

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 	__magic_name__    :





			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					,  )   -> List[str]:




						'''simple docstring'''
						__a        =parent
						__a        =batch_size
						__a        =seq_length
						__a        =is_training
						__a        =use_input_mask
						__a        =use_token_type_ids
						__a        =use_labels
						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =intermediate_size
						__a        =hidden_act
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =type_vocab_size
						__a        =type_sequence_label_size
						__a        =initializer_range
						__a        =num_labels
						__a        =num_choices
						__a        =scope





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




						'''simple docstring'''
						__a        =ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size					)

						__a        =None
						if self.use_input_mask:
									__a        =random_attention_mask([self.batch_size, self.seq_length]					)

						__a        =None
						if self.use_token_type_ids:
									__a        =ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size					)

						__a        =None
						__a        =None
						__a        =None
						if self.use_labels:
									__a        =ids_tensor([self.batch_size]					,  self.type_sequence_label_size					)
									__a        =ids_tensor([self.batch_size, self.seq_length]					,  self.num_labels					)
									__a        =ids_tensor([self.batch_size]					,  self.num_choices					)

						__a        =self.get_config()

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





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						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 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =OpenLlamaModel(config=__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =model(__snake_case					,  attention_mask=__snake_case					)
						__a        =model(__snake_case					)
						self.parent.assertEqual(result.last_hidden_state.shape					,  (self.batch_size, self.seq_length, self.hidden_size)					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )   -> int:




						'''simple docstring'''
						__a        =True
						__a        =OpenLlamaModel(__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =model(
						    __snake_case					,  attention_mask=__snake_case					,  encoder_hidden_states=__snake_case					,  encoder_attention_mask=__snake_case					,  )
						__a        =model(
						    __snake_case					,  attention_mask=__snake_case					,  encoder_hidden_states=__snake_case					,  )
						__a        =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 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )   -> Tuple:




						'''simple docstring'''
						__a        =OpenLlamaForCausalLM(config=__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =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 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )   -> int:




						'''simple docstring'''
						__a        =True
						__a        =True
						__a        =OpenLlamaForCausalLM(config=__snake_case					)
						model.to(__snake_case					)
						model.eval()

						# first forward pass
						__a        =model(
						    __snake_case					,  attention_mask=__snake_case					,  encoder_hidden_states=__snake_case					,  encoder_attention_mask=__snake_case					,  use_cache=__snake_case					,  )
						__a        =outputs.past_key_values

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

						# append to next input_ids and
						__a        =torch.cat([input_ids, next_tokens]					,  dim=-1					)
						__a        =torch.cat([input_mask, next_mask]					,  dim=-1					)

						__a        =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]
						__a        =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
						__a        =ids_tensor((1,)					,  output_from_past.shape[-1]					).item()
						__a        =output_from_no_past[:, -3:, random_slice_idx].detach()
						__a        =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 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =self.prepare_config_and_inputs()
						(
						    (
						    __a
						)      ,    (
						    __a
						)      ,    (
						    __a
						)      ,    (
						    __a
						)      ,    (
						    __a
						)      ,    (
						    __a
						)      ,    (
						    __a
						)      ,    
						)        =config_and_inputs
						__a        ={'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict






@require_torch
class 	__magic_name__    (			lowerCAmelCase_  ,    lowerCAmelCase_  ,    lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						(
			    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
			)
			SCREAMING_SNAKE_CASE							   =						(OpenLlamaForCausalLM,) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE							   =						(
			    {
			        'feature-extraction': OpenLlamaModel,
			        'text-classification': OpenLlamaForSequenceClassification,
			        'text-generation': OpenLlamaForCausalLM,
			        'zero-shot': OpenLlamaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			SCREAMING_SNAKE_CASE							   =						False
			SCREAMING_SNAKE_CASE							   =						False





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        =OpenLlamaModelTester(self					)
						__a        =ConfigTester(self					,  config_class=__snake_case					,  hidden_size=37					)





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						self.config_tester.run_common_tests()





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__snake_case					)





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
									__a        =type
									self.model_tester.create_and_check_model(*__snake_case					)





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a      ,    __a        =self.model_tester.prepare_config_and_inputs_for_common()
						__a        =3
						__a        =input_dict['input_ids']
						__a        =input_ids.ne(1					).to(__snake_case					)
						__a        =ids_tensor([self.model_tester.batch_size]					,  self.model_tester.type_sequence_label_size					)
						__a        =OpenLlamaForSequenceClassification(__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =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 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a      ,    __a        =self.model_tester.prepare_config_and_inputs_for_common()
						__a        =3
						__a        ='single_label_classification'
						__a        =input_dict['input_ids']
						__a        =input_ids.ne(1					).to(__snake_case					)
						__a        =ids_tensor([self.model_tester.batch_size]					,  self.model_tester.type_sequence_label_size					)
						__a        =OpenLlamaForSequenceClassification(__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =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 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a      ,    __a        =self.model_tester.prepare_config_and_inputs_for_common()
						__a        =3
						__a        ='multi_label_classification'
						__a        =input_dict['input_ids']
						__a        =input_ids.ne(1					).to(__snake_case					)
						__a        =ids_tensor(
						    [self.model_tester.batch_size, config.num_labels]					,  self.model_tester.type_sequence_label_size					).to(torch.float					)
						__a        =OpenLlamaForSequenceClassification(__snake_case					)
						model.to(__snake_case					)
						model.eval()
						__a        =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 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						pass





			@parameterized.expand([('linear',), ('dynamic',)]					)
			def 		__magic_name__    (		self					,  __snake_case					)   -> Tuple:




						'''simple docstring'''
						__a      ,    __a        =self.model_tester.prepare_config_and_inputs_for_common()
						__a        =ids_tensor([1, 10]					,  config.vocab_size					)
						__a        =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
						__a        =OpenLlamaModel(__snake_case					)
						original_model.to(__snake_case					)
						original_model.eval()
						__a        =original_model(__snake_case					).last_hidden_state
						__a        =original_model(__snake_case					).last_hidden_state

						set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
						__a        ={'type': scaling_type, 'factor': 10.0}
						__a        =OpenLlamaModel(__snake_case					)
						scaled_model.to(__snake_case					)
						scaled_model.eval()
						__a        =scaled_model(__snake_case					).last_hidden_state
						__a        =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					)					)









import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)  # pylint: disable=invalid-name






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






			warnings.warn(
			    'The preprocess method is deprecated and will be removed in a future version. Please'
			    ' use VaeImageProcessor.preprocess instead'       ,     _snake_case       ,     )
			if isinstance(_snake_case       ,     torch.Tensor	):
						return image
			elif isinstance(_snake_case       ,     PIL.Image.Image	):
						__a        =[image]

			if isinstance(image[0]       ,     PIL.Image.Image	):
						__a      ,    __a        =image[0].size
						__a      ,    __a        =(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

						__a        =[np.array(i.resize((w, h)       ,     resample=PIL_INTERPOLATION['lanczos']	)	)[None, :] for i in image]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =np.array(_snake_case	).astype(np.floataa	) / 255.0
						__a        =image.transpose(0       ,     3       ,     1       ,     2	)
						__a        =2.0 * image - 1.0
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(image[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return image






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






			if isinstance(_snake_case       ,     torch.Tensor	):
						return mask
			elif isinstance(_snake_case       ,     PIL.Image.Image	):
						__a        =[mask]

			if isinstance(mask[0]       ,     PIL.Image.Image	):
						__a      ,    __a        =mask[0].size
						__a      ,    __a        =(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						__a        =[np.array(m.convert('L'	).resize((w, h)       ,     resample=PIL_INTERPOLATION['nearest']	)	)[None, :] for m in mask]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =mask.astype(np.floataa	) / 255.0
						__a        =0
						__a        =1
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(mask[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return mask






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						42
			SCREAMING_SNAKE_CASE							   =						42





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




						'''simple docstring'''
						super().__init__()
						self.register_modules(unet=__snake_case					,  scheduler=__snake_case					)





			@torch.no_grad()
			def __call__(		self					,  __snake_case					,  __snake_case					,  __snake_case = 250					,  __snake_case = 0.0					,  __snake_case = 10					,  __snake_case = 10					,  __snake_case = None					,  __snake_case = "pil"					,  __snake_case = True					,  )   -> Union[ImagePipelineOutput, Tuple]:




						'''simple docstring'''
						__a        =image

						__a        =_preprocess_image(__snake_case					)
						__a        =original_image.to(device=self.device					,  dtype=self.unet.dtype					)
						__a        =_preprocess_mask(__snake_case					)
						__a        =mask_image.to(device=self.device					,  dtype=self.unet.dtype					)

						__a        =original_image.shape[0]

						# sample gaussian noise to begin the loop
						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.'					)

						__a        =original_image.shape
						__a        =randn_tensor(__snake_case					,  generator=__snake_case					,  device=self.device					,  dtype=self.unet.dtype					)

						# set step values
						self.scheduler.set_timesteps(__snake_case					,  __snake_case					,  __snake_case					,  self.device					)
						__a        =eta

						__a        =self.scheduler.timesteps[0] + 1
						__a        =generator[0] if isinstance(__snake_case					,  __snake_case					) else generator
						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
									if t < t_last:
												# predict the noise residual
												__a        =self.unet(__snake_case					,  __snake_case					).sample
												# compute previous image: x_t -> x_t-1
												__a        =self.scheduler.step(__snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					).prev_sample

									else:
												# compute the reverse: x_t-1 -> x_t
												__a        =self.scheduler.undo_step(__snake_case					,  __snake_case					,  __snake_case					)
									__a        =t

						__a        =(image / 2 + 0.5).clamp(0					,  1					)
						__a        =image.cpu().permute(0					,  2					,  3					,  1					).numpy()
						if output_type == "pil":
									__a        =self.numpy_to_pil(__snake_case					)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__snake_case					)






