


'''simple docstring'''

import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()


class       a__				( __A							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		Tuple	=	PriorTransformer
				__UpperCamelCase    :		Dict	=	'hidden_states'




				@property
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				4
										__lowerCAmelCase							       =				8
										__lowerCAmelCase							       =				7

										__lowerCAmelCase							       =				floats_tensor((batch_size, embedding_dim)		).to(__lowercase		)

										__lowerCAmelCase							       =				floats_tensor((batch_size, embedding_dim)		).to(__lowercase		)
										__lowerCAmelCase							       =				floats_tensor((batch_size, num_embeddings, embedding_dim)		).to(__lowercase		)

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}




				def 					_snake_case  (self       ,			__lowercase=0		):
										torch.manual_seed(__lowercase		)
										__lowerCAmelCase							       =				4
										__lowerCAmelCase							       =				8
										__lowerCAmelCase							       =				7

										__lowerCAmelCase							       =				torch.randn((batch_size, embedding_dim)		).to(__lowercase		)

										__lowerCAmelCase							       =				torch.randn((batch_size, embedding_dim)		).to(__lowercase		)
										__lowerCAmelCase							       =				torch.randn((batch_size, num_embeddings, embedding_dim)		).to(__lowercase		)

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}




				@property
				def 					_snake_case  (self		):
										return (4, 8)




				@property
				def 					_snake_case  (self		):
										return (4, 8)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				{
										    '''num_attention_heads''': 2,
										    '''attention_head_dim''': 4,
										    '''num_layers''': 2,
										    '''embedding_dim''': 8,
										    '''num_embeddings''': 7,
										    '''additional_embeddings''': 4,
										}
										__lowerCAmelCase							       =				self.dummy_input
										return init_dict, inputs_dict




				def 					_snake_case  (self		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				PriorTransformer.from_pretrained(
										    '''hf-internal-testing/prior-dummy'''       ,			output_loading_info=__lowercase		)
										self.assertIsNotNone(__lowercase		)
										self.assertEqual(len(loading_info['''missing_keys''']		)       ,			0		)

										model.to(__lowercase		)
										__lowerCAmelCase							       =				model(**self.dummy_input		)[0]

										assert hidden_states is not None, "Make sure output is not None"




				def 					_snake_case  (self		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				self.prepare_init_args_and_inputs_for_common()

										__lowerCAmelCase							       =				self.model_class(**__lowercase		)
										__lowerCAmelCase							       =				inspect.signature(model.forward		)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										__lowerCAmelCase							       =				[*signature.parameters.keys()]

										__lowerCAmelCase							       =				['''hidden_states''', '''timestep''']
										self.assertListEqual(arg_names[:2]       ,			__lowercase		)




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''		)
										__lowerCAmelCase							       =				model.to(__lowercase		)

										if hasattr(__lowercase       ,			'''set_default_attn_processor'''		):
																model.set_default_attn_processor()

										__lowerCAmelCase							       =				self.get_dummy_seed_input()

										with torch.no_grad():
																__lowerCAmelCase							       =				model(**__lowercase		)[0]

										__lowerCAmelCase							       =				output[0, :5].flatten().cpu()
										print(__lowercase		)

										# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
										# the expected output slices are not the same for CPU and GPU.
										__lowerCAmelCase							       =				torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9]		)
										self.assertTrue(torch_all_close(__lowercase       ,			__lowercase       ,			rtol=1e-2		)		)


@slow
class       a__				( unittest.TestCase      ):







				"""simple docstring"""



				def 					_snake_case  (self       ,			__lowercase=1       ,			__lowercase=7_68       ,			__lowercase=77       ,			__lowercase=0		):
										torch.manual_seed(__lowercase		)
										__lowerCAmelCase							       =				batch_size
										__lowerCAmelCase							       =				embedding_dim
										__lowerCAmelCase							       =				num_embeddings

										__lowerCAmelCase							       =				torch.randn((batch_size, embedding_dim)		).to(__lowercase		)

										__lowerCAmelCase							       =				torch.randn((batch_size, embedding_dim)		).to(__lowercase		)
										__lowerCAmelCase							       =				torch.randn((batch_size, num_embeddings, embedding_dim)		).to(__lowercase		)

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}




				def 					_snake_case  (self		):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




				@parameterized.expand(
				    [
				        # fmt: off
				        [13, [-0.5_8_6_1,  0.1_2_8_3, -0.0_9_3_1,  0.0_8_8_2,  0.4_4_7_6,  0.1_3_2_9, -0.0_4_9_8,  0.0_6_4_0]],
				        [37, [-0.4_9_1_3,  0.0_1_1_0, -0.0_4_8_3,  0.0_5_4_1,  0.4_9_5_4, -0.0_1_7_0,  0.0_3_5_4,  0.1_6_5_1]],
				        # fmt: on
				    ]		)
				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase		):
										__lowerCAmelCase							       =				PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''       ,			subfolder='''prior'''		)
										model.to(__lowercase		)
										__lowerCAmelCase							       =				self.get_dummy_seed_input(seed=__lowercase		)

										with torch.no_grad():
																__lowerCAmelCase							       =				model(**__lowercase		)[0]

										assert list(sample.shape		) == [1, 7_68]

										__lowerCAmelCase							       =				sample[0, :8].flatten().cpu()
										print(__lowercase		)
										__lowerCAmelCase							       =				torch.tensor(__lowercase		)

										assert torch_all_close(__lowercase       ,			__lowercase       ,			atol=1e-3		)







'''simple docstring'''

import numpy as np

from transformers import Pipeline

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				np.max(lowerCamelCase,				axis=-1,				keepdims=lowerCamelCase)
						__lowerCAmelCase							       =				np.exp(outputs - maxes)
						return shifted_exp / shifted_exp.sum(axis=-1,				keepdims=lowerCamelCase)




class       a__				( __A      ):







				"""simple docstring"""



				def 					_snake_case  (self       ,			**__lowercase		):
										__lowerCAmelCase							       =				{}
										if "second_text" in kwargs:
																__lowerCAmelCase							       =				kwargs['''second_text''']
										return preprocess_kwargs, {}, {}




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=None		):
										return self.tokenizer(__lowercase       ,			text_pair=__lowercase       ,			return_tensors=self.framework		)




				def 					_snake_case  (self       ,			__lowercase		):
										return self.model(**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase		):
										__lowerCAmelCase							       =				model_outputs.logits[0].numpy()
										__lowerCAmelCase							       =				softmax(__lowercase		)

										__lowerCAmelCase							       =				np.argmax(__lowercase		)
										__lowerCAmelCase							       =				self.model.config.idalabel[best_class]
										__lowerCAmelCase							       =				probabilities[best_class].item()
										__lowerCAmelCase							       =				logits.tolist()
										return {"label": label, "score": score, "logits": logits}

