
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					DDIMPipeline
	snake_case_    =					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
	snake_case_    =					PipelineTesterMixin.required_optional_params - {
	    "num_images_per_prompt",
	    "latents",
	    "callback",
	    "callback_steps",
	}
	snake_case_    =					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
	snake_case_    =					False






	def     UpperCamelCase_  (	self :			str			):
					torch.manual_seed(0			)
					__A             =			UNetaDModel(
					    block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=("DownBlock2D", "AttnDownBlock2D")							,up_block_types=("AttnUpBlock2D", "UpBlock2D")							,)
					__A             =			DDIMScheduler()
					__A             =			{"unet": unet, "scheduler": scheduler}
					return components






	def     UpperCamelCase_  (	self :			Any							,A :			Any							,A :			Any=0			):
					if str(A			).startswith("mps"			):
									__A             =			torch.manual_seed(A			)
					else:
									__A             =			torch.Generator(device=A			).manual_seed(A			)
					__A             =			{
					    "batch_size": 1,
					    "generator": generator,
					    "num_inference_steps": 2,
					    "output_type": "numpy",
					}
					return inputs






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			"cpu"

					__A             =			self.get_dummy_components()
					__A             =			self.pipeline_class(**A			)
					pipe.to(A			)
					pipe.set_progress_bar_config(disable=A			)

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

					self.assertEqual(image.shape							,(1, 32, 32, 3)			)
					__A             =			np.array(
					    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]			)
					__A             =			np.abs(image_slice.flatten() - expected_slice			).max()
					self.assertLessEqual(A							,1E-3			)






	def     UpperCamelCase_  (	self :			Dict			):
					super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)






	def     UpperCamelCase_  (	self :			List[str]			):
					super().test_save_load_local(expected_max_difference=3E-3			)






	def     UpperCamelCase_  (	self :			List[Any]			):
					super().test_save_load_optional_components(expected_max_difference=3E-3			)







	def     UpperCamelCase_  (	self :			Dict			):
					super().test_inference_batch_single_identical(expected_max_diff=3E-3			)





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




	'''simple docstring'''





	def     UpperCamelCase_  (	self :			str			):
					__A             =			"google/ddpm-cifar10-32"

					__A             =			UNetaDModel.from_pretrained(A			)
					__A             =			DDIMScheduler()

					__A             =			DDIMPipeline(unet=A							,scheduler=A			)
					ddim.to(A			)
					ddim.set_progress_bar_config(disable=A			)

					__A             =			torch.manual_seed(0			)
					__A             =			ddim(generator=A							,eta=0.0							,output_type="numpy"			).images

					__A             =			image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					__A             =			np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			"google/ddpm-ema-bedroom-256"

					__A             =			UNetaDModel.from_pretrained(A			)
					__A             =			DDIMScheduler.from_pretrained(A			)

					__A             =			DDIMPipeline(unet=A							,scheduler=A			)
					ddpm.to(A			)
					ddpm.set_progress_bar_config(disable=A			)

					__A             =			torch.manual_seed(0			)
					__A             =			ddpm(generator=A							,output_type="numpy"			).images

					__A             =			image[0, -3:, -3:, -1]

					assert image.shape == (1, 2_56, 2_56, 3)
					__A             =			np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2


from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]






	@register_to_config
	def __init__(	self :			Tuple							,A :			int							,A :			int							,A :			Optional[int] = None							,A :			int = 5_02_57							,A :			int = 10_24							,A :			int = 7_68							,A :			int = 12							,A :			int = 12							,A :			Optional[int] = None							,A :			str = "gelu_new"							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 1E-5							,A :			float = 0.02							,A :			bool = True							,A :			bool = True							,A :			bool = False							,A :			bool = False							,):
					super().__init__()

					__A             =			prefix_length

					if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
									raise ValueError(
									    f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
									    f''' `n_embd`: {n_embd} are not equal.'''			)

					__A             =			prefix_inner_dim
					__A             =			prefix_hidden_dim

					__A             =			(
					    nn.Linear(self.prefix_inner_dim							,self.prefix_hidden_dim			)
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					__A             =			(
					    nn.Linear(self.prefix_hidden_dim							,A			) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					__A             =			GPTaConfig(
					    vocab_size=A							,n_positions=A							,n_embd=A							,n_layer=A							,n_head=A							,n_inner=A							,activation_function=A							,resid_pdrop=A							,embd_pdrop=A							,attn_pdrop=A							,layer_norm_epsilon=A							,initializer_range=A							,scale_attn_weights=A							,use_cache=A							,scale_attn_by_inverse_layer_idx=A							,reorder_and_upcast_attn=A							,)
					__A             =			GPTaLMHeadModel(A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			torch.Tensor							,A :			torch.Tensor							,A :			Optional[torch.Tensor] = None							,A :			Optional[torch.Tensor] = None							,):
					__A             =			self.transformer.transformer.wte(A			)
					__A             =			self.encode_prefix(A			)
					__A             =			self.decode_prefix(A			)
					__A             =			torch.cat((prefix_embeds, embedding_text)							,dim=1			)

					if labels is not None:
									__A             =			self.get_dummy_token(input_ids.shape[0]							,input_ids.device			)
									__A             =			torch.cat((dummy_token, input_ids)							,dim=1			)
					__A             =			self.transformer(inputs_embeds=A							,labels=A							,attention_mask=A			)
					if self.prefix_hidden_dim is not None:
									return out, hidden
					else:
									return out






	def     UpperCamelCase_  (	self :			int							,A :			int							,A :			torch.device			):
					return torch.zeros(A							,self.prefix_length							,dtype=torch.intaa							,device=A			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[Any]			):
					return self.encode_prefix(A			)






	@torch.no_grad()
	def     UpperCamelCase_  (	self :			str							,A :			List[str]							,A :			List[Any]							,A :			List[Any]			):
					__A             =			torch.split(A							,1							,dim=0			)
					__A             =			[]
					__A             =			[]
					for feature in features:
									__A             =			self.decode_prefix(feature.to(A			)			)  # back to the clip feature
									# Only support beam search for now
									__A      ,							__A             =			self.generate_beam(
									    input_embeds=A							,device=A							,eos_token_id=A			)
									generated_tokens.append(output_tokens[0]			)
									generated_seq_lengths.append(seq_lengths[0]			)
					__A             =			torch.stack(A			)
					__A             =			torch.stack(A			)
					return generated_tokens, generated_seq_lengths







	@torch.no_grad()
	def     UpperCamelCase_  (	self :			Tuple							,A :			List[str]=None							,A :			Dict=None							,A :			Optional[int]=None							,A :			int = 5							,A :			int = 67							,A :			float = 1.0							,A :			Optional[int] = None							,):
					__A             =			eos_token_id
					__A             =			None
					__A             =			None
					__A             =			torch.ones(A							,device=A							,dtype=torch.int			)
					__A             =			torch.zeros(A							,device=A							,dtype=torch.bool			)

					if input_embeds is not None:
									__A             =			input_embeds
					else:
									__A             =			self.transformer.transformer.wte(A			)

					for i in range(A			):
									__A             =			self.transformer(inputs_embeds=A			)
									__A             =			outputs.logits
									__A             =			logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
									__A             =			logits.softmax(-1			).log()

									if scores is None:
													__A      ,							__A             =			logits.topk(A							,-1			)
													__A             =			generated.expand(A							,*generated.shape[1:]			)
													__A      ,							__A             =			next_tokens.permute(1							,0			), scores.squeeze(0			)
													if tokens is None:
																	__A             =			next_tokens
													else:
																	__A             =			tokens.expand(A							,*tokens.shape[1:]			)
																	__A             =			torch.cat((tokens, next_tokens)							,dim=1			)
									else:
													__A             =			-float(np.inf			)
													__A             =			0
													__A             =			scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													__A             =			scores_sum / seq_lengths[:, None]
													__A      ,							__A             =			scores_sum_average.view(-1			).topk(A							,-1			)
													__A             =			next_tokens // scores_sum.shape[1]
													__A             =			seq_lengths[next_tokens_source]
													__A             =			next_tokens % scores_sum.shape[1]
													__A             =			next_tokens.unsqueeze(1			)
													__A             =			tokens[next_tokens_source]
													__A             =			torch.cat((tokens, next_tokens)							,dim=1			)
													__A             =			generated[next_tokens_source]
													__A             =			scores_sum_average * seq_lengths
													__A             =			is_stopped[next_tokens_source]

									__A             =			self.transformer.transformer.wte(next_tokens.squeeze()			).view(generated.shape[0]							,1							,-1			)
									__A             =			torch.cat((generated, next_token_embed)							,dim=1			)
									__A             =			is_stopped + next_tokens.eq(A			).squeeze()
									if is_stopped.all():
													break

					__A             =			scores / seq_lengths
					__A             =			scores.argsort(descending=A			)
					# tokens tensors are already padded to max_seq_length
					__A             =			[tokens[i] for i in order]
					__A             =			torch.stack(A							,dim=0			)
					__A             =			torch.tensor([seq_lengths[i] for i in order]							,dtype=seq_lengths.dtype			)
					return output_texts, seq_lengths
