
"""simple docstring"""
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_						(							UpperCAmelCase__							,							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 					A__		(      self							)    ->   Optional[int]:
													return 32




							@property
							def 					A__		(      self							)    ->   Union[str, Any]:
													return 32




							@property
							def 					A__		(      self							)    ->   Optional[Any]:
													return self.time_input_dim




							@property
							def 					A__		(      self							)    ->   Tuple:
													return self.time_input_dim * 4




							@property
							def 					A__		(      self							)    ->   Optional[int]:
													return 100




							@property
							def 					A__		(      self							)    ->   Union[str, Any]:
													__lowerCAmelCase   					=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
													return tokenizer




							@property
							def 					A__		(      self							)    ->   List[str]:
													torch.manual_seed(0							)
													__lowerCAmelCase   					=   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-0_5  ,							num_attention_heads=4  ,							num_hidden_layers=5  ,							pad_token_id=1  ,							vocab_size=1_000  ,							)
													return CLIPTextModelWithProjection(UpperCAmelCase__							)




							@property
							def 					A__		(      self							)    ->   Optional[Any]:
													torch.manual_seed(0							)

													__lowerCAmelCase   					=   {
													    """num_attention_heads""": 2,
													    """attention_head_dim""": 12,
													    """embedding_dim""": self.text_embedder_hidden_size,
													    """num_layers""": 1,
													}

													__lowerCAmelCase   					=   PriorTransformer(**UpperCAmelCase__							)
													# 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   					=   nn.Parameter(torch.ones(model.clip_std.shape							)							)
													return model




							@property
							def 					A__		(      self							)    ->   int:
													torch.manual_seed(0							)
													__lowerCAmelCase   					=   CLIPVisionConfig(
													    hidden_size=self.text_embedder_hidden_size  ,							image_size=224  ,							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   					=   CLIPVisionModelWithProjection(UpperCAmelCase__							)
													return model




							@property
							def 					A__		(      self							)    ->   Tuple:
													__lowerCAmelCase   					=   CLIPImageProcessor(
													    crop_size=224  ,							do_center_crop=UpperCAmelCase__  ,							do_normalize=UpperCAmelCase__  ,							do_resize=UpperCAmelCase__  ,							image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]  ,							image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]  ,							resample=3  ,							size=224  ,							)

													return image_processor




							def 					A__		(      self							)    ->   Tuple:
													__lowerCAmelCase   					=   self.dummy_prior
													__lowerCAmelCase   					=   self.dummy_image_encoder
													__lowerCAmelCase   					=   self.dummy_text_encoder
													__lowerCAmelCase   					=   self.dummy_tokenizer
													__lowerCAmelCase   					=   self.dummy_image_processor

													__lowerCAmelCase   					=   UnCLIPScheduler(
													    variance_type="""fixed_small_log"""  ,							prediction_type="""sample"""  ,							num_train_timesteps=1_000  ,							clip_sample=UpperCAmelCase__  ,							clip_sample_range=10.0  ,							)

													__lowerCAmelCase   					=   {
													    """prior""": prior,
													    """image_encoder""": image_encoder,
													    """text_encoder""": text_encoder,
													    """tokenizer""": tokenizer,
													    """scheduler""": scheduler,
													    """image_processor""": image_processor,
													}

													return components




							def 					A__		(      self  ,							snake_case_  ,							snake_case_=0							)    ->   str:

													if str(UpperCAmelCase__							).startswith("""mps"""							):
																			__lowerCAmelCase   					=   torch.manual_seed(UpperCAmelCase__							)
													else:
																			__lowerCAmelCase   					=   torch.Generator(device=UpperCAmelCase__							).manual_seed(UpperCAmelCase__							)
													__lowerCAmelCase   					=   {
													    """prompt""": """horse""",
													    """generator""": generator,
													    """guidance_scale""": 4.0,
													    """num_inference_steps""": 2,
													    """output_type""": """np""",
													}
													return inputs




							def 					A__		(      self							)    ->   Tuple:
													__lowerCAmelCase   					=   """cpu"""

													__lowerCAmelCase   					=   self.get_dummy_components()

													__lowerCAmelCase   					=   self.pipeline_class(**UpperCAmelCase__							)
													__lowerCAmelCase   					=   pipe.to(UpperCAmelCase__							)

													pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

													__lowerCAmelCase   					=   pipe(**self.get_dummy_inputs(UpperCAmelCase__							)							)
													__lowerCAmelCase   					=   output.image_embeds

													__lowerCAmelCase   					=   pipe(
													    **self.get_dummy_inputs(UpperCAmelCase__							)  ,							return_dict=UpperCAmelCase__  ,							)[0]

													__lowerCAmelCase   					=   image[0, -10:]
													__lowerCAmelCase   					=   image_from_tuple[0, -10:]

													assert image.shape == (1, 32)

													__lowerCAmelCase   					=   np.array(
													    [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]							)

													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 					A__		(      self							)    ->   Optional[Any]:
													__lowerCAmelCase   					=   torch_device == """cpu"""
													__lowerCAmelCase   					=   True
													__lowerCAmelCase   					=   False

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




							@skip_mps
							def 					A__		(      self							)    ->   List[str]:
													__lowerCAmelCase   					=   torch_device == """cpu"""
													__lowerCAmelCase   					=   False

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






import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCamelCase	:		List[Any]							       =   """sshleifer/bart-tiny-random"""
_lowerCamelCase	:		List[Any]							       =   """patrickvonplaten/t5-tiny-random"""


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           return AutoConfig.from_pretrained(UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.num_hidden_layers					, 1)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)


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





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, self.teacher_config.encoder_layers)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, 1)





    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->List[Any]:





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=UpperCAmelCase__					, d=UpperCAmelCase__)


