean_pixel_difference=__lowerCamelCase		,)

