
"""simple docstring"""







import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class  A__       (	unittest.TestCase):

		@property
		def 							__lowerCamelCase							(      self ):
			torch.manual_seed(0 )
			__lowerCAmelCase			:						Optional[Any]									=      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')       ,		)
			return model

		@property
		def 							__lowerCamelCase							(      self ):
			torch.manual_seed(0 )
			__lowerCAmelCase			:						Optional[Any]									=      VQModel(
			    block_out_channels=[32, 64]       ,		in_channels=3       ,		out_channels=3       ,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,		latent_channels=3       ,		)
			return model

		@property
		def 							__lowerCamelCase							(      self ):
			torch.manual_seed(0 )
			__lowerCAmelCase			:						Tuple									=      CLIPTextConfig(
			    bos_token_id=0       ,		eos_token_id=2       ,		hidden_size=32       ,		intermediate_size=37       ,		layer_norm_eps=1E-05       ,		num_attention_heads=4       ,		num_hidden_layers=5       ,		pad_token_id=1       ,		vocab_size=10_00       ,		)
			return CLIPTextModel(_a )







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[Any]									=      self.dummy_uncond_unet
			__lowerCAmelCase			:						Tuple									=      DDIMScheduler()
			__lowerCAmelCase			:						List[Any]									=      self.dummy_vq_model

			__lowerCAmelCase			:						Union[str, Any]									=      LDMPipeline(unet=_a       ,		vqvae=_a       ,		scheduler=_a )
			ldm.to(_a )
			ldm.set_progress_bar_config(disable=_a )

			__lowerCAmelCase			:						str									=      torch.manual_seed(0 )
			__lowerCAmelCase			:						str									=      ldm(generator=_a       ,		num_inference_steps=2       ,		output_type='numpy' ).images

			__lowerCAmelCase			:						int									=      torch.manual_seed(0 )
			__lowerCAmelCase			:						List[Any]									=      ldm(generator=_a       ,		num_inference_steps=2       ,		output_type='numpy'       ,		return_dict=_a )[0]

			__lowerCAmelCase			:						List[str]									=      image[0, -3:, -3:, -1]
			__lowerCAmelCase			:						Dict									=      image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			__lowerCAmelCase			:						Any									=      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
			__lowerCAmelCase			:						Tuple									=      1E-2 if torch_device != "mps" else 3E-2

			assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance




@slow
@require_torch
class  A__       (	unittest.TestCase):

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Union[str, Any]									=      LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
			ldm.to(_a )
			ldm.set_progress_bar_config(disable=_a )

			__lowerCAmelCase			:						Optional[int]									=      torch.manual_seed(0 )
			__lowerCAmelCase			:						Union[str, Any]									=      ldm(generator=_a       ,		num_inference_steps=5       ,		output_type='numpy' ).images

			__lowerCAmelCase			:						Optional[Any]									=      image[0, -3:, -3:, -1]

			assert image.shape == (1, 2_56, 2_56, 3)
			__lowerCAmelCase			:						List[Any]									=      np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
			__lowerCAmelCase			:						int									=      1E-2 if torch_device != "mps" else 3E-2

			assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance

import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]   )				->		Optional[Any]:







	'''simple docstring'''


	if isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     np.full((len(_snake_case   ), sequence_length, 2)      ,   _snake_case   )
	else:
		__magic_name__		:		List[Any]	   =     np.full((len(_snake_case   ), sequence_length)      ,   _snake_case   )

	for i, tensor in enumerate(_snake_case   ):
		if padding_side == "right":
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Union[str, Any]	   =     tensor[:sequence_length]
		else:
			if isinstance(_snake_case      ,   _snake_case   ):
				__magic_name__		:		List[Any]	   =     tensor[:sequence_length]
			else:
				__magic_name__		:		Optional[Any]	   =     tensor[:sequence_length]

	return out_tensor.tolist()






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     ord(_snake_case   )
	if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
		return True
	__magic_name__		:		Any	   =     unicodedata.category(_snake_case   )
	if cat.startswith("P"   ):
		return True
	return False





@dataclass
class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    42
			UpperCamelCase__          =    True
			UpperCamelCase__          =    None
			UpperCamelCase__          =    None
			UpperCamelCase__          =    -100
			UpperCamelCase__          =    "pt"







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				import torch

				__magic_name__		:		List[str]	   =     "label" if "label" in features[0].keys() else "labels"
				__magic_name__		:		Union[str, Any]	   =     [feature[label_name] for feature in features] if label_name in features[0].keys() else None
				__magic_name__		:		Optional[int]	   =     self.tokenizer.pad(
				    _a						,   padding=self.padding						,   max_length=self.max_length						,   pad_to_multiple_of=self.pad_to_multiple_of						,   return_tensors="pt" if labels is None else None						,   )

				if labels is None:
					return batch

				__magic_name__		:		Dict	   =     torch.tensor(batch["entity_ids"]		).shape[1]
				__magic_name__		:		List[Any]	   =     self.tokenizer.padding_side
				if padding_side == "right":
					__magic_name__		:		str	   =     [
					    list(_a		) + [self.label_pad_token_id] * (sequence_length - len(_a		)) for label in labels
					]
				else:
					__magic_name__		:		int	   =     [
					    [self.label_pad_token_id] * (sequence_length - len(_a		)) + list(_a		) for label in labels
					]

				__magic_name__		:		Dict	   =     [feature["ner_tags"] for feature in features]
				__magic_name__		:		List[Any]	   =     padding_tensor(_a						,   -1						,   _a						,   _a		)
				__magic_name__		:		Any	   =     [feature["original_entity_spans"] for feature in features]
				__magic_name__		:		Any	   =     padding_tensor(_a						,   (-1, -1)						,   _a						,   _a		)
				__magic_name__		:		List[Any]	   =     {k: torch.tensor(_a						,   dtype=torch.intaa		) for k, v in batch.items()}

				return batch
