
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy


class 							lowercase   (						__UpperCAmelCase  ):
		__lowercase			:			Optional[int]      	=							"MarkupLMFeatureExtractor"
		__lowercase			:			Tuple      	=							("MarkupLMTokenizer", "MarkupLMTokenizerFast")
		__lowercase			:			Union[str, Any]      	=							True



		def __call__(					self							, A_=None							, A_=None							, A_=None							, A_=None							, A_=None							, A_ = True							, A_ = False							, A_ = None							, A_ = None							, A_ = 0							, A_ = None							, A_ = None							, A_ = None							, A_ = False							, A_ = False							, A_ = False							, A_ = False							, A_ = True							, A_ = None							, **A_							, )    ->   Optional[Any]:
				"""simple docstring"""





				if self.parse_html:
						if html_strings is None:
								raise ValueError('Make sure to pass HTML strings in case `parse_html` is set to `True`'			)

						if nodes is not None or xpaths is not None or node_labels is not None:
								raise ValueError(
								    'Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'			)

						UpperCamelCase            =	self.feature_extractor(__snake_case			)
						UpperCamelCase            =	features['nodes']
						UpperCamelCase            =	features['xpaths']
				else:
						if html_strings is not None:
								raise ValueError('You have passed HTML strings but `parse_html` is set to `False`.'			)
						if nodes is None or xpaths is None:
								raise ValueError('Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'			)

        # # second, apply the tokenizer
				if questions is not None and self.parse_html:
						if isinstance(__snake_case							, __snake_case			):
								UpperCamelCase            =	[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

				UpperCamelCase            =	self.tokenizer(
				    text=questions if questions is not None else nodes							, text_pair=nodes if questions is not None else None							, xpaths=__snake_case							, node_labels=__snake_case							, add_special_tokens=__snake_case							, padding=__snake_case							, truncation=__snake_case							, max_length=__snake_case							, stride=__snake_case							, pad_to_multiple_of=__snake_case							, return_token_type_ids=__snake_case							, return_attention_mask=__snake_case							, return_overflowing_tokens=__snake_case							, return_special_tokens_mask=__snake_case							, return_offsets_mapping=__snake_case							, return_length=__snake_case							, verbose=__snake_case							, return_tensors=__snake_case							, **__snake_case							, )

				return encoded_inputs



		def __UpperCamelCase					(					self							, *A_							, **A_			)    ->   str:
				"""simple docstring"""
				return self.tokenizer.batch_decode(*__snake_case							, **__snake_case			)



		def __UpperCamelCase					(					self							, *A_							, **A_			)    ->   str:
				"""simple docstring"""
				return self.tokenizer.decode(*__snake_case							, **__snake_case			)



		@property
		def __UpperCamelCase					(					self			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	self.tokenizer.model_input_names
				return tokenizer_input_names







'''simple docstring'''






import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor







class       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	bool = False							):





											'''simple docstring'''





											_snake_case: Any	       =		10

											if only_cross_attention:
																	_snake_case: Optional[int]	       =		12
											else:
																	# when only cross attention is not set, the cross attention dim must be the same as the query dim
																	_snake_case: Optional[int]	       =		query_dim

											return {
											    "query_dim": query_dim,
											    "cross_attention_dim": cross_attention_dim,
											    "heads": 2,
											    "dim_head": 4,
											    "added_kv_proj_dim": 6,
											    "norm_num_groups": 1,
											    "only_cross_attention": only_cross_attention,
											    "processor": AttnAddedKVProcessor(),
											}


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	List[Any]							,  __snake_case						:	Tuple							):





											'''simple docstring'''





											_snake_case: Optional[int]	       =		2

											_snake_case: Union[str, Any]	       =		torch.rand(__snake_case							,  __snake_case							,  3							,  2							)
											_snake_case: Optional[int]	       =		torch.rand(__snake_case							,  4							,  __snake_case							)
											_snake_case: int	       =		None

											return {
											    "hidden_states": hidden_states,
											    "encoder_hidden_states": encoder_hidden_states,
											    "attention_mask": attention_mask,
											}




					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											torch.manual_seed(0							)

											_snake_case: int	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Union[str, Any]	       =		Attention(**__snake_case							)

											self.assertTrue(attn.to_k is not None							)
											self.assertTrue(attn.to_v is not None							)

											_snake_case: Optional[int]	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

											# only self attention

											torch.manual_seed(0							)

											_snake_case: str	       =		self.get_constructor_arguments(only_cross_attention=__snake_case							)
											_snake_case: Optional[Any]	       =		Attention(**__snake_case							)

											self.assertTrue(attn.to_k is None							)
											self.assertTrue(attn.to_v is None							)

											_snake_case: Dict	       =		self.get_forward_arguments(
											    query_dim=constructor_args['query_dim']							,  added_kv_proj_dim=constructor_args['added_kv_proj_dim']							)

											_snake_case: str	       =		attn(**__snake_case							)

											self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()							)







class       lowerCamelCase       (    unittest.TestCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: str	       =		DiffusionPipeline.from_pretrained('hf-internal-testing/tiny-stable-diffusion-pipe'							,  safety_checker=__snake_case							)

											_snake_case: int	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											# the initial conversion succeeds
											_snake_case: Dict	       =		DiffusionPipeline.from_pretrained(
											    'hf-internal-testing/tiny-stable-diffusion-pipe'							,  device_map='sequential'							,  safety_checker=__snake_case							)

											_snake_case: Any	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											with tempfile.TemporaryDirectory() as tmpdir:
																	# save the converted model
																	pipe.save_pretrained(__snake_case							)

																	# can also load the converted weights
																	_snake_case: Union[str, Any]	       =		DiffusionPipeline.from_pretrained(__snake_case							,  device_map='sequential'							,  safety_checker=__snake_case							)

											_snake_case: Union[str, Any]	       =		pipe(
											    'foo'							,  num_inference_steps=2							,  generator=torch.Generator('cpu'							).manual_seed(0							)							,  output_type='np'							,  ).images

											self.assertTrue(np.allclose(__snake_case							,  __snake_case							,  atol=1e-5							)							)
											self.assertTrue(np.allclose(__snake_case							,  __snake_case							,  atol=1e-5							)							)





