


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        UpperCAmelCase__			:



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=13   ,  A_=7   ,  A_=True   ,  A_=True   ,  A_=True   ,  A_=True   ,  A_=99   ,  A_=32   ,  A_=5   ,  A_=4   ,  A_=37   ,  A_="gelu"   ,  A_=0.1   ,  A_=0.1   ,  A_=512   ,  A_=16   ,  A_=2   ,  A_=0.02   ,  A_=3   ,  A_=None   ,  A_=20   ,  A_=30   ,  A_=2   ,  A_=2   ,  A_=10   ,  )    ->			Union[str, 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 _a   (     self       )    ->			int:
         __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 _a   (     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 _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			Optional[int]:
         __UpperCamelCase       				=MarkupLMModel(config=A_       )
         model.to(A_       )
         model.eval()
         print('Configs:'   ,  model.config.tag_pad_id   ,  model.config.subs_pad_id       )
         __UpperCamelCase       				=model(A_   ,  attention_mask=A_   ,  token_type_ids=A_       )
         __UpperCamelCase       				=model(A_   ,  token_type_ids=A_       )
         __UpperCamelCase       				=model(A_       )
         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 _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			List[str]:
         __UpperCamelCase       				=self.num_labels
         __UpperCamelCase       				=MarkupLMForSequenceClassification(A_       )
         model.to(A_       )
         model.eval()
         __UpperCamelCase       				=model(
             A_   ,  xpath_tags_seq=A_   ,  xpath_subs_seq=A_   ,  attention_mask=A_   ,  token_type_ids=A_   ,  labels=A_   ,  )
         self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.num_labels)       )






    def _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			Tuple:
         __UpperCamelCase       				=self.num_labels
         __UpperCamelCase       				=MarkupLMForTokenClassification(config=A_       )
         model.to(A_       )
         model.eval()
         __UpperCamelCase       				=model(
             A_   ,  xpath_tags_seq=A_   ,  xpath_subs_seq=A_   ,  attention_mask=A_   ,  token_type_ids=A_   ,  labels=A_   ,  )
         self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length, self.num_labels)       )






    def _a   (     self   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  A_   ,  )    ->			Optional[Any]:
         __UpperCamelCase       				=MarkupLMForQuestionAnswering(config=A_       )
         model.to(A_       )
         model.eval()
         __UpperCamelCase       				=model(
             A_   ,  xpath_tags_seq=A_   ,  xpath_subs_seq=A_   ,  attention_mask=A_   ,  token_type_ids=A_   ,  start_positions=A_   ,  end_positions=A_   ,  )
         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 _a   (     self       )    ->			List[str]:
         __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        UpperCAmelCase__			(   A_  ,    A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						Tuple    =    (
        (
            MarkupLMModel,
            MarkupLMForSequenceClassification,
            MarkupLMForTokenClassification,
            MarkupLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    UpperCAmelCase__		:						int    =    (
        {
            "feature-extraction": MarkupLMModel,
            "question-answering": MarkupLMForQuestionAnswering,
            "text-classification": MarkupLMForSequenceClassification,
            "token-classification": MarkupLMForTokenClassification,
            "zero-shot": MarkupLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )






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






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=MarkupLMModelTester(self       )
         __UpperCamelCase       				=ConfigTester(self   ,  config_class=A_   ,  hidden_size=37       )






    def _a   (     self       )    ->			List[Any]:
         self.config_tester.run_common_tests()






    def _a   (     self       )    ->			Union[str, Any]:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_       )






    def _a   (     self       )    ->			Any:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A_       )






    def _a   (     self       )    ->			Tuple:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A_       )






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A_       )

def 		_UpperCAmelCase							(     ):
     __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        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    @cached_property
    def _a   (     self       )    ->			Union[str, Any]:
         # TODO use from_pretrained here
         __UpperCamelCase       				=MarkupLMFeatureExtractor()
         __UpperCamelCase       				=MarkupLMTokenizer.from_pretrained('microsoft/markuplm-base'       )

         return MarkupLMProcessor(A_   ,  A_       )






    @slow
    def _a   (     self       )    ->			Optional[Any]:
         __UpperCamelCase       				=MarkupLMModel.from_pretrained('microsoft/markuplm-base'       ).to(A_       )

         __UpperCamelCase       				=self.default_processor

         __UpperCamelCase       				=processor(prepare_html_string()   ,  return_tensors='pt'       )
         __UpperCamelCase       				=inputs.to(A_       )

         # forward pass
         with torch.no_grad():
              __UpperCamelCase       				=model(**A_       )

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

         __UpperCamelCase       				=torch.tensor(
             [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]       ).to(A_       )

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









import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=GPTNeoConfig(
         hidden_size=config_json['n_embd']     ,	num_layers=config_json['n_layer']     ,	num_heads=config_json['n_head']     ,	attention_types=config_json['attention_types']     ,	max_position_embeddings=config_json['n_positions']     ,	resid_dropout=config_json['res_dropout']     ,	embed_dropout=config_json['embed_dropout']     ,	attention_dropout=config_json['attn_dropout']     ,	)
     print(F'Building PyTorch model from configuration: {config}'							)
     __UpperCamelCase       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   _A									=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained mesh-tf model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





