
import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
					import torch

					from transformers import DecisionTransformerModel
					from transformers.models.decision_transformer.modeling_decision_transformer import (
					    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
					)


class     lowercase :



		def __init__(			self    ,				A_    ,				A_=13    ,				A_=7    ,				A_=6    ,				A_=17    ,				A_=23    ,				A_=11    ,				A_=True    ,				)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					seq_length
				UpperCamelCase          =					act_dim
				UpperCamelCase          =					state_dim
				UpperCamelCase          =					hidden_size
				UpperCamelCase          =					max_length
				UpperCamelCase          =					is_training



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor((self.batch_size, self.seq_length, self.state_dim)	)
				UpperCamelCase          =					floats_tensor((self.batch_size, self.seq_length, self.act_dim)	)
				UpperCamelCase          =					floats_tensor((self.batch_size, self.seq_length, 1)	)
				UpperCamelCase          =					floats_tensor((self.batch_size, self.seq_length, 1)	)
				UpperCamelCase          =					ids_tensor((self.batch_size, self.seq_length)    ,				vocab_size=1_000	)
				UpperCamelCase          =					random_attention_mask((self.batch_size, self.seq_length)	)

				UpperCamelCase          =					self.get_config()

				return (
				    config,
				    states,
				    actions,
				    rewards,
				    returns_to_go,
				    timesteps,
				    attention_mask,
				)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return DecisionTransformerConfig(
				    batch_size=self.batch_size    ,				seq_length=self.seq_length    ,				act_dim=self.act_dim    ,				state_dim=self.state_dim    ,				hidden_size=self.hidden_size    ,				max_length=self.max_length    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					DecisionTransformerModel(config=A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					model(A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)

				self.parent.assertEqual(result.state_preds.shape    ,				states.shape	)
				self.parent.assertEqual(result.action_preds.shape    ,				actions.shape	)
				self.parent.assertEqual(result.return_preds.shape    ,				returns_to_go.shape	)
				self.parent.assertEqual(
				    result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length * 3, self.hidden_size)	)  # seq length *3 as there are 3 modelities: states, returns and actions



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()
				(
				    (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  (
				    UpperCamelCase
				)     ,  
				)          =					config_and_inputs
				UpperCamelCase          =					{
				    'states': states,
				    'actions': actions,
				    'rewards': rewards,
				    'returns_to_go': returns_to_go,
				    'timesteps': timesteps,
				    'attention_mask': attention_mask,
				}
				return config, inputs_dict






@require_torch
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Tuple     					=    (DecisionTransformerModel,) if is_torch_available() else ()
		__lowercase	:	Dict     					=    ()
		__lowercase	:	int     					=    {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

		# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
		__lowercase	:	List[str]     					=    False

		# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
		__lowercase	:	Union[str, Any]     					=    False
		__lowercase	:	Dict     					=    False
		__lowercase	:	Union[str, Any]     					=    False
		__lowercase	:	Optional[Any]     					=    False
		__lowercase	:	Dict     					=    False
		__lowercase	:	Union[str, Any]     					=    False
		__lowercase	:	Optional[Any]     					=    False
		__lowercase	:	int     					=    False
		__lowercase	:	Any     					=    False



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					DecisionTransformerModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=A_    ,				hidden_size=37	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					DecisionTransformerModel.from_pretrained(A_	)
						self.assertIsNotNone(A_	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase          =					model_class(A_	)
						UpperCamelCase          =					inspect.signature(model.forward	)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						UpperCamelCase          =					[*signature.parameters.keys()]

						UpperCamelCase          =					[
						    'states',
						    'actions',
						    'rewards',
						    'returns_to_go',
						    'timesteps',
						    'attention_mask',
						]

						self.assertListEqual(arg_names[: len(A_	)]    ,				A_	)






@require_torch
class     lowercase (		unittest.TestCase       ):



		@slow
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					2  # number of steps of autoregressive prediction we will perform
				UpperCamelCase          =					10  # defined by the RL environment, may be normalized
				UpperCamelCase          =					DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'	)
				UpperCamelCase          =					model.to(A_	)
				UpperCamelCase          =					model.config
				torch.manual_seed(0	)
				UpperCamelCase          =					torch.randn(1    ,				1    ,				config.state_dim	).to(device=A_    ,				dtype=torch.floataa	)  # env.reset()

				UpperCamelCase          =					torch.tensor(
				    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]    ,				device=A_	)

				UpperCamelCase          =					torch.tensor(A_    ,				device=A_    ,				dtype=torch.floataa	).reshape(1    ,				1    ,				1	)
				UpperCamelCase          =					state
				UpperCamelCase          =					torch.zeros(1    ,				0    ,				config.act_dim    ,				device=A_    ,				dtype=torch.floataa	)
				UpperCamelCase          =					torch.zeros(1    ,				0    ,				device=A_    ,				dtype=torch.floataa	)
				UpperCamelCase          =					torch.tensor(0    ,				device=A_    ,				dtype=torch.long	).reshape(1    ,				1	)

				for step in range(A_	):
						UpperCamelCase          =					torch.cat([actions, torch.zeros(1    ,				1    ,				config.act_dim    ,				device=A_	)]    ,				dim=1	)
						UpperCamelCase          =					torch.cat([rewards, torch.zeros(1    ,				1    ,				device=A_	)]    ,				dim=1	)

						UpperCamelCase          =					torch.ones(1    ,				states.shape[1]	).to(dtype=torch.long    ,				device=states.device	)

						with torch.no_grad():
								UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					model(
								    states=A_    ,				actions=A_    ,				rewards=A_    ,				returns_to_go=A_    ,				timesteps=A_    ,				attention_mask=A_    ,				return_dict=A_    ,				)

						self.assertEqual(action_pred.shape    ,				actions.shape	)
						self.assertTrue(torch.allclose(action_pred[0, -1]    ,				expected_outputs[step]    ,				atol=1e-4	)	)
						UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					(  # env.step(action)
						    torch.randn(1    ,				1    ,				config.state_dim	).to(device=A_    ,				dtype=torch.floataa	),
						    1.0,
						    False,
						    {},
						)

						UpperCamelCase          =					action_pred[0, -1]
						UpperCamelCase          =					torch.cat([states, state]    ,				dim=1	)
						UpperCamelCase          =					returns_to_go[0, -1] - reward
						UpperCamelCase          =					torch.cat([returns_to_go, pred_return.reshape(1    ,				1    ,				1	)]    ,				dim=1	)
						UpperCamelCase          =					torch.cat(
						    [timesteps, torch.ones((1, 1)    ,				device=A_    ,				dtype=torch.long	) * (step + 1)]    ,				dim=1	)


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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 (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	List[Any]     					=    KandinskyVaaControlnetImgaImgPipeline
		__lowercase	:	Optional[Any]     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Any     					=    ["image_embeds", "negative_image_embeds", "image", "hint"]
		__lowercase	:	Union[str, Any]     					=    [
		    "generator",
		    "height",
		    "width",
		    "strength",
		    "guidance_scale",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		__lowercase	:	Optional[int]     					=    False



		@property
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return self.time_input_dim * 4



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				return 100



		@property
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				torch.manual_seed(0	)

				UpperCamelCase          =					{
				    'in_channels': 8,
				    # Out channels is double in channels because predicts mean and variance
				    'out_channels': 8,
				    'addition_embed_type': 'image_hint',
				    '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          =					UNetaDConditionModel(**A_	)
				return model



		@property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return {
				    "block_out_channels": [32, 32, 64, 64],
				    "down_block_types": [
				        "DownEncoderBlock2D",
				        "DownEncoderBlock2D",
				        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
				    "vq_embed_dim": 4,
				}



		@property
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				torch.manual_seed(0	)
				UpperCamelCase          =					VQModel(**self.dummy_movq_kwargs	)
				return model



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.dummy_unet
				UpperCamelCase          =					self.dummy_movq

				UpperCamelCase          =					{
				    'num_train_timesteps': 1_000,
				    'beta_schedule': 'linear',
				    'beta_start': 0.0_0085,
				    'beta_end': 0.012,
				    'clip_sample': False,
				    'set_alpha_to_one': False,
				    'steps_offset': 0,
				    'prediction_type': 'epsilon',
				    'thresholding': False,
				}

				UpperCamelCase          =					DDIMScheduler(**A_	)

				UpperCamelCase          =					{
				    'unet': unet,
				    'scheduler': scheduler,
				    'movq': movq,
				}

				return components



		def __UpperCamelCase			(			self    ,				A_    ,				A_=0	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(A_	)	).to(A_	)
				UpperCamelCase          =					floats_tensor((1, self.text_embedder_hidden_size)    ,				rng=random.Random(seed + 1	)	).to(
				    A_	)
				# create init_image
				UpperCamelCase          =					floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(A_	)	).to(A_	)
				UpperCamelCase          =					image.cpu().permute(0    ,				2    ,				3    ,				1	)[0]
				UpperCamelCase          =					Image.fromarray(np.uinta(A_	)	).convert('RGB'	).resize((256, 256)	)
				# create hint
				UpperCamelCase          =					floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(A_	)	).to(A_	)

				if str(A_	).startswith('mps'	):
						UpperCamelCase          =					torch.manual_seed(A_	)
				else:
						UpperCamelCase          =					torch.Generator(device=A_	).manual_seed(A_	)
				UpperCamelCase          =					{
				    'image': init_image,
				    'image_embeds': image_embeds,
				    'negative_image_embeds': negative_image_embeds,
				    'hint': hint,
				    'generator': generator,
				    'height': 64,
				    'width': 64,
				    'num_inference_steps': 10,
				    'guidance_scale': 7.0,
				    'strength': 0.2,
				    'output_type': 'np',
				}
				return inputs



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					'cpu'

				UpperCamelCase          =					self.get_dummy_components()

				UpperCamelCase          =					self.pipeline_class(**A_	)
				UpperCamelCase          =					pipe.to(A_	)

				pipe.set_progress_bar_config(disable=A_	)

				UpperCamelCase          =					pipe(**self.get_dummy_inputs(A_	)	)
				UpperCamelCase          =					output.images

				UpperCamelCase          =					pipe(
				    **self.get_dummy_inputs(A_	)    ,				return_dict=A_    ,				)[0]

				UpperCamelCase          =					image[0, -3:, -3:, -1]
				UpperCamelCase          =					image_from_tuple[0, -3:, -3:, -1]

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

				UpperCamelCase          =					np.array(
				    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]	)
				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 __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'	)
				UpperCamelCase          =					init_image.resize((512, 512)	)

				UpperCamelCase          =					load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/kandinskyv22/hint_image_cat.png'	)
				UpperCamelCase          =					torch.from_numpy(np.array(A_	)	).float() / 255.0
				UpperCamelCase          =					hint.permute(2    ,				0    ,				1	).unsqueeze(0	)

				UpperCamelCase          =					'A robot, 4k photo'

				UpperCamelCase          =					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-prior'    ,				torch_dtype=torch.floataa	)
				pipe_prior.to(A_	)

				UpperCamelCase          =					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
				    'kandinsky-community/kandinsky-2-2-controlnet-depth'    ,				torch_dtype=torch.floataa	)
				UpperCamelCase          =					pipeline.to(A_	)

				pipeline.set_progress_bar_config(disable=A_	)

				UpperCamelCase          =					torch.Generator(device='cpu'	).manual_seed(0	)

				UpperCamelCase     ,  UpperCamelCase          =					pipe_prior(
				    A_    ,				image=A_    ,				strength=0.85    ,				generator=A_    ,				negative_prompt=''    ,				).to_tuple()

				UpperCamelCase          =					pipeline(
				    image=A_    ,				image_embeds=A_    ,				negative_image_embeds=A_    ,				hint=A_    ,				generator=A_    ,				num_inference_steps=100    ,				height=512    ,				width=512    ,				strength=0.5    ,				output_type='np'    ,				)

				UpperCamelCase          =					output.images[0]

				assert image.shape == (512, 512, 3)

				assert_mean_pixel_difference(A_    ,				A_	)
