
'''simple docstring'''





import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase            = logging.get_logger(__name__)

def   A			(							A_				:       Union[str, Any]							):
						snake_case							:  Optional[Any]			=     tf.image.resize(A_     ,				(346, 346)							)
						snake_case							:  Optional[Any]			=     tf.image.crop_to_bounding_box(A_     ,				(346 - 289) // 2     ,				(346 - 289) // 2     ,				289     ,				289							)
						return image

def   A			(							):
						snake_case							:  Tuple			=     EfficientNetConfig.from_pretrained('''google/efficientnet-b7'''							)
						snake_case							:  Tuple			=     289
						snake_case							:  int			=     640
						snake_case							:  Optional[Any]			=     {'''0''': '''LABEL_0''', '''1''': '''LABEL_1'''}
						snake_case							:  Tuple			=     {'''LABEL_0''': 0, '''LABEL_1''': 1}
						snake_case							:  Optional[Any]			=     []

						snake_case							:  Any			=     BertConfig()
						snake_case							:  List[str]			=     AlignConfig.from_text_vision_configs(
						    text_config=A_     ,				vision_config=A_     ,				projection_dim=640							)
						return config

def   A			(							):
						snake_case							:  Tuple			=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case							:  Optional[Any]			=     Image.open(requests.get(A_     ,				stream=A_							).raw							)
						return im

def   A			(							):
						snake_case							:  List[str]			=     EfficientNetImageProcessor(
						    do_center_crop=A_     ,				rescale_factor=1 / 127.5     ,				rescale_offset=A_     ,				do_normalize=A_     ,				include_top=A_     ,				resample=Image.BILINEAR     ,				)
						snake_case							:  Tuple			=     BertTokenizer.from_pretrained('''bert-base-uncased'''							)
						snake_case							:  List[str]			=     64
						snake_case							:  List[str]			=     AlignProcessor(image_processor=A_     ,				tokenizer=A_							)
						return processor

def   A			(							A_				:       Dict							):
						# EfficientNet image encoder
						snake_case							:  Dict			=     [v.split('''_'''							)[0].split('''block'''							)[1] for v in original_param_names if v.startswith('''block'''							)]
						snake_case							:  str			=     list(set(A_							)							)
						snake_case							:  Union[str, Any]			=     sorted(A_							)
						snake_case							:  int			=     len(A_							)
						snake_case							:  Dict			=     {b: str(A_							) for b, i in zip(A_     ,				range(A_							)							)}

						snake_case							:  List[Any]			=     []
						rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''')							)
						rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''')							)
						rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''')							)
						rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''')							)
						rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''')							)

						for b in block_names:
												snake_case							:  List[Any]			=     block_name_mapping[b]
												rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")							)
												rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")							)
												rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")							)
												rename_keys.append(
												    (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")							)
												rename_keys.append(
												    (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")							)
												rename_keys.append(
												    (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")							)
												rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")							)
												rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")							)
												rename_keys.append(
												    (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")							)
												rename_keys.append(
												    (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")							)

												rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")							)
												rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")							)
												rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")							)
												rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")							)
												rename_keys.append(
												    (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""")							)
												rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""")							)
												rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""")							)
												rename_keys.append(
												    (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")							)
												rename_keys.append(
												    (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")							)

						snake_case							:  Optional[int]			=     {}
						for item in rename_keys:
												if item[0] in original_param_names:
																		snake_case							:  int			=     '''vision_model.''' + item[1]

    # BERT text encoder
						snake_case							:  Union[str, Any]			=     []
						snake_case							:  Optional[Any]			=     '''tf_bert_model/bert'''
						snake_case							:  Optional[Any]			=     '''text_model'''
						for i in range(12							):
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/attention/self/query/kernel:0""",
												        F"""{new}.encoder.layer.{i}.attention.self.query.weight""",
												    )							)
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/attention/self/query/bias:0""",
												        F"""{new}.encoder.layer.{i}.attention.self.query.bias""",
												    )							)
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/attention/self/key/kernel:0""",
												        F"""{new}.encoder.layer.{i}.attention.self.key.weight""",
												    )							)
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/attention/self/key/bias:0""",
												        F"""{new}.encoder.layer.{i}.attention.self.key.bias""",
												    )							)
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/attention/self/value/kernel:0""",
												        F"""{new}.encoder.layer.{i}.attention.self.value.weight""",
												    )							)
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/attention/self/value/bias:0""",
												        F"""{new}.encoder.layer.{i}.attention.self.value.bias""",
												    )							)
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/attention/output/dense/kernel:0""",
												        F"""{new}.encoder.layer.{i}.attention.output.dense.weight""",
												    )							)
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/attention/output/dense/bias:0""",
												        F"""{new}.encoder.layer.{i}.attention.output.dense.bias""",
												    )							)
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0""",
												        F"""{new}.encoder.layer.{i}.attention.output.LayerNorm.weight""",
												    )							)
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0""",
												        F"""{new}.encoder.layer.{i}.attention.output.LayerNorm.bias""",
												    )							)
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/intermediate/dense/kernel:0""",
												        F"""{new}.encoder.layer.{i}.intermediate.dense.weight""",
												    )							)
												rename_keys.append(
												    (
												        F"""{old}/encoder/layer_._{i}/intermediate/dense/bias:0""",
												        F"""{new}.encoder.layer.{i}.intermediate.dense.bias""",
												    )							)
												rename_keys.append(
												    (F"""{old}/encoder/layer_._{i}/output/dense/kernel:0""", F"""{new}.encoder.layer.{i}.output.dense.weight""")							)
												rename_keys.append(
												    (F"""{old}/encoder/layer_._{i}/output/dense/bias:0""", F"""{new}.encoder.layer.{i}.output.dense.bias""")							)
												rename_keys.append(
												    (F"""{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0""", F"""{new}.encoder.layer.{i}.output.LayerNorm.weight""")							)
												rename_keys.append(
												    (F"""{old}/encoder/layer_._{i}/output/LayerNorm/beta:0""", F"""{new}.encoder.layer.{i}.output.LayerNorm.bias""")							)

						rename_keys.append((F"""{old}/embeddings/word_embeddings/weight:0""", F"""{new}.embeddings.word_embeddings.weight""")							)
						rename_keys.append(
						    (F"""{old}/embeddings/position_embeddings/embeddings:0""", F"""{new}.embeddings.position_embeddings.weight""")							)
						rename_keys.append(
						    (F"""{old}/embeddings/token_type_embeddings/embeddings:0""", F"""{new}.embeddings.token_type_embeddings.weight""")							)
						rename_keys.append((F"""{old}/embeddings/LayerNorm/gamma:0""", F"""{new}.embeddings.LayerNorm.weight""")							)
						rename_keys.append((F"""{old}/embeddings/LayerNorm/beta:0""", F"""{new}.embeddings.LayerNorm.bias""")							)

						rename_keys.append((F"""{old}/pooler/dense/kernel:0""", F"""{new}.pooler.dense.weight""")							)
						rename_keys.append((F"""{old}/pooler/dense/bias:0""", F"""{new}.pooler.dense.bias""")							)
						rename_keys.append(('''dense/kernel:0''', '''text_projection.weight''')							)
						rename_keys.append(('''dense/bias:0''', '''text_projection.bias''')							)
						rename_keys.append(('''dense/bias:0''', '''text_projection.bias''')							)
						rename_keys.append(('''temperature:0''', '''temperature''')							)

						for item in rename_keys:
												if item[0] in original_param_names:
																		snake_case							:  Tuple			=     item[1]
						return key_mapping

def   A			(							A_				:       Union[str, Any]     ,				A_				:       List[str]     ,				A_				:       Any							):
						list(hf_params.keys()							)

						for key, value in tf_params.items():
												if key not in key_mapping:
																		continue

												snake_case							:  Optional[int]			=     key_mapping[key]
												if "_conv" in key and "kernel" in key:
																		snake_case							:  int			=     torch.from_numpy(A_							).permute(3     ,				2     ,				0     ,				1							)
												elif "embeddings" in key:
																		snake_case							:  Any			=     torch.from_numpy(A_							)
												elif "depthwise_kernel" in key:
																		snake_case							:  Tuple			=     torch.from_numpy(A_							).permute(2     ,				3     ,				0     ,				1							)
												elif "kernel" in key:
																		snake_case							:  Union[str, Any]			=     torch.from_numpy(np.transpose(A_							)							)
												elif "temperature" in key:
																		snake_case							:  Dict			=     value
												elif "bn/gamma" or "bn/beta" in key:
																		snake_case							:  Optional[int]			=     torch.from_numpy(np.transpose(A_							)							).squeeze()
												else:
																		snake_case							:  str			=     torch.from_numpy(A_							)

												# Replace HF parameters with original TF model parameters
												hf_params[hf_key].copy_(A_							)

@torch.no_grad()
def   A			(							A_				:       Union[str, Any]     ,				A_				:       Tuple     ,				A_				:       Optional[int]     ,				A_				:       List[str]							):
						snake_case							:  Dict			=     64
						snake_case							:  int			=     Tokenizer(A_							)
						snake_case							:  Dict			=     align.Align('''efficientnet-b7'''     ,				'''bert-base'''     ,				640     ,				A_     ,				tok.get_vocab_size()							)
						original_model.compile()
						original_model.load_weights(A_							)

						snake_case							:  List[Any]			=     original_model.trainable_variables
						snake_case							:  Optional[Any]			=     original_model.non_trainable_variables
						snake_case							:  List[Any]			=     {param.name: param.numpy() for param in tf_params}
						for param in tf_non_train_params:
												snake_case							:  Any			=     param.numpy()
						snake_case							:  List[str]			=     list(tf_params.keys()							)

						# Load HuggingFace model
						snake_case							:  int			=     get_align_config()
						snake_case							:  Any			=     AlignModel(A_							).eval()
						snake_case							:  Any			=     hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print('''Converting parameters...'''							)
						snake_case							:  Tuple			=     rename_keys(A_							)
						replace_params(A_     ,				A_     ,				A_							)

						# Initialize processor
						snake_case							:  int			=     get_processor()
						snake_case							:  Optional[Any]			=     processor(
						    images=prepare_img()     ,				text='''A picture of a cat'''     ,				padding='''max_length'''     ,				max_length=64     ,				return_tensors='''pt'''							)

						# HF model inference
						hf_model.eval()
						with torch.no_grad():
												snake_case							:  Optional[int]			=     hf_model(**A_							)

						snake_case							:  List[str]			=     outputs.image_embeds.detach().numpy()
						snake_case							:  int			=     outputs.text_embeds.detach().numpy()

						# Original model inference
						snake_case							:  Dict			=     False
						snake_case							:  int			=     EfficientNetImageProcessor(
						    do_center_crop=A_     ,				do_rescale=A_     ,				do_normalize=A_     ,				include_top=A_     ,				resample=Image.BILINEAR     ,				)
						snake_case							:  int			=     tf_image_processor(images=prepare_img()     ,				return_tensors='''tf'''     ,				data_format='''channels_last'''							)['''pixel_values''']
						snake_case							:  Union[str, Any]			=     tok(tf.constant(['''A picture of a cat''']							)							)

						snake_case							:  int			=     original_model.image_encoder(A_     ,				training=A_							)
						snake_case							:  Any			=     original_model.text_encoder(A_     ,				training=A_							)

						snake_case							:  Any			=     tf.nn.la_normalize(A_     ,				axis=-1							)
						snake_case							:  List[str]			=     tf.nn.la_normalize(A_     ,				axis=-1							)

						# Check whether original and HF model outputs match  -> np.allclose
						if not np.allclose(A_     ,				A_     ,				atol=1e-3							):
												raise ValueError('''The predicted image features are not the same.'''							)
						if not np.allclose(A_     ,				A_     ,				atol=1e-3							):
												raise ValueError('''The predicted text features are not the same.'''							)
						print('''Model outputs match!'''							)

						if save_model:
												# Create folder to save model
												if not os.path.isdir(A_							):
																		os.mkdir(A_							)
												# Save converted model and image processor
												hf_model.save_pretrained(A_							)
												processor.save_pretrained(A_							)

						if push_to_hub:
												# Push model and image processor to hub
												print('''Pushing converted ALIGN to the hub...'''							)
												processor.push_to_hub('''align-base'''							)
												hf_model.push_to_hub('''align-base'''							)


if __name__ == "__main__":
						UpperCAmelCase            = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_path",
						    default="./weights/model-weights",
						    type=str,
						    help="Path to the pretrained TF ALIGN checkpoint.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="hf_model",
						    type=str,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument("--save_model", action="store_true", help="Save model to local")
						parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

						UpperCAmelCase            = parser.parse_args()
						convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)



'''simple docstring'''





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     a :




				def __init__(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Any=13,     SCREAMING_SNAKE_CASE_						:					Any=7,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=True,     SCREAMING_SNAKE_CASE_						:					List[str]=True,     SCREAMING_SNAKE_CASE_						:					Optional[int]=True,     SCREAMING_SNAKE_CASE_						:					List[Any]=True,     SCREAMING_SNAKE_CASE_						:					str=99,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=32,     SCREAMING_SNAKE_CASE_						:					Optional[int]=5,     SCREAMING_SNAKE_CASE_						:					str=4,     SCREAMING_SNAKE_CASE_						:					Dict=37,     SCREAMING_SNAKE_CASE_						:					Optional[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					List[str]=0.1,     SCREAMING_SNAKE_CASE_						:					Tuple=0.1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					Dict=16,     SCREAMING_SNAKE_CASE_						:					Any=2,     SCREAMING_SNAKE_CASE_						:					Tuple=0.02,     SCREAMING_SNAKE_CASE_						:					int=3,     SCREAMING_SNAKE_CASE_						:					Dict=None,     SCREAMING_SNAKE_CASE_						:					List[str]=20,     SCREAMING_SNAKE_CASE_						:					List[Any]=30,     SCREAMING_SNAKE_CASE_						:					Optional[int]=2,     SCREAMING_SNAKE_CASE_						:					Any=2,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=10,     ):
										snake_case							:  Union[str, Any]			=     parent
										snake_case							:  List[str]			=     batch_size
										snake_case							:  List[str]			=     seq_length
										snake_case							:  Optional[int]			=     is_training
										snake_case							:  Optional[Any]			=     use_input_mask
										snake_case							:  List[Any]			=     use_token_type_ids
										snake_case							:  int			=     use_labels
										snake_case							:  List[Any]			=     vocab_size
										snake_case							:  Tuple			=     hidden_size
										snake_case							:  List[str]			=     num_hidden_layers
										snake_case							:  List[Any]			=     num_attention_heads
										snake_case							:  int			=     intermediate_size
										snake_case							:  List[str]			=     hidden_act
										snake_case							:  int			=     hidden_dropout_prob
										snake_case							:  Dict			=     attention_probs_dropout_prob
										snake_case							:  Tuple			=     max_position_embeddings
										snake_case							:  List[str]			=     type_vocab_size
										snake_case							:  str			=     type_sequence_label_size
										snake_case							:  Optional[Any]			=     initializer_range
										snake_case							:  List[str]			=     num_labels
										snake_case							:  Union[str, Any]			=     scope
										snake_case							:  int			=     max_xpath_tag_unit_embeddings
										snake_case							:  Optional[Any]			=     max_xpath_subs_unit_embeddings
										snake_case							:  str			=     tag_pad_id
										snake_case							:  Dict			=     subs_pad_id
										snake_case							:  List[Any]			=     max_depth




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  Tuple			=     ids_tensor([self.batch_size, self.seq_length],     self.vocab_size   )

										snake_case							:  Tuple			=     ids_tensor(
										    [self.batch_size, self.seq_length, self.max_depth],     self.max_xpath_tag_unit_embeddings   )

										snake_case							:  Tuple			=     ids_tensor(
										    [self.batch_size, self.seq_length, self.max_depth],     self.max_xpath_subs_unit_embeddings   )

										snake_case							:  Dict			=     None
										if self.use_input_mask:
																snake_case							:  List[str]			=     ids_tensor([self.batch_size, self.seq_length],     vocab_size=2   )

										snake_case							:  Any			=     None
										if self.use_token_type_ids:
																snake_case							:  Optional[int]			=     ids_tensor([self.batch_size, self.seq_length],     self.type_vocab_size   )

										snake_case							:  List[Any]			=     None
										snake_case							:  str			=     None
										if self.use_labels:
																snake_case							:  List[str]			=     ids_tensor([self.batch_size],     self.type_sequence_label_size   )
																snake_case							:  Union[str, Any]			=     ids_tensor([self.batch_size, self.seq_length],     self.num_labels   )

										snake_case							:  Union[str, Any]			=     self.get_config()

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




				def 						__snake_case				(				self						:					Tuple   ):
										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 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  Optional[Any]			=     MarkupLMModel(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										print('''Configs:''',     model.config.tag_pad_id,     model.config.subs_pad_id   )
										snake_case							:  Optional[Any]			=     model(SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     model(SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Dict			=     model(SCREAMING_SNAKE_CASE_   )
										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 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     ):
										snake_case							:  int			=     self.num_labels
										snake_case							:  Union[str, Any]			=     MarkupLMForSequenceClassification(SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  str			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_,     labels=SCREAMING_SNAKE_CASE_,     )
										self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.num_labels)   )




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  Any			=     self.num_labels
										snake_case							:  List[str]			=     MarkupLMForTokenClassification(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  Dict			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_,     labels=SCREAMING_SNAKE_CASE_,     )
										self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.seq_length, self.num_labels)   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int,     ):
										snake_case							:  List[Any]			=     MarkupLMForQuestionAnswering(config=SCREAMING_SNAKE_CASE_   )
										model.to(SCREAMING_SNAKE_CASE_   )
										model.eval()
										snake_case							:  Union[str, Any]			=     model(
										    SCREAMING_SNAKE_CASE_,     xpath_tags_seq=SCREAMING_SNAKE_CASE_,     xpath_subs_seq=SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_,     start_positions=SCREAMING_SNAKE_CASE_,     end_positions=SCREAMING_SNAKE_CASE_,     )
										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 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  int			=     self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       (
										    snake_case
										),       
										)							:  Any			=     config_and_inputs
										snake_case							:  str			=     {
										    '''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     a (					__magic_name__					,__magic_name__					,unittest.TestCase     ):
				_snake_case		     =			(
				    (
				        MarkupLMModel,
				        MarkupLMForSequenceClassification,
				        MarkupLMForTokenClassification,
				        MarkupLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				_snake_case		     =			(
				    {
				        '''feature-extraction''': MarkupLMModel,
				        '''question-answering''': MarkupLMForQuestionAnswering,
				        '''text-classification''': MarkupLMForSequenceClassification,
				        '''token-classification''': MarkupLMForTokenClassification,
				        '''zero-shot''': MarkupLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					List[str],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					List[Any]   ):
										# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
										# (batch of pretokenized examples).
										return True




				def 						__snake_case				(				self						:					str   ):
										snake_case							:  Tuple			=     MarkupLMModelTester(self   )
										snake_case							:  List[Any]			=     ConfigTester(self,     config_class=SCREAMING_SNAKE_CASE_,     hidden_size=37   )




				def 						__snake_case				(				self						:					Any   ):
										self.config_tester.run_common_tests()




				def 						__snake_case				(				self						:					Optional[int]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  List[Any]			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										snake_case							:  Dict			=     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_   )




def   A			(							):
						snake_case							:  Optional[int]			=     '''
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    '''

						return html_string





@require_torch
class     a (					unittest.TestCase     ):




				@cached_property
				def 						__snake_case				(				self						:					Tuple   ):
										# TODO use from_pretrained here
										snake_case							:  Optional[int]			=     MarkupLMFeatureExtractor()
										snake_case							:  Tuple			=     MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''   )

										return MarkupLMProcessor(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )




				@slow
				def 						__snake_case				(				self						:					Dict   ):
										snake_case							:  Dict			=     MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''   ).to(SCREAMING_SNAKE_CASE_   )

										snake_case							:  List[str]			=     self.default_processor

										snake_case							:  str			=     processor(prepare_html_string(),     return_tensors='''pt'''   )
										snake_case							:  List[Any]			=     inputs.to(SCREAMING_SNAKE_CASE_   )

										# forward pass
										with torch.no_grad():
																snake_case							:  Any			=     model(**SCREAMING_SNAKE_CASE_   )

										# verify the last hidden states
										snake_case							:  Tuple			=     torch.Size([1, 14, 7_68]   )
										self.assertEqual(outputs.last_hidden_state.shape,     SCREAMING_SNAKE_CASE_   )

										snake_case							:  Union[str, Any]			=     torch.tensor(
										    [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]   ).to(SCREAMING_SNAKE_CASE_   )

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

