

'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 					lowercase						(  a_,					unittest.TestCase ):
							_lowerCamelCase		:      List[str]=   KandinskyVaaImgaImgPipeline
							_lowerCamelCase		:      Union[str, Any]=   ["image_embeds", "negative_image_embeds", "image"]
							_lowerCamelCase		:      Optional[Any]=   [
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							]
							_lowerCamelCase		:      Union[str, Any]=   [
							    "generator",
							    "height",
							    "width",
							    "strength",
							    "guidance_scale",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							_lowerCamelCase		:      int=   False


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


							@property
							def 						_snake_case							(			self)							->				int:
								return 32


							@property
							def 						_snake_case							(			self)							->				Optional[int]:
								return self.time_input_dim


							@property
							def 						_snake_case							(			self)							->				List[str]:
								return self.time_input_dim * 4


							@property
							def 						_snake_case							(			self)							->				int:
								return 100


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

								UpperCAmelCase_		:      Dict       	=					{
								    'in_channels': 4,
								    # Out channels is double in channels because predicts mean and variance
								    'out_channels': 8,
								    'addition_embed_type': 'image',
								    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
								    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
								    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
								    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
								    'layers_per_block': 1,
								    'encoder_hid_dim': self.text_embedder_hidden_size,
								    'encoder_hid_dim_type': 'image_proj',
								    'cross_attention_dim': self.cross_attention_dim,
								    'attention_head_dim': 4,
								    'resnet_time_scale_shift': 'scale_shift',
								    'class_embed_type': None,
								}

								UpperCAmelCase_		:      int       	=					UNetaDConditionModel(**_snake_case)
								return model


							@property
							def 						_snake_case							(			self)							->				List[str]:
								return {
								    "block_out_channels": [32, 64],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 12,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}


							@property
							def 						_snake_case							(			self)							->				str:
								torch.manual_seed(0)
								UpperCAmelCase_		:      Tuple       	=					VQModel(**self.dummy_movq_kwargs)
								return model


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      List[Any]       	=					self.dummy_unet
								UpperCAmelCase_		:      int       	=					self.dummy_movq

								UpperCAmelCase_		:      Tuple       	=					{
								    'num_train_timesteps': 1000,
								    'beta_schedule': 'linear',
								    'beta_start': 0.00_085,
								    'beta_end': 0.012,
								    'clip_sample': False,
								    'set_alpha_to_one': False,
								    'steps_offset': 0,
								    'prediction_type': 'epsilon',
								    'thresholding': False,
								}

								UpperCAmelCase_		:      Optional[Any]       	=					DDIMScheduler(**_snake_case)

								UpperCAmelCase_		:      Optional[int]       	=					{
								    'unet': unet,
								    'scheduler': scheduler,
								    'movq': movq,
								}

								return components


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=0)							->				Optional[int]:
								UpperCAmelCase_		:      Tuple       	=					floats_tensor((1, self.text_embedder_hidden_size)    ,			rng=random.Random(_snake_case)).to(_snake_case)
								UpperCAmelCase_		:      List[str]       	=					floats_tensor((1, self.text_embedder_hidden_size)    ,			rng=random.Random(seed + 1)).to(
								    _snake_case)
								# create init_image
								UpperCAmelCase_		:      Dict       	=					floats_tensor((1, 3, 64, 64)    ,			rng=random.Random(_snake_case)).to(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					image.cpu().permute(0    ,			2    ,			3    ,			1)[0]
								UpperCAmelCase_		:      Optional[Any]       	=					Image.fromarray(np.uinta(_snake_case)).convert('RGB').resize((256, 256))

								if str(_snake_case).startswith('mps'):
									UpperCAmelCase_		:      int       	=					torch.manual_seed(_snake_case)
								else:
									UpperCAmelCase_		:      Optional[Any]       	=					torch.Generator(device=_snake_case).manual_seed(_snake_case)
								UpperCAmelCase_		:      str       	=					{
								    'image': init_image,
								    'image_embeds': image_embeds,
								    'negative_image_embeds': negative_image_embeds,
								    'generator': generator,
								    'height': 64,
								    'width': 64,
								    'num_inference_steps': 10,
								    'guidance_scale': 7.0,
								    'strength': 0.2,
								    'output_type': 'np',
								}
								return inputs





							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Optional[int]       	=					'cpu'

								UpperCAmelCase_		:      Tuple       	=					self.get_dummy_components()

								UpperCAmelCase_		:      List[str]       	=					self.pipeline_class(**_snake_case)
								UpperCAmelCase_		:      List[str]       	=					pipe.to(_snake_case)

								pipe.set_progress_bar_config(disable=_snake_case)

								UpperCAmelCase_		:      int       	=					pipe(**self.get_dummy_inputs(_snake_case))
								UpperCAmelCase_		:      int       	=					output.images

								UpperCAmelCase_		:      List[str]       	=					pipe(
								    **self.get_dummy_inputs(_snake_case)    ,			return_dict=_snake_case    ,			)[0]

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

								assert image.shape == (1, 64, 64, 3)

								UpperCAmelCase_		:      Tuple       	=					np.array(
								    [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263])
								assert (
								    np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
								), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
								), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""



@slow
@require_torch_gpu
class 					lowercase						(  unittest.TestCase ):


							def 						_snake_case							(			self)							->				Optional[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      List[Any]       	=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/kandinskyv22/kandinskyv22_img2img_frog.npy')

								UpperCAmelCase_		:      int       	=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png')
								UpperCAmelCase_		:      Tuple       	=					'A red cartoon frog, 4k'

								UpperCAmelCase_		:      List[Any]       	=					KandinskyVaaPriorPipeline.from_pretrained(
								    'kandinsky-community/kandinsky-2-2-prior'    ,			torch_dtype=torch.floataa)
								pipe_prior.to(_snake_case)

								UpperCAmelCase_		:      int       	=					KandinskyVaaImgaImgPipeline.from_pretrained(
								    'kandinsky-community/kandinsky-2-2-decoder'    ,			torch_dtype=torch.floataa)
								UpperCAmelCase_		:      Optional[int]       	=					pipeline.to(_snake_case)

								pipeline.set_progress_bar_config(disable=_snake_case)

								UpperCAmelCase_		:      str       	=					torch.Generator(device='cpu').manual_seed(0)
								UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					pipe_prior(
								    _snake_case    ,			generator=_snake_case    ,			num_inference_steps=5    ,			negative_prompt=''    ,			).to_tuple()

								UpperCAmelCase_		:      Any       	=					pipeline(
								    image=_snake_case    ,			image_embeds=_snake_case    ,			negative_image_embeds=_snake_case    ,			generator=_snake_case    ,			num_inference_steps=100    ,			height=768    ,			width=768    ,			strength=0.2    ,			output_type='np'    ,			)

								UpperCAmelCase_		:      List[Any]       	=					output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(_snake_case    ,			_snake_case)









'''simple docstring'''


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case=13    ,			_snake_case=7    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=99    ,			_snake_case=32    ,			_snake_case=5    ,			_snake_case=4    ,			_snake_case=37    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=512    ,			_snake_case=16    ,			_snake_case=2    ,			_snake_case=0.02    ,			_snake_case=3    ,			_snake_case=4    ,			_snake_case=None    ,			)							->				str:
								UpperCAmelCase_		:      Dict       	=					parent
								UpperCAmelCase_		:      Tuple       	=					batch_size
								UpperCAmelCase_		:      Tuple       	=					seq_length
								UpperCAmelCase_		:      Dict       	=					is_training
								UpperCAmelCase_		:      Tuple       	=					use_input_mask
								UpperCAmelCase_		:      List[Any]       	=					use_token_type_ids
								UpperCAmelCase_		:      Optional[int]       	=					use_labels
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      int       	=					hidden_size
								UpperCAmelCase_		:      Any       	=					num_hidden_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_attention_heads
								UpperCAmelCase_		:      List[str]       	=					intermediate_size
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_act
								UpperCAmelCase_		:      Optional[int]       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Any       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Dict       	=					max_position_embeddings
								UpperCAmelCase_		:      Union[str, Any]       	=					type_vocab_size
								UpperCAmelCase_		:      int       	=					type_sequence_label_size
								UpperCAmelCase_		:      List[str]       	=					initializer_range
								UpperCAmelCase_		:      Any       	=					num_labels
								UpperCAmelCase_		:      Optional[Any]       	=					num_choices
								UpperCAmelCase_		:      str       	=					scope


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size)

								UpperCAmelCase_		:      Any       	=					None
								if self.use_input_mask:
									UpperCAmelCase_		:      Optional[Any]       	=					random_attention_mask([self.batch_size, self.seq_length])

								UpperCAmelCase_		:      Union[str, Any]       	=					None
								if self.use_token_type_ids:
									UpperCAmelCase_		:      Tuple       	=					ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size)

								UpperCAmelCase_		:      Dict       	=					None
								UpperCAmelCase_		:      List[str]       	=					None
								UpperCAmelCase_		:      Optional[Any]       	=					None
								if self.use_labels:
									UpperCAmelCase_		:      Any       	=					ids_tensor([self.batch_size]    ,			self.type_sequence_label_size)
									UpperCAmelCase_		:      List[str]       	=					ids_tensor([self.batch_size, self.seq_length]    ,			self.num_labels)
									UpperCAmelCase_		:      Tuple       	=					ids_tensor([self.batch_size]    ,			self.num_choices)

								UpperCAmelCase_		:      Tuple       	=					self.get_config()

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


							def 						_snake_case							(			self)							->				Optional[Any]:
								return OpenLlamaConfig(
								    vocab_size=self.vocab_size    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			type_vocab_size=self.type_vocab_size    ,			is_decoder=_snake_case    ,			initializer_range=self.initializer_range    ,			use_stable_embedding=_snake_case    ,			)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					OpenLlamaModel(config=_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Dict       	=					model(_snake_case    ,			attention_mask=_snake_case)
								UpperCAmelCase_		:      List[str]       	=					model(_snake_case)
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				int:
								UpperCAmelCase_		:      Union[str, Any]       	=					True
								UpperCAmelCase_		:      List[Any]       	=					OpenLlamaModel(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Optional[int]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			)
								UpperCAmelCase_		:      Any       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			)
								UpperCAmelCase_		:      Optional[int]       	=					model(_snake_case    ,			attention_mask=_snake_case)
								self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				Any:
								UpperCAmelCase_		:      Optional[int]       	=					OpenLlamaForCausalLM(config=_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Optional[int]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size))


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			)							->				Union[str, Any]:
								UpperCAmelCase_		:      Any       	=					True
								UpperCAmelCase_		:      Optional[Any]       	=					True
								UpperCAmelCase_		:      int       	=					OpenLlamaForCausalLM(config=_snake_case)
								model.to(_snake_case)
								model.eval()

								# first forward pass
								UpperCAmelCase_		:      List[str]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			use_cache=_snake_case    ,			)
								UpperCAmelCase_		:      Dict       	=					outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								UpperCAmelCase_		:      str       	=					ids_tensor((self.batch_size, 3)    ,			config.vocab_size)
								UpperCAmelCase_		:      int       	=					ids_tensor((self.batch_size, 3)    ,			vocab_size=2)

								# append to next input_ids and
								UpperCAmelCase_		:      Tuple       	=					torch.cat([input_ids, next_tokens]    ,			dim=-1)
								UpperCAmelCase_		:      List[str]       	=					torch.cat([input_mask, next_mask]    ,			dim=-1)

								UpperCAmelCase_		:      Optional[Any]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			output_hidden_states=_snake_case    ,			)['hidden_states'][0]
								UpperCAmelCase_		:      List[str]       	=					model(
								    _snake_case    ,			attention_mask=_snake_case    ,			encoder_hidden_states=_snake_case    ,			encoder_attention_mask=_snake_case    ,			past_key_values=_snake_case    ,			output_hidden_states=_snake_case    ,			)['hidden_states'][0]

								# select random slice
								UpperCAmelCase_		:      Optional[Any]       	=					ids_tensor((1,)    ,			output_from_past.shape[-1]).item()
								UpperCAmelCase_		:      Optional[int]       	=					output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase_		:      Union[str, Any]       	=					output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-3))





							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Optional[Any]       	=					self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    (
								    UpperCAmelCase_
								)	,    
								)		:      Union[str, Any]       	=					config_and_inputs
								UpperCAmelCase_		:      List[str]       	=					{'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict



@require_torch
class 					lowercase						(  a_,					a_,					a_,					unittest.TestCase ):
							_lowerCamelCase		:      Dict=   (
							    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
							)
							_lowerCamelCase		:      str=   (OpenLlamaForCausalLM,) if is_torch_available() else ()
							_lowerCamelCase		:      Optional[int]=   (
							    {
							        "feature-extraction": OpenLlamaModel,
							        "text-classification": OpenLlamaForSequenceClassification,
							        "text-generation": OpenLlamaForCausalLM,
							        "zero-shot": OpenLlamaForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							_lowerCamelCase		:      Tuple=   False
							_lowerCamelCase		:      int=   False


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Any       	=					OpenLlamaModelTester(self)
								UpperCAmelCase_		:      Union[str, Any]       	=					ConfigTester(self    ,			config_class=_snake_case    ,			hidden_size=37)


							def 						_snake_case							(			self)							->				Optional[Any]:
								self.config_tester.run_common_tests()


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Tuple       	=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
									UpperCAmelCase_		:      List[Any]       	=					type
									self.model_tester.create_and_check_model(*_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      List[str]       	=					3
								UpperCAmelCase_		:      List[str]       	=					input_dict['input_ids']
								UpperCAmelCase_		:      Optional[Any]       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.model_tester.batch_size]    ,			self.model_tester.type_sequence_label_size)
								UpperCAmelCase_		:      List[Any]       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      Tuple       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      Optional[Any]       	=					3
								UpperCAmelCase_		:      int       	=					'single_label_classification'
								UpperCAmelCase_		:      int       	=					input_dict['input_ids']
								UpperCAmelCase_		:      Any       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.model_tester.batch_size]    ,			self.model_tester.type_sequence_label_size)
								UpperCAmelCase_		:      int       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      List[str]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							def 						_snake_case							(			self)							->				Optional[int]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      int       	=					3
								UpperCAmelCase_		:      Any       	=					'multi_label_classification'
								UpperCAmelCase_		:      Tuple       	=					input_dict['input_ids']
								UpperCAmelCase_		:      str       	=					input_ids.ne(1).to(_snake_case)
								UpperCAmelCase_		:      Any       	=					ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]    ,			self.model_tester.type_sequence_label_size).to(torch.float)
								UpperCAmelCase_		:      Optional[Any]       	=					OpenLlamaForSequenceClassification(_snake_case)
								model.to(_snake_case)
								model.eval()
								UpperCAmelCase_		:      List[Any]       	=					model(_snake_case    ,			attention_mask=_snake_case    ,			labels=_snake_case)
								self.assertEqual(result.logits.shape    ,			(self.model_tester.batch_size, self.model_tester.num_labels))


							@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test')
							def 						_snake_case							(			self)							->				Optional[int]:
								pass





							@parameterized.expand([('linear',), ('dynamic',)])
							def 						_snake_case							(			self    ,			_snake_case)							->				List[str]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_		:      List[Any]       	=					ids_tensor([1, 10]    ,			config.vocab_size)
								UpperCAmelCase_		:      List[str]       	=					ids_tensor([1, int(config.max_position_embeddings * 1.5)]    ,			config.vocab_size)

								set_seed(42)  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase_		:      int       	=					OpenLlamaModel(_snake_case)
								original_model.to(_snake_case)
								original_model.eval()
								UpperCAmelCase_		:      Optional[Any]       	=					original_model(_snake_case).last_hidden_state
								UpperCAmelCase_		:      Tuple       	=					original_model(_snake_case).last_hidden_state

								set_seed(42)  # Fixed seed at init time so the two models get the same random weights
								UpperCAmelCase_		:      Dict       	=					{'type': scaling_type, 'factor': 10.0}
								UpperCAmelCase_		:      Union[str, Any]       	=					OpenLlamaModel(_snake_case)
								scaled_model.to(_snake_case)
								scaled_model.eval()
								UpperCAmelCase_		:      Dict       	=					scaled_model(_snake_case).last_hidden_state
								UpperCAmelCase_		:      Optional[int]       	=					scaled_model(_snake_case).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
									self.assertTrue(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))
								else:
									self.assertFalse(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(_snake_case    ,			_snake_case    ,			atol=1e-5))






