



'''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       lowerCamelCase_							:






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase=13     ,						__UpperCamelCase=7     ,						__UpperCamelCase=True     ,						__UpperCamelCase=True     ,						__UpperCamelCase=True     ,						__UpperCamelCase=True     ,						__UpperCamelCase=99     ,						__UpperCamelCase=32     ,						__UpperCamelCase=5     ,						__UpperCamelCase=4     ,						__UpperCamelCase=37     ,						__UpperCamelCase="gelu"     ,						__UpperCamelCase=0.1     ,						__UpperCamelCase=0.1     ,						__UpperCamelCase=5_12     ,						__UpperCamelCase=16     ,						__UpperCamelCase=2     ,						__UpperCamelCase=0.02     ,						__UpperCamelCase=3     ,						__UpperCamelCase=None     ,						__UpperCamelCase=20     ,						__UpperCamelCase=30     ,						__UpperCamelCase=2     ,						__UpperCamelCase=2     ,						__UpperCamelCase=10     ,						):
       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 ):
       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 ):
       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     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						):
       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 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						):
       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 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						):
       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 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						):
       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 	_UpperCamelCase       (  self ):
       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       lowerCamelCase_							(	lowerCAmelCase_					,    lowerCAmelCase_					,    unittest.TestCase  ):






   '''simple docstring'''

   A :     Any				=	(
       (
           MarkupLMModel,
           MarkupLMForSequenceClassification,
           MarkupLMForTokenClassification,
           MarkupLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   A :     Optional[int]				=	(
       {
           "feature-extraction": MarkupLMModel,
           "question-answering": MarkupLMForQuestionAnswering,
           "text-classification": MarkupLMForSequenceClassification,
           "token-classification": MarkupLMForTokenClassification,
           "zero-shot": MarkupLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
       # (batch of pretokenized examples).
       return True


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			MarkupLMModelTester(self )
       UpperCamelCase__      =			ConfigTester(self     ,						config_class=__UpperCamelCase     ,						hidden_size=37 )


   def 	_UpperCamelCase       (  self ):
       self.config_tester.run_common_tests()


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )







   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )





def 						snake_case__ ():
    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       lowerCamelCase_							(	unittest.TestCase  ):






   '''simple docstring'''

   @cached_property
   def 	_UpperCamelCase       (  self ):
       # TODO use from_pretrained here
       UpperCamelCase__      =			MarkupLMFeatureExtractor()
       UpperCamelCase__      =			MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base" )

       return MarkupLMProcessor(__UpperCamelCase     ,						__UpperCamelCase )







   @slow
   def 	_UpperCamelCase       (  self ):
       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, 14, 7_68] )
       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 ) )









'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			TaConfig.from_pretrained(lowerCAmelCase_	)
    UpperCamelCase__      =			FlaxTaForConditionalGeneration(config=lowerCAmelCase_	)
    UpperCamelCase__      =			checkpoints.load_tax_checkpoint(lowerCAmelCase_	)

    UpperCamelCase__      =			"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

    # Encoder
    for layer_index in range(config.num_layers	):
        UpperCamelCase__      =			F'layers_{str(lowerCAmelCase_	)}'

        # Self-Attention
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

        if split_mlp_wi:
            UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        UpperCamelCase__      =			tax_attention_key
        UpperCamelCase__      =			tax_attention_out
        UpperCamelCase__      =			tax_attention_query
        UpperCamelCase__      =			tax_attention_value

        UpperCamelCase__      =			tax_attention_layer_norm

        if split_mlp_wi:
            UpperCamelCase__      =			tax_mlp_wi_a
            UpperCamelCase__      =			tax_mlp_wi_a
        else:
            UpperCamelCase__      =			tax_mlp_wi

        UpperCamelCase__      =			tax_mlp_wo
        UpperCamelCase__      =			tax_mlp_layer_norm

    # Only for layer 0:
    UpperCamelCase__      =			tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
    UpperCamelCase__      =			tax_encoder_rel_embedding

    # Assigning
    UpperCamelCase__      =			tax_model["target"]["encoder"]["encoder_norm"]["scale"]
    UpperCamelCase__      =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers	):
        UpperCamelCase__      =			F'layers_{str(lowerCAmelCase_	)}'

        # Self-Attention
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
            "scale"
        ]

        # Encoder-Decoder-Attention
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
            "kernel"
        ]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
            "kernel"
        ]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
            "kernel"
        ]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
            "kernel"
        ]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

        # MLP
        if split_mlp_wi:
            UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        UpperCamelCase__      =			tax_attention_key
        UpperCamelCase__      =			tax_attention_out
        UpperCamelCase__      =			tax_attention_query
        UpperCamelCase__      =			tax_attention_value

        UpperCamelCase__      =			tax_pre_attention_layer_norm

        UpperCamelCase__      =			tax_enc_dec_attention_key
        UpperCamelCase__      =			tax_enc_dec_attention_out
        UpperCamelCase__      =			tax_enc_dec_attention_query
        UpperCamelCase__      =			tax_enc_dec_attention_value

        UpperCamelCase__      =			tax_cross_layer_norm

        if split_mlp_wi:
            UpperCamelCase__      =			tax_mlp_wi_a
            UpperCamelCase__      =			tax_mlp_wi_a
        else:
            UpperCamelCase__      =			tax_mlp_wi

        UpperCamelCase__      =			tax_mlp_wo

        UpperCamelCase__      =			txa_mlp_layer_norm

    # Decoder Normalization
    UpperCamelCase__      =			tax_model["target"]["decoder"]["decoder_norm"]["scale"]
    UpperCamelCase__      =			txa_decoder_norm

    # Only for layer 0:
    UpperCamelCase__      =			tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
    UpperCamelCase__      =			tax_decoder_rel_embedding

    # Token Embeddings
    UpperCamelCase__      =			tax_model["target"]["token_embedder"]["embedding"]
    UpperCamelCase__      =			txa_token_embeddings

    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        UpperCamelCase__      =			tax_model["target"]["decoder"]["logits_dense"]["kernel"]

    flax_model.save_pretrained(lowerCAmelCase_	)
    print("T5X Model was sucessfully converted!"	)


if __name__ == "__main__":
      lowercase__     :  Any    					=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
      )
      parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
      parser.add_argument(
          '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
      )
      lowercase__     :  int    					=			parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




