



"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}




class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''gpt_neo'''
							_UpperCAmelCase      				=					['''past_key_values''']
							_UpperCAmelCase      				=					{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






							def __init__(  self	,       snake_case=50257	,       snake_case=2048	,       snake_case=2048	,       snake_case=24	,       snake_case=[[["global", "local"], 12]]	,       snake_case=16	,       snake_case=None	,       snake_case=256	,       snake_case="gelu_new"	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.1	,       snake_case=1E-5	,       snake_case=0.02	,       snake_case=True	,       snake_case=50256	,       snake_case=50256	,       **snake_case	,       )			->							Any:
									_UpperCAmelCase							 =		vocab_size
									_UpperCAmelCase							 =		max_position_embeddings
									_UpperCAmelCase							 =		hidden_size
									_UpperCAmelCase							 =		num_layers
									_UpperCAmelCase							 =		num_heads
									_UpperCAmelCase							 =		intermediate_size
									_UpperCAmelCase							 =		window_size
									_UpperCAmelCase							 =		activation_function
									_UpperCAmelCase							 =		resid_dropout
									_UpperCAmelCase							 =		embed_dropout
									_UpperCAmelCase							 =		attention_dropout
									_UpperCAmelCase							 =		classifier_dropout
									_UpperCAmelCase							 =		layer_norm_epsilon
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		use_cache

									_UpperCAmelCase							 =		bos_token_id
									_UpperCAmelCase							 =		eos_token_id

									_UpperCAmelCase							 =		attention_types
									_UpperCAmelCase							 =		self.expand_attention_types_params(snake_case						)

									if len(self.attention_layers						) != self.num_layers:
											raise ValueError(
											    'Configuration for convolutional module is incorrect. '
											    'It is required that `len(config.attention_layers)` == `config.num_layers` '
											    f'but is `len(config.attention_layers) = {len(self.attention_layers						)}`, '
											    f'`config.num_layers = {self.num_layers}`. '
											    '`config.attention_layers` is prepared using `config.attention_types`. '
											    'Please verify the value of `config.attention_types` argument.'						)

									super().__init__(bos_token_id=snake_case	,       eos_token_id=snake_case	,       **snake_case						)






							@staticmethod
							def        lowerCamelCase_   (  snake_case						)			->							Optional[int]:
									_UpperCAmelCase							 =		[]
									for item in attention_types:
											for _ in range(item[1]						):
													attentions.extend(item[0]						)
									return attentions







def 	UpperCAmelCase     (		A	:						Optional[int]     ,				A	:						int     ,				A	:						Any     ,				A	:						Optional[int]       ):

		'''simple docstring'''


		import torch

		_UpperCAmelCase							 =		input.size()
		_UpperCAmelCase							 =		len(A       )
		_UpperCAmelCase							 =		shape[dimension]

		_UpperCAmelCase							 =		torch.arange(0     ,				A     ,				A       )
		_UpperCAmelCase							 =		torch.div(sizedim - size     ,				A     ,				rounding_mode='floor'       ) + 1
		_UpperCAmelCase							 =		torch.arange(A       ) + low_indices[:min_length][:, None]

		_UpperCAmelCase							 =		[slice(A       )] * rank
		_UpperCAmelCase							 =		indices
		_UpperCAmelCase							 =		input[s]

		_UpperCAmelCase							 =		list(range(0     ,				rank + 1       )       )
		perm.append(perm.pop(dimension + 1       )       )

		return sliced.permute(A       )




def 	UpperCAmelCase     (		A	:						Dict     ,				A	:						Dict       ):

		'''simple docstring'''


		import torch

		_UpperCAmelCase							 =		torch.arange(1     ,				A       )
		_UpperCAmelCase							 =		torch.remainder(A     ,				A       )
		_UpperCAmelCase							 =		remainders == 0
		_UpperCAmelCase							 =		candidates[divisor_indices]
		_UpperCAmelCase							 =		torch.max(A       )
		return largest_divisor, torch.div(A     ,				A     ,				rounding_mode='floor'       )






class 			lowercase__			(      A    ):
							'''simple docstring'''







							@property
							def        lowerCamelCase_   (  self						)			->							Mapping[str, Mapping[int, str]]:
									_UpperCAmelCase							 =		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}						)
									if self.use_past:
											self.fill_with_past_key_values_(snake_case	,       direction='inputs'						)
											_UpperCAmelCase							 =		{0: 'batch', 1: 'past_sequence + sequence'}
									else:
											_UpperCAmelCase							 =		{0: 'batch', 1: 'sequence'}

									return common_inputs






							@property
							def        lowerCamelCase_   (  self						)			->							int:
									return self._config.num_heads






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case = -1	,       snake_case = -1	,       snake_case = False	,       snake_case = None	,       )			->							Mapping[str, Any]:
									_UpperCAmelCase							 =		super(snake_case	,       self						).generate_dummy_inputs(
									    snake_case	,       batch_size=snake_case	,       seq_length=snake_case	,       is_pair=snake_case	,       framework=snake_case						)

									# We need to order the input in the way they appears in the forward()
									_UpperCAmelCase							 =		OrderedDict({'input_ids': common_inputs['input_ids']}						)

									# Need to add the past_keys
									if self.use_past:
											if not is_torch_available():
													raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
											else:
													import torch

													_UpperCAmelCase ,					_UpperCAmelCase							 =		common_inputs['input_ids'].shape
													# Not using the same length for past_key_values
													_UpperCAmelCase							 =		seqlen + 2
													_UpperCAmelCase							 =		(
													    batch,
													    self.num_attention_heads,
													    past_key_values_length,
													    self._config.hidden_size // self.num_attention_heads,
													)
													_UpperCAmelCase							 =		[
													    (torch.zeros(snake_case						), torch.zeros(snake_case						)) for _ in range(self.num_layers						)
													]

									_UpperCAmelCase							 =		common_inputs['attention_mask']
									if self.use_past:
											_UpperCAmelCase							 =		ordered_inputs['attention_mask'].dtype
											_UpperCAmelCase							 =		torch.cat(
											    [ordered_inputs['attention_mask'], torch.ones(snake_case	,       snake_case	,       dtype=snake_case						)]	,       dim=1						)

									return ordered_inputs






							@property
							def        lowerCamelCase_   (  self						)			->							int:
									return 13







"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					DiTPipeline
							_UpperCAmelCase      				=					CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							_UpperCAmelCase      				=					PipelineTesterMixin.required_optional_params - {
							    '''latents''',
							    '''num_images_per_prompt''',
							    '''callback''',
							    '''callback_steps''',
							}
							_UpperCAmelCase      				=					CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self						)			->							str:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		TransformeraDModel(
									    sample_size=16	,       num_layers=2	,       patch_size=4	,       attention_head_dim=8	,       num_attention_heads=2	,       in_channels=4	,       out_channels=8	,       attention_bias=snake_case	,       activation_fn='gelu-approximate'	,       num_embeds_ada_norm=1000	,       norm_type='ada_norm_zero'	,       norm_elementwise_affine=snake_case	,       )
									_UpperCAmelCase							 =		AutoencoderKL()
									_UpperCAmelCase							 =		DDIMScheduler()
									_UpperCAmelCase							 =		{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
									return components






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0						)			->							Optional[Any]:
									if str(snake_case						).startswith('mps'						):
											_UpperCAmelCase							 =		torch.manual_seed(snake_case						)
									else:
											_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)
									_UpperCAmelCase							 =		{
									    'class_labels': [1],
									    'generator': generator,
									    'num_inference_steps': 2,
									    'output_type': 'numpy',
									}
									return inputs






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		'cpu'

									_UpperCAmelCase							 =		self.get_dummy_components()
									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									pipe.to(snake_case						)
									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		self.get_dummy_inputs(snake_case						)
									_UpperCAmelCase							 =		pipe(**snake_case						).images
									_UpperCAmelCase							 =		image[0, -3:, -3:, -1]

									self.assertEqual(image.shape	,       (1, 16, 16, 3)						)
									_UpperCAmelCase							 =		np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]						)
									_UpperCAmelCase							 =		np.abs(image_slice.flatten() - expected_slice						).max()
									self.assertLessEqual(snake_case	,       1E-3						)






							def        lowerCamelCase_   (  self						)			->							Any:
									self._test_inference_batch_single_identical(relax_max_difference=snake_case	,       expected_max_diff=1E-3						)






							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()	,       reason='XFormers attention is only available with CUDA and `xformers` installed'	,       )
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)







@require_torch_gpu
@slow
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							int:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		torch.manual_seed(0						)

									_UpperCAmelCase							 =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'						)
									pipe.to('cuda'						)

									_UpperCAmelCase							 =		['vase', 'umbrella', 'white shark', 'white wolf']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

									_UpperCAmelCase							 =		pipe(snake_case	,       generator=snake_case	,       num_inference_steps=40	,       output_type='np'						).images

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		load_numpy(
											    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'						)
											assert np.abs((expected_image - image).max()						) < 1E-2






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'						)
									_UpperCAmelCase							 =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
									pipe.to('cuda'						)

									_UpperCAmelCase							 =		['vase', 'umbrella']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

									_UpperCAmelCase							 =		torch.manual_seed(0						)
									_UpperCAmelCase							 =		pipe(snake_case	,       generator=snake_case	,       num_inference_steps=25	,       output_type='np'						).images

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    f'/dit/{word}_512.npy'						)

											assert np.abs((expected_image - image).max()						) < 1E-1


