'''simple docstring'''






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   AltDiffusionPipeline
   __lowerCAmelCase         =   TEXT_TO_IMAGE_PARAMS
   __lowerCAmelCase         =   TEXT_TO_IMAGE_BATCH_PARAMS
   __lowerCAmelCase         =   TEXT_TO_IMAGE_IMAGE_PARAMS
   __lowerCAmelCase         =   TEXT_TO_IMAGE_IMAGE_PARAMS
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[str]:
        torch.manual_seed(0     )
        _A    		=  UNetaDConditionModel(
            block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'), up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'), cross_attention_dim=32, )
        _A    		=  DDIMScheduler(
            beta_start=0.00_085, beta_end=0.012, beta_schedule='scaled_linear', clip_sample=UpperCamelCase__, set_alpha_to_one=UpperCamelCase__, )
        torch.manual_seed(0     )
        _A    		=  AutoencoderKL(
            block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'], up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'], latent_channels=4, )

        # TODO: address the non-deterministic text encoder (fails for save-load tests)
        # torch.manual_seed(0)
        # text_encoder_config = RobertaSeriesConfig(
        #     hidden_size=32,
        #     project_dim=32,
        #     intermediate_size=37,
        #     layer_norm_eps=1e-05,
        #     num_attention_heads=4,
        #     num_hidden_layers=5,
        #     vocab_size=5002,
        # )
        # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

        torch.manual_seed(0     )
        _A    		=  CLIPTextConfig(
            bos_token_id=0, eos_token_id=2, hidden_size=32, projection_dim=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=50_02, )
        _A    		=  CLIPTextModel(UpperCamelCase__     )

        _A    		=  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'     )
        _A    		=  77

        _A    		=  {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			str, UpperCamelCase__      :			str=0     )  ->			Tuple:
        if str(UpperCamelCase__     ).startswith('mps'     ):
             _A    		=  torch.manual_seed(UpperCamelCase__     )
        else:
             _A    		=  torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )
        _A    		=  {
            'prompt': 'A painting of a squirrel eating a burger',
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Dict:
        super().test_attention_slicing_forward_pass(expected_max_diff=3e-3     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Any:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Union[str, Any]:
        _A    		=  'cpu'  # ensure determinism for the device-dependent torch.Generator

        _A    		=  self.get_dummy_components()
        torch.manual_seed(0     )
        _A    		=  RobertaSeriesConfig(
            hidden_size=32, project_dim=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, vocab_size=50_02, )
        # TODO: remove after fixing the non-deterministic text encoder
        _A    		=  RobertaSeriesModelWithTransformation(UpperCamelCase__     )
        _A    		=  text_encoder

        _A    		=  AltDiffusionPipeline(**UpperCamelCase__     )
        _A    		=  alt_pipe.to(UpperCamelCase__     )
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__     )
        _A    		=  'A photo of an astronaut'
        _A    		=  alt_pipe(**UpperCamelCase__     )
        _A    		=  output.images
        _A    		=  image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _A    		=  np.array(
            [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]     )

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

   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Optional[Any]:
        _A    		=  'cpu'  # ensure determinism for the device-dependent torch.Generator

        _A    		=  self.get_dummy_components()
        _A    		=  PNDMScheduler(skip_prk_steps=UpperCamelCase__     )
        torch.manual_seed(0     )
        _A    		=  RobertaSeriesConfig(
            hidden_size=32, project_dim=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, vocab_size=50_02, )
        # TODO: remove after fixing the non-deterministic text encoder
        _A    		=  RobertaSeriesModelWithTransformation(UpperCamelCase__     )
        _A    		=  text_encoder
        _A    		=  AltDiffusionPipeline(**UpperCamelCase__     )
        _A    		=  alt_pipe.to(UpperCamelCase__     )
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  self.get_dummy_inputs(UpperCamelCase__     )
        _A    		=  alt_pipe(**UpperCamelCase__     )
        _A    		=  output.images
        _A    		=  image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _A    		=  np.array(
            [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]     )

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







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







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			str:
        # make sure here that pndm scheduler skips prk
        _A    		=  AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion', safety_checker=UpperCamelCase__     )
        _A    		=  alt_pipe.to(UpperCamelCase__     )
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  'A painting of a squirrel eating a burger'
        _A    		=  torch.manual_seed(0     )
        _A    		=  alt_pipe([prompt], generator=UpperCamelCase__, guidance_scale=6.0, num_inference_steps=20, output_type='np'     )

        _A    		=  output.images

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

        assert image.shape == (1, 5_12, 5_12, 3)
        _A    		=  np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]     )

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

   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Dict:
        _A    		=  DDIMScheduler.from_pretrained('BAAI/AltDiffusion', subfolder='scheduler'     )

        _A    		=  AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion', scheduler=UpperCamelCase__, safety_checker=UpperCamelCase__     )
        _A    		=  alt_pipe.to(UpperCamelCase__     )
        alt_pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  'A painting of a squirrel eating a burger'
        _A    		=  torch.manual_seed(0     )

        _A    		=  alt_pipe([prompt], generator=UpperCamelCase__, num_inference_steps=2, output_type='numpy'     )
        _A    		=  output.images

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

        assert image.shape == (1, 5_12, 5_12, 3)
        _A    		=  np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]     )

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








"""simple docstring"""







class       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__ = ""						,	SCREAMING_SNAKE_CASE__ = False		)      ->	None:
				# Mapping from the first character of the prefix of the node
				A__											=					{}

				# A node will be a leaf if the tree contains its word
				A__											=					is_leaf

				A__											=					prefix


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	tuple[str, str, str]:
				A__											=					0
				for q, w in zip(self.prefix						,	SCREAMING_SNAKE_CASE__		):
					if q != w:
						break

					x += 1

				return self.prefix[:x], self.prefix[x:], word[x:]


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				for word in words:
					self.insert(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				# Case 1: If the word is the prefix of the node
				# Solution: We set the current node as leaf
				if self.prefix == word:
					A__											=					True

				# Case 2: The node has no edges that have a prefix to the word
				# Solution: We create an edge from the current node to a new one
				# containing the word
				elif word[0] not in self.nodes:
					A__											=					RadixNode(prefix=SCREAMING_SNAKE_CASE__						,	is_leaf=SCREAMING_SNAKE_CASE__		)

				else:
					A__											=					self.nodes[word[0]]
					A__							,							A__							,							A__											=					incoming_node.match(
					    SCREAMING_SNAKE_CASE__		)

					# Case 3: The node prefix is equal to the matching
					# Solution: We insert remaining word on the next node
					if remaining_prefix == "":
						self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE__		)

					# Case 4: The word is greater equal to the matching
					# Solution: Create a node in between both nodes, change
					# prefixes and add the new node for the remaining word
					else:
						A__											=					remaining_prefix

						A__											=					self.nodes[matching_string[0]]
						A__											=					RadixNode(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
						A__											=					aux_node

						if remaining_word == "":
							A__											=					True
						else:
							self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	bool:
				A__											=					self.nodes.get(word[0]						,	SCREAMING_SNAKE_CASE__		)
				if not incoming_node:
					return False
				else:
					A__							,							A__							,							A__											=					incoming_node.match(
					    SCREAMING_SNAKE_CASE__		)
					# If there is remaining prefix, the word can't be on the tree
					if remaining_prefix != "":
						return False
					# This applies when the word and the prefix are equal
					elif remaining_word == "":
						return incoming_node.is_leaf
					# We have word remaining so we check the next node
					else:
						return incoming_node.find(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	bool:
				A__											=					self.nodes.get(word[0]						,	SCREAMING_SNAKE_CASE__		)
				if not incoming_node:
					return False
				else:
					A__							,							A__							,							A__											=					incoming_node.match(
					    SCREAMING_SNAKE_CASE__		)
					# If there is remaining prefix, the word can't be on the tree
					if remaining_prefix != "":
						return False
					# We have word remaining so we check the next node
					elif remaining_word != "":
						return incoming_node.delete(SCREAMING_SNAKE_CASE__		)
					else:
						# If it is not a leaf, we don't have to delete
						if not incoming_node.is_leaf:
							return False
						else:
							# We delete the nodes if no edges go from it
							if len(incoming_node.nodes		) == 0:
								del self.nodes[word[0]]
								# We merge the current node with its only child
								if len(self.nodes		) == 1 and not self.is_leaf:
									A__											=					list(self.nodes.values()		)[0]
									A__											=					merging_node.is_leaf
									self.prefix += merging_node.prefix
									A__											=					merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
							elif len(incoming_node.nodes		) > 1:
								A__											=					False
							# If there is 1 edge, we merge it with its child
							else:
								A__											=					list(incoming_node.nodes.values()		)[0]
								A__											=					merging_node.is_leaf
								incoming_node.prefix += merging_node.prefix
								A__											=					merging_node.nodes

							return True







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__ = 0		)      ->	None:
				if self.prefix != "":
					print("-" * height						,	self.prefix						,	"  (leaf)" if self.is_leaf else ""		)

				for value in self.nodes.values():
					value.print_tree(height + 1		)



def       _lowerCamelCase				(							)	-> bool:






	"""simple docstring"""





	A__											=					"banana bananas bandana band apple all beast".split()
	A__											=					RadixNode()
	root.insert_many(UpperCAmelCase_     )

	assert all(root.find(UpperCAmelCase_     ) for word in words     )
	assert not root.find("bandanas"     )
	assert not root.find("apps"     )
	root.delete("all"     )
	assert not root.find("all"     )
	root.delete("banana"     )
	assert not root.find("banana"     )
	assert root.find("bananas"     )

	return True

def       _lowerCamelCase				(							)	-> None:






	"""simple docstring"""





	assert test_trie()

def       _lowerCamelCase				(							)	-> None:






	"""simple docstring"""





	A__											=					RadixNode()
	A__											=					"banana bananas bandanas bandana band apple all beast".split()
	root.insert_many(UpperCAmelCase_     )

	print("Words:",	UpperCAmelCase_     )
	print("Tree:"     )
	root.print_tree()


if __name__ == "__main__":
			main()




