

'''simple docstring'''


from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase__										= transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Any:
	if isinstance(UpperCamelCase   ,torch.Tensor  ):
		return image
	elif isinstance(UpperCamelCase   ,PIL.Image.Image  ):
		UpperCAmelCase_		:      Dict       	=					[image]

	UpperCAmelCase_		:      Any       	=					[trans(img.convert('RGB'  )  ) for img in image]
	UpperCAmelCase_		:      str       	=					torch.stack(UpperCamelCase  )
	return image







class 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				Optional[Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								UpperCAmelCase_		:      Any       	=					DDIMScheduler.from_config(scheduler.config)

								self.register_modules(unet=_snake_case    ,			scheduler=_snake_case)


							def 						_snake_case							(			self    ,			_snake_case)							->				Optional[int]:
								if strength < 0 or strength > 1:
									raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}""")


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Tuple:
								# get the original timestep using init_timestep
								UpperCAmelCase_		:      List[str]       	=					min(int(num_inference_steps * strength)    ,			_snake_case)

								UpperCAmelCase_		:      Any       	=					max(num_inference_steps - init_timestep    ,			0)
								UpperCAmelCase_		:      List[Any]       	=					self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case=None)							->				Optional[int]:
								if not isinstance(_snake_case    ,			(torch.Tensor, PIL.Image.Image, list)):
									raise ValueError(
									    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_snake_case)}""")

								UpperCAmelCase_		:      Optional[Any]       	=					image.to(device=_snake_case    ,			dtype=_snake_case)

								if isinstance(_snake_case    ,			_snake_case) and len(_snake_case) != batch_size:
									raise ValueError(
									    F"""You have passed a list of generators of length {len(_snake_case)}, but requested an effective batch"""
									    F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

								UpperCAmelCase_		:      List[str]       	=					init_latents.shape
								UpperCAmelCase_		:      List[str]       	=					randn_tensor(_snake_case    ,			generator=_snake_case    ,			device=_snake_case    ,			dtype=_snake_case)

								# get latents
								print('add noise to latents at timestep'    ,			_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					self.scheduler.add_noise(_snake_case    ,			_snake_case    ,			_snake_case)
								UpperCAmelCase_		:      str       	=					init_latents

								return latents





							@torch.no_grad()
							def __call__(			self    ,			_snake_case = None    ,			_snake_case = 0.8    ,			_snake_case = 1    ,			_snake_case = None    ,			_snake_case = 0.0    ,			_snake_case = 50    ,			_snake_case = None    ,			_snake_case = "pil"    ,			_snake_case = True    ,			)							->				Union[ImagePipelineOutput, Tuple]:
								self.check_inputs(_snake_case)

								# 2. Preprocess image
								UpperCAmelCase_		:      Tuple       	=					preprocess(_snake_case)

								# 3. set timesteps
								self.scheduler.set_timesteps(_snake_case    ,			device=self.device)
								UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					self.get_timesteps(_snake_case    ,			_snake_case    ,			self.device)
								UpperCAmelCase_		:      List[Any]       	=					timesteps[:1].repeat(_snake_case)

								# 4. Prepare latent variables
								UpperCAmelCase_		:      List[Any]       	=					self.prepare_latents(_snake_case    ,			_snake_case    ,			_snake_case    ,			self.unet.dtype    ,			self.device    ,			_snake_case)
								UpperCAmelCase_		:      List[str]       	=					latents

								# 5. Denoising loop
								for t in self.progress_bar(_snake_case):
									# 1. predict noise model_output
									UpperCAmelCase_		:      Optional[Any]       	=					self.unet(_snake_case    ,			_snake_case).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCAmelCase_		:      List[Any]       	=					self.scheduler.step(
									    _snake_case    ,			_snake_case    ,			_snake_case    ,			eta=_snake_case    ,			use_clipped_model_output=_snake_case    ,			generator=_snake_case    ,			).prev_sample

								UpperCAmelCase_		:      Optional[int]       	=					(image / 2 + 0.5).clamp(0    ,			1)
								UpperCAmelCase_		:      Any       	=					image.cpu().permute(0    ,			2    ,			3    ,			1).numpy()
								if output_type == "pil":
									UpperCAmelCase_		:      Optional[int]       	=					self.numpy_to_pil(_snake_case)

								if not return_dict:
									return (image, latent_timestep.item())

								return ImagePipelineOutput(images=_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))






