

import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MarkupLMForQuestionAnswering,
     MarkupLMForSequenceClassification,
     MarkupLMForTokenClassification,
     MarkupLMModel,
 )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer






class 	__magic_name__				:






     def __init__(							self		,	_lowercase		,	_lowercase=13		,	_lowercase=7		,	_lowercase=True		,	_lowercase=True		,	_lowercase=True		,	_lowercase=True		,	_lowercase=99		,	_lowercase=32		,	_lowercase=5		,	_lowercase=4		,	_lowercase=37		,	_lowercase="gelu"		,	_lowercase=0.1		,	_lowercase=0.1		,	_lowercase=512		,	_lowercase=16		,	_lowercase=2		,	_lowercase=0.02		,	_lowercase=3		,	_lowercase=None		,	_lowercase=20		,	_lowercase=30		,	_lowercase=2		,	_lowercase=2		,	_lowercase=10		,	)->    Any:
         UpperCamelCase_      =	parent
         UpperCamelCase_      =	batch_size
         UpperCamelCase_      =	seq_length
         UpperCamelCase_      =	is_training
         UpperCamelCase_      =	use_input_mask
         UpperCamelCase_      =	use_token_type_ids
         UpperCamelCase_      =	use_labels
         UpperCamelCase_      =	vocab_size
         UpperCamelCase_      =	hidden_size
         UpperCamelCase_      =	num_hidden_layers
         UpperCamelCase_      =	num_attention_heads
         UpperCamelCase_      =	intermediate_size
         UpperCamelCase_      =	hidden_act
         UpperCamelCase_      =	hidden_dropout_prob
         UpperCamelCase_      =	attention_probs_dropout_prob
         UpperCamelCase_      =	max_position_embeddings
         UpperCamelCase_      =	type_vocab_size
         UpperCamelCase_      =	type_sequence_label_size
         UpperCamelCase_      =	initializer_range
         UpperCamelCase_      =	num_labels
         UpperCamelCase_      =	scope
         UpperCamelCase_      =	max_xpath_tag_unit_embeddings
         UpperCamelCase_      =	max_xpath_subs_unit_embeddings
         UpperCamelCase_      =	tag_pad_id
         UpperCamelCase_      =	subs_pad_id
         UpperCamelCase_      =	max_depth






     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         UpperCamelCase_      =	ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size  )

         UpperCamelCase_      =	ids_tensor(
             [self.batch_size, self.seq_length, self.max_depth]		,	self.max_xpath_tag_unit_embeddings  )

         UpperCamelCase_      =	ids_tensor(
             [self.batch_size, self.seq_length, self.max_depth]		,	self.max_xpath_subs_unit_embeddings  )

         UpperCamelCase_      =	None
         if self.use_input_mask:
             UpperCamelCase_      =	ids_tensor([self.batch_size, self.seq_length]		,	vocab_size=2  )

         UpperCamelCase_      =	None
         if self.use_token_type_ids:
             UpperCamelCase_      =	ids_tensor([self.batch_size, self.seq_length]		,	self.type_vocab_size  )

         UpperCamelCase_      =	None
         UpperCamelCase_      =	None
         if self.use_labels:
             UpperCamelCase_      =	ids_tensor([self.batch_size]		,	self.type_sequence_label_size  )
             UpperCamelCase_      =	ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels  )

         UpperCamelCase_      =	self.get_config()

         return (
             config,
             input_ids,
             xpath_tags_seq,
             xpath_subs_seq,
             token_type_ids,
             input_mask,
             sequence_labels,
             token_labels,
         )






     def   UpperCAmelCase_							(							self  )->    Optional[int]:
         return MarkupLMConfig(
             vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	hidden_act=self.hidden_act		,	hidden_dropout_prob=self.hidden_dropout_prob		,	attention_probs_dropout_prob=self.attention_probs_dropout_prob		,	max_position_embeddings=self.max_position_embeddings		,	type_vocab_size=self.type_vocab_size		,	initializer_range=self.initializer_range		,	max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings		,	max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings		,	tag_pad_id=self.tag_pad_id		,	subs_pad_id=self.subs_pad_id		,	max_depth=self.max_depth		,	)






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	)->    Optional[Any]:
         UpperCamelCase_      =	MarkupLMModel(config=_lowercase  )
         model.to(_lowercase  )
         model.eval()
         print("Configs:"		,	model.config.tag_pad_id		,	model.config.subs_pad_id  )
         UpperCamelCase_      =	model(_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase  )
         UpperCamelCase_      =	model(_lowercase		,	token_type_ids=_lowercase  )
         UpperCamelCase_      =	model(_lowercase  )
         self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)  )
         self.parent.assertEqual(result.pooler_output.shape		,	(self.batch_size, self.hidden_size)  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	)->    List[Any]:
         UpperCamelCase_      =	self.num_labels
         UpperCamelCase_      =	MarkupLMForSequenceClassification(_lowercase  )
         model.to(_lowercase  )
         model.eval()
         UpperCamelCase_      =	model(
             _lowercase		,	xpath_tags_seq=_lowercase		,	xpath_subs_seq=_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase		,	labels=_lowercase		,	)
         self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	)->    Union[str, Any]:
         UpperCamelCase_      =	self.num_labels
         UpperCamelCase_      =	MarkupLMForTokenClassification(config=_lowercase  )
         model.to(_lowercase  )
         model.eval()
         UpperCamelCase_      =	model(
             _lowercase		,	xpath_tags_seq=_lowercase		,	xpath_subs_seq=_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase		,	labels=_lowercase		,	)
         self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.num_labels)  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	)->    Tuple:
         UpperCamelCase_      =	MarkupLMForQuestionAnswering(config=_lowercase  )
         model.to(_lowercase  )
         model.eval()
         UpperCamelCase_      =	model(
             _lowercase		,	xpath_tags_seq=_lowercase		,	xpath_subs_seq=_lowercase		,	attention_mask=_lowercase		,	token_type_ids=_lowercase		,	start_positions=_lowercase		,	end_positions=_lowercase		,	)
         self.parent.assertEqual(result.start_logits.shape		,	(self.batch_size, self.seq_length)  )
         self.parent.assertEqual(result.end_logits.shape		,	(self.batch_size, self.seq_length)  )






     def   UpperCAmelCase_							(							self  )->    Optional[int]:
         UpperCamelCase_      =	self.prepare_config_and_inputs()
         (
             (
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					(
             UpperCamelCase_
         )     ,					
         )      =	config_and_inputs
         UpperCamelCase_      =	{
             "input_ids": input_ids,
             "xpath_tags_seq": xpath_tags_seq,
             "xpath_subs_seq": xpath_subs_seq,
             "token_type_ids": token_type_ids,
             "attention_mask": input_mask,
         }
         return config, inputs_dict






@require_torch
class 	__magic_name__				(       snake_case ,			snake_case ,			unittest.TestCase							):
     UpperCamelCase_  :List[str]								=			(
         (
             MarkupLMModel,
             MarkupLMForSequenceClassification,
             MarkupLMForTokenClassification,
             MarkupLMForQuestionAnswering,
         )
         if is_torch_available()
         else None
     )
     UpperCamelCase_  :str								=			(
         {
             """feature-extraction""": MarkupLMModel,
             """question-answering""": MarkupLMForQuestionAnswering,
             """text-classification""": MarkupLMForSequenceClassification,
             """token-classification""": MarkupLMForTokenClassification,
             """zero-shot""": MarkupLMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    List[Any]:
         # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
         # (batch of pretokenized examples).
         return True






     def   UpperCAmelCase_							(							self  )->    int:
         UpperCamelCase_      =	MarkupLMModelTester(self  )
         UpperCamelCase_      =	ConfigTester(self		,	config_class=_lowercase		,	hidden_size=37  )






     def   UpperCAmelCase_							(							self  )->    str:
         self.config_tester.run_common_tests()






     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Optional[int]:
         UpperCamelCase_      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_lowercase  )






     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_lowercase  )




def  lowerCAmelCase(			)->	List[Any]:



    """simple docstring"""






    UpperCamelCase_      =	"\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    "

    return html_string






@require_torch
class 	__magic_name__				(       unittest.TestCase							):






     @cached_property
     def   UpperCAmelCase_							(							self  )->    Any:
         # TODO use from_pretrained here
         UpperCamelCase_      =	MarkupLMFeatureExtractor()
         UpperCamelCase_      =	MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"  )

         return MarkupLMProcessor(_lowercase		,	_lowercase  )






     @slow
     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	MarkupLMModel.from_pretrained("microsoft/markuplm-base"  ).to(_lowercase  )

         UpperCamelCase_      =	self.default_processor

         UpperCamelCase_      =	processor(prepare_html_string()		,	return_tensors="pt"  )
         UpperCamelCase_      =	inputs.to(_lowercase  )

         # forward pass
         with torch.no_grad():
             UpperCamelCase_      =	model(**_lowercase  )

         # verify the last hidden states
         UpperCamelCase_      =	torch.Size([1, 14, 768]  )
         self.assertEqual(outputs.last_hidden_state.shape		,	_lowercase  )

         UpperCamelCase_      =	torch.tensor(
             [[0.0_675, -0.0_052, 0.5_001], [-0.2_281, 0.0_802, 0.2_192], [-0.0_583, -0.3_311, 0.1_185]]  ).to(_lowercase  )

         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]		,	_lowercase		,	atol=1e-4  )  )



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 	__magic_name__				(       snake_case ,			unittest.TestCase							):
     UpperCamelCase_  :Any								=			KandinskyPipeline
     UpperCamelCase_  :str								=			[
         """prompt""",
         """image_embeds""",
         """negative_image_embeds""",
     ]
     UpperCamelCase_  :Union[str, Any]								=			["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
     UpperCamelCase_  :int								=			[
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     UpperCamelCase_  :Optional[Any]								=			False






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    str:
         return self.time_input_dim






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         return self.time_input_dim * 4






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         return 100






     @property
     def   UpperCAmelCase_							(							self  )->    List[Any]:
         UpperCamelCase_      =	XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"  )
         return tokenizer






     @property
     def   UpperCAmelCase_							(							self  )->    int:
         torch.manual_seed(0  )
         UpperCamelCase_      =	MCLIPConfig(
             numDims=self.cross_attention_dim		,	transformerDimensions=self.text_embedder_hidden_size		,	hidden_size=self.text_embedder_hidden_size		,	intermediate_size=37		,	num_attention_heads=4		,	num_hidden_layers=5		,	vocab_size=1_005		,	)

         UpperCamelCase_      =	MultilingualCLIP(_lowercase  )
         UpperCamelCase_      =	text_encoder.eval()

         return text_encoder






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         torch.manual_seed(0  )

         UpperCamelCase_      =	{
             "in_channels": 4,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "text_image",
             "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": "text_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(**_lowercase  )
         return model






     @property
     def   UpperCAmelCase_							(							self  )->    Dict:
         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["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",
             ],
             "vq_embed_dim": 4,
         }






     @property
     def   UpperCAmelCase_							(							self  )->    Tuple:
         torch.manual_seed(0  )
         UpperCamelCase_      =	VQModel(**self.dummy_movq_kwargs  )
         return model






     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	self.dummy_text_encoder
         UpperCamelCase_      =	self.dummy_tokenizer
         UpperCamelCase_      =	self.dummy_unet
         UpperCamelCase_      =	self.dummy_movq

         UpperCamelCase_      =	DDIMScheduler(
             num_train_timesteps=1_000		,	beta_schedule="linear"		,	beta_start=0.00_085		,	beta_end=0.012		,	clip_sample=_lowercase		,	set_alpha_to_one=_lowercase		,	steps_offset=1		,	prediction_type="epsilon"		,	thresholding=_lowercase		,	)

         UpperCamelCase_      =	{
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase=0  )->    Union[str, Any]:
         UpperCamelCase_      =	floats_tensor((1, self.cross_attention_dim)		,	rng=random.Random(_lowercase  )  ).to(_lowercase  )
         UpperCamelCase_      =	floats_tensor((1, self.cross_attention_dim)		,	rng=random.Random(seed + 1  )  ).to(_lowercase  )
         if str(_lowercase  ).startswith("mps"  ):
             UpperCamelCase_      =	torch.manual_seed(_lowercase  )
         else:
             UpperCamelCase_      =	torch.Generator(device=_lowercase  ).manual_seed(_lowercase  )
         UpperCamelCase_      =	{
             "prompt": "horse",
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 64,
             "width": 64,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs






     def   UpperCAmelCase_							(							self  )->    Tuple:
         UpperCamelCase_      =	"cpu"

         UpperCamelCase_      =	self.get_dummy_components()

         UpperCamelCase_      =	self.pipeline_class(**_lowercase  )
         UpperCamelCase_      =	pipe.to(_lowercase  )

         pipe.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	pipe(**self.get_dummy_inputs(_lowercase  )  )
         UpperCamelCase_      =	output.images

         UpperCamelCase_      =	pipe(
             **self.get_dummy_inputs(_lowercase  )		,	return_dict=_lowercase		,	)[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(
             [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

         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 	__magic_name__				(       unittest.TestCase							):






     def   UpperCAmelCase_							(							self  )->    Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_text2img_cat_fp16.npy"  )

         UpperCamelCase_      =	KandinskyPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-prior"		,	torch_dtype=torch.floataa  )
         pipe_prior.to(_lowercase  )

         UpperCamelCase_      =	KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"		,	torch_dtype=torch.floataa  )
         UpperCamelCase_      =	pipeline.to(_lowercase  )
         pipeline.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	"red cat, 4k photo"

         UpperCamelCase_      =	torch.Generator(device="cuda"  ).manual_seed(0  )
         UpperCamelCase_     ,					UpperCamelCase_      =	pipe_prior(
             _lowercase		,	generator=_lowercase		,	num_inference_steps=5		,	negative_prompt=""		,	).to_tuple()

         UpperCamelCase_      =	torch.Generator(device="cuda"  ).manual_seed(0  )
         UpperCamelCase_      =	pipeline(
             _lowercase		,	image_embeds=_lowercase		,	negative_image_embeds=_lowercase		,	generator=_lowercase		,	num_inference_steps=100		,	output_type="np"		,	)

         UpperCamelCase_      =	output.images[0]

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

         assert_mean_pixel_difference(_lowercase		,	_lowercase  )
