
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 		lowercase_							:

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase=1_3  ,						__UpperCamelCase=7  ,						__UpperCamelCase=True  ,						__UpperCamelCase=True  ,						__UpperCamelCase=True  ,						__UpperCamelCase=True  ,						__UpperCamelCase=9_9  ,						__UpperCamelCase=3_2  ,						__UpperCamelCase=5  ,						__UpperCamelCase=4  ,						__UpperCamelCase=3_7  ,						__UpperCamelCase="gelu"  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=5_1_2  ,						__UpperCamelCase=1_6  ,						__UpperCamelCase=2  ,						__UpperCamelCase=0.02  ,						__UpperCamelCase=3  ,						__UpperCamelCase=None  ,						__UpperCamelCase=2_0  ,						__UpperCamelCase=3_0  ,						__UpperCamelCase=2  ,						__UpperCamelCase=2  ,						__UpperCamelCase=1_0  ,						):




        """simple docstring"""


        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 lowerCamelCase_					(   self  ):




        """simple docstring"""


        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 lowerCamelCase_					(   self  ):




        """simple docstring"""


        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 lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						):




        """simple docstring"""


        UpperCamelCase_											=							MarkupLMModel(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        print("""Configs:"""  ,						model.config.tag_pad_id  ,						model.config.subs_pad_id  )
        UpperCamelCase_											=							model(__UpperCamelCase  ,						attention_mask=__UpperCamelCase  ,						token_type_ids=__UpperCamelCase  )
        UpperCamelCase_											=							model(__UpperCamelCase  ,						token_type_ids=__UpperCamelCase  )
        UpperCamelCase_											=							model(__UpperCamelCase  )
        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 lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						):




        """simple docstring"""


        UpperCamelCase_											=							self.num_labels
        UpperCamelCase_											=							MarkupLMForSequenceClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        UpperCamelCase_											=							model(
            __UpperCamelCase  ,						xpath_tags_seq=__UpperCamelCase  ,						xpath_subs_seq=__UpperCamelCase  ,						attention_mask=__UpperCamelCase  ,						token_type_ids=__UpperCamelCase  ,						labels=__UpperCamelCase  ,						)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						):




        """simple docstring"""


        UpperCamelCase_											=							self.num_labels
        UpperCamelCase_											=							MarkupLMForTokenClassification(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        UpperCamelCase_											=							model(
            __UpperCamelCase  ,						xpath_tags_seq=__UpperCamelCase  ,						xpath_subs_seq=__UpperCamelCase  ,						attention_mask=__UpperCamelCase  ,						token_type_ids=__UpperCamelCase  ,						labels=__UpperCamelCase  ,						)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.num_labels)  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						):




        """simple docstring"""


        UpperCamelCase_											=							MarkupLMForQuestionAnswering(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        UpperCamelCase_											=							model(
            __UpperCamelCase  ,						xpath_tags_seq=__UpperCamelCase  ,						xpath_subs_seq=__UpperCamelCase  ,						attention_mask=__UpperCamelCase  ,						token_type_ids=__UpperCamelCase  ,						start_positions=__UpperCamelCase  ,						end_positions=__UpperCamelCase  ,						)
        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 lowerCamelCase_					(   self  ):




        """simple docstring"""


        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 		lowercase_							(							__SCREAMING_SNAKE_CASE				,						__SCREAMING_SNAKE_CASE				,						unittest.TestCase					):
 A__    :					Tuple    =     (
     (
         MarkupLMModel,
         MarkupLMForSequenceClassification,
         MarkupLMForTokenClassification,
         MarkupLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 A__    :					Union[str, Any]    =     (
     {
         """feature-extraction""": MarkupLMModel,
         """question-answering""": MarkupLMForQuestionAnswering,
         """text-classification""": MarkupLMForSequenceClassification,
         """token-classification""": MarkupLMForTokenClassification,
         """zero-shot""": MarkupLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        return True

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							MarkupLMModelTester(self  )
        UpperCamelCase_											=							ConfigTester(self  ,						config_class=__UpperCamelCase  ,						hidden_size=3_7  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.config_tester.run_common_tests()

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase  )




def 					lowerCamelCase__					(    )		->						Union[str, Any]:
       UpperCamelCase_											=							"""
    <!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 		lowercase_							(							unittest.TestCase					):

 @cached_property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							MarkupLMFeatureExtractor()
        UpperCamelCase_											=							MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""  )

        return MarkupLMProcessor(__UpperCamelCase  ,						__UpperCamelCase  )

 @slow
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""  ).to(__UpperCamelCase  )

        UpperCamelCase_											=							self.default_processor

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

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

        # verify the last hidden states
        UpperCamelCase_											=							torch.Size([1, 1_4, 7_6_8]  )
        self.assertEqual(outputs.last_hidden_state.shape  ,						__UpperCamelCase  )

        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(__UpperCamelCase  )

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





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   start_method="""spawn"""  )


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
      # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

      # on-demand GPU
      # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
      _A							=       rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
      gpu.up_if_not()

      # on-prem GPU
      # gpu = rh.cluster(
      #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
      #       )

      # Set up remote function
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      launch_train_gpu(config, train_args, stream_logs=True)

      # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
      # gpu.install_packages(reqs)
      # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



