






"""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 					_UpperCamelCase		:






    def __init__(self						,      lowerCamelCase__						,      lowerCamelCase__=1_3						,      lowerCamelCase__=7						,      lowerCamelCase__=True						,      lowerCamelCase__=True						,      lowerCamelCase__=True						,      lowerCamelCase__=True						,      lowerCamelCase__=9_9						,      lowerCamelCase__=3_2						,      lowerCamelCase__=5						,      lowerCamelCase__=4						,      lowerCamelCase__=3_7						,      lowerCamelCase__="gelu"						,      lowerCamelCase__=0.1						,      lowerCamelCase__=0.1						,      lowerCamelCase__=5_1_2						,      lowerCamelCase__=1_6						,      lowerCamelCase__=2						,      lowerCamelCase__=0.0_2						,      lowerCamelCase__=3						,      lowerCamelCase__=None						,      lowerCamelCase__=2_0						,      lowerCamelCase__=3_0						,      lowerCamelCase__=2						,      lowerCamelCase__=2						,      lowerCamelCase__=1_0						,      ):


      """simple docstring"""
      A__											=							parent
      A__											=							batch_size
      A__											=							seq_length
      A__											=							is_training
      A__											=							use_input_mask
      A__											=							use_token_type_ids
      A__											=							use_labels
      A__											=							vocab_size
      A__											=							hidden_size
      A__											=							num_hidden_layers
      A__											=							num_attention_heads
      A__											=							intermediate_size
      A__											=							hidden_act
      A__											=							hidden_dropout_prob
      A__											=							attention_probs_dropout_prob
      A__											=							max_position_embeddings
      A__											=							type_vocab_size
      A__											=							type_sequence_label_size
      A__											=							initializer_range
      A__											=							num_labels
      A__											=							scope
      A__											=							max_xpath_tag_unit_embeddings
      A__											=							max_xpath_subs_unit_embeddings
      A__											=							tag_pad_id
      A__											=							subs_pad_id
      A__											=							max_depth






    def  A    (self   ):


      """simple docstring"""
      A__											=							ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size   )

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

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

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

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

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

      A__											=							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   ):


      """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  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):


      """simple docstring"""
      A__											=							MarkupLMModel(config=lowerCamelCase__   )
      model.to(lowerCamelCase__   )
      model.eval()
      print("""Configs:"""						,      model.config.tag_pad_id						,      model.config.subs_pad_id   )
      A__											=							model(lowerCamelCase__						,      attention_mask=lowerCamelCase__						,      token_type_ids=lowerCamelCase__   )
      A__											=							model(lowerCamelCase__						,      token_type_ids=lowerCamelCase__   )
      A__											=							model(lowerCamelCase__   )
      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						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):


      """simple docstring"""
      A__											=							self.num_labels
      A__											=							MarkupLMForSequenceClassification(lowerCamelCase__   )
      model.to(lowerCamelCase__   )
      model.eval()
      A__											=							model(
          lowerCamelCase__						,      xpath_tags_seq=lowerCamelCase__						,      xpath_subs_seq=lowerCamelCase__						,      attention_mask=lowerCamelCase__						,      token_type_ids=lowerCamelCase__						,      labels=lowerCamelCase__						,      )
      self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):


      """simple docstring"""
      A__											=							self.num_labels
      A__											=							MarkupLMForTokenClassification(config=lowerCamelCase__   )
      model.to(lowerCamelCase__   )
      model.eval()
      A__											=							model(
          lowerCamelCase__						,      xpath_tags_seq=lowerCamelCase__						,      xpath_subs_seq=lowerCamelCase__						,      attention_mask=lowerCamelCase__						,      token_type_ids=lowerCamelCase__						,      labels=lowerCamelCase__						,      )
      self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.num_labels)   )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):


      """simple docstring"""
      A__											=							MarkupLMForQuestionAnswering(config=lowerCamelCase__   )
      model.to(lowerCamelCase__   )
      model.eval()
      A__											=							model(
          lowerCamelCase__						,      xpath_tags_seq=lowerCamelCase__						,      xpath_subs_seq=lowerCamelCase__						,      attention_mask=lowerCamelCase__						,      token_type_ids=lowerCamelCase__						,      start_positions=lowerCamelCase__						,      end_positions=lowerCamelCase__						,      )
      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   ):


      """simple docstring"""
      A__											=							self.prepare_config_and_inputs()
      (
          (
          A__
      )						,(
          A__
      )						,(
          A__
      )						,(
          A__
      )						,(
          A__
      )						,(
          A__
      )						,(
          A__
      )						,(
          A__
      )						,
      )											=							config_and_inputs
      A__											=							{
          """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		( __snake_case						, __snake_case						, unittest.TestCase):
    __lowerCamelCase         =  (
        (
            MarkupLMModel,
            MarkupLMForSequenceClassification,
            MarkupLMForTokenClassification,
            MarkupLMForQuestionAnswering,
        )
        if is_torch_available()
        else None
    )
    __lowerCamelCase         =  (
        {
            "feature-extraction": MarkupLMModel,
            "question-answering": MarkupLMForQuestionAnswering,
            "text-classification": MarkupLMForSequenceClassification,
            "token-classification": MarkupLMForTokenClassification,
            "zero-shot": MarkupLMForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )






    def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   ):


      """simple docstring"""
      # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
      # (batch of pretokenized examples).
      return True






    def  A    (self   ):


      """simple docstring"""
      A__											=							MarkupLMModelTester(self   )
      A__											=							ConfigTester(self						,      config_class=lowerCamelCase__						,      hidden_size=3_7   )






    def  A    (self   ):


      """simple docstring"""
      self.config_tester.run_common_tests()






    def  A    (self   ):


      """simple docstring"""
      A__											=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__   )






def 						_SCREAMING_SNAKE_CASE							( ):
  A__											=							"""
    <!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 					_UpperCamelCase		( unittest.TestCase):






    @cached_property
    def  A    (self   ):


      """simple docstring"""
      # TODO use from_pretrained here
      A__											=							MarkupLMFeatureExtractor()
      A__											=							MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""   )

      return MarkupLMProcessor(lowerCamelCase__						,      lowerCamelCase__   )






    @slow
    def  A    (self   ):


      """simple docstring"""
      A__											=							MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""   ).to(lowerCamelCase__   )

      A__											=							self.default_processor

      A__											=							processor(prepare_html_string()						,      return_tensors="""pt"""   )
      A__											=							inputs.to(lowerCamelCase__   )

      # forward pass
      with torch.no_grad():
        A__											=							model(**lowerCamelCase__   )

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

      A__											=							torch.tensor(
          [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]]   ).to(lowerCamelCase__   )

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












"""simple docstring"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Any		,				UpperCamelCase     :	Optional[Any]		,				UpperCamelCase     :	Dict    ):
  # Initialise PyTorch model
  A__											=							FNetConfig.from_json_file(UpperCamelCase    )
  print(F"""Building PyTorch model from configuration: {config}"""    )
  A__											=							FNetForPreTraining(UpperCamelCase    )

  A__											=							restore_checkpoint(UpperCamelCase		,				UpperCamelCase    )
  A__											=							checkpoint_dict["""target"""]

  # Embeddings
  # Position IDs
  A__											=							fnet_pretraining_model.state_dict()

  A__											=							state_dict["""fnet.embeddings.position_ids"""]
  A__											=							{"""fnet.embeddings.position_ids""": position_ids}
  # Embedding Layers
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]    ).T
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]    )

  # Encoder Layers
  for layer in range(config.num_hidden_layers    ):
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""mixing_layer_norm"""]["""scale"""]    )
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""mixing_layer_norm"""]["""bias"""]    )

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""intermediate"""]["""kernel"""]    ).T
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""intermediate"""]["""bias"""]    )

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""output"""]["""kernel"""]    ).T
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""feed_forward_{layer}"""]["""output"""]["""bias"""]    )

    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""output_layer_norm"""]["""scale"""]    )
    A__											=							torch.tensor(
        pretrained_model_params["""encoder"""][F"""encoder_{layer}"""]["""output_layer_norm"""]["""bias"""]    )

  # Pooler Layers
  A__											=							torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]    ).T
  A__											=							torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]    )

  # Masked LM Layers
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_dense"""]["""kernel"""]    ).T
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_dense"""]["""bias"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_layer_norm"""]["""scale"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_layer_norm"""]["""bias"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""predictions_output"""]["""output_bias"""]    )
  A__											=							torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]    )

  # Seq Relationship Layers
  A__											=							torch.tensor(
      pretrained_model_params["""classification"""]["""output_kernel"""]    )
  A__											=							torch.tensor(
      pretrained_model_params["""classification"""]["""output_bias"""]    )

  # Load State Dict
  fnet_pretraining_model.load_state_dict(UpperCamelCase    )

  # Save PreTrained
  print(F"""Saving pretrained model to {save_path}"""    )
  fnet_pretraining_model.save_pretrained(UpperCamelCase    )


if __name__ == "__main__":
      lowerCamelCase__			=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--flax_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--fnet_config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained FNet model. \n"
              "This specifies the model architecture."
          ),
      )
      parser.add_argument("--save_path", default=None, type=str, required=True, help="Path to the output model.")
      lowerCamelCase__			=   parser.parse_args()
      convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




