






"""simple docstring"""



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 _lowerCAmelCase  :  List[str]   =			"""platform"""

 import jax
 import jax.numpy as jnp

 from transformers import AutoTokenizer
 from transformers.models.mbart.modeling_flax_mbart import (
     FlaxMBartForConditionalGeneration,
     FlaxMBartForQuestionAnswering,
     FlaxMBartForSequenceClassification,
     FlaxMBartModel,
     shift_tokens_right,
 )


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   Optional[Any] ,				snake_case					:   str ,				snake_case					:   Optional[Any]=None ,				snake_case					:   Any=None ,				snake_case					:   List[str]=None ,				snake_case					:   Optional[Any]=None ,				snake_case					:   Optional[Any]=None ,				snake_case					:   Union[str, Any]=None ,				)->		Tuple:






     '''simple docstring'''



     if attention_mask is None:
          UpperCAmelCase__	:						Optional[int]       =       np.where(input_ids != config.pad_token_id ,				1 ,				0							)
     if decoder_attention_mask is None:
          UpperCAmelCase__	:						Optional[int]       =       np.where(decoder_input_ids != config.pad_token_id ,				1 ,				0							)
     if head_mask is None:
          UpperCAmelCase__	:						int       =       np.ones((config.encoder_layers, config.encoder_attention_heads)							)
     if decoder_head_mask is None:
          UpperCAmelCase__	:						Optional[Any]       =       np.ones((config.decoder_layers, config.decoder_attention_heads)							)
     if cross_attn_head_mask is None:
          UpperCAmelCase__	:						Any       =       np.ones((config.decoder_layers, config.decoder_attention_heads)							)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
     }




class  lowerCAmelCase__ (						unittest.TestCase      ):
   def __init__(		self			:  str       , snake_case__			:  Union[str, Any]       , snake_case__			:  Any=1_3       , snake_case__			:  List[str]=7       , snake_case__			:  int=True       , snake_case__			:  Optional[int]=False       , snake_case__			:  Optional[Any]=9_9       , snake_case__			:  List[Any]=1_6       , snake_case__			:  Optional[Any]=2       , snake_case__			:  int=4       , snake_case__			:  Dict=4       , snake_case__			:  Union[str, Any]="gelu"       , snake_case__			:  str=0.1       , snake_case__			:  Any=0.1       , snake_case__			:  List[str]=3_2       , snake_case__			:  Dict=2       , snake_case__			:  Optional[int]=1       , snake_case__			:  Any=0       , snake_case__			:  Dict=2       , snake_case__			:  Union[str, Any]=0.02       , ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[Any]       =       parent
        UpperCAmelCase__	:						Tuple       =       batch_size
        UpperCAmelCase__	:						Union[str, Any]       =       seq_length
        UpperCAmelCase__	:						Tuple       =       is_training
        UpperCAmelCase__	:						int       =       use_labels
        UpperCAmelCase__	:						Optional[int]       =       vocab_size
        UpperCAmelCase__	:						Tuple       =       hidden_size
        UpperCAmelCase__	:						List[Any]       =       num_hidden_layers
        UpperCAmelCase__	:						Any       =       num_attention_heads
        UpperCAmelCase__	:						Dict       =       intermediate_size
        UpperCAmelCase__	:						int       =       hidden_act
        UpperCAmelCase__	:						Tuple       =       hidden_dropout_prob
        UpperCAmelCase__	:						List[Any]       =       attention_probs_dropout_prob
        UpperCAmelCase__	:						Optional[int]       =       max_position_embeddings
        UpperCAmelCase__	:						str       =       eos_token_id
        UpperCAmelCase__	:						Optional[int]       =       pad_token_id
        UpperCAmelCase__	:						str       =       bos_token_id
        UpperCAmelCase__	:						int       =       decoder_start_token_id
        UpperCAmelCase__	:						str       =       initializer_range
   def 			__a       (		self			:  Union[str, Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[Any]       =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       , self.vocab_size       )       , 3       , self.vocab_size       )
        UpperCAmelCase__	:						str       =       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       , dtype=np.intaa       ))       , -1       )

        UpperCAmelCase__	:						str       =       shift_tokens_right(snake_case__       , 1       )

        UpperCAmelCase__	:						Any       =       MBartConfig(
            vocab_size=self.vocab_size       , d_model=self.hidden_size       , encoder_layers=self.num_hidden_layers       , decoder_layers=self.num_hidden_layers       , encoder_attention_heads=self.num_attention_heads       , decoder_attention_heads=self.num_attention_heads       , encoder_ffn_dim=self.intermediate_size       , decoder_ffn_dim=self.intermediate_size       , dropout=self.hidden_dropout_prob       , attention_dropout=self.attention_probs_dropout_prob       , max_position_embeddings=self.max_position_embeddings       , eos_token_id=self.eos_token_id       , bos_token_id=self.bos_token_id       , pad_token_id=self.pad_token_id       , decoder_start_token_id=self.decoder_start_token_id       , initializer_range=self.initializer_range       , use_cache=snake_case__       , )
        UpperCAmelCase__	:						Tuple       =       prepare_mbart_inputs_dict(snake_case__       , snake_case__       , snake_case__       )
        return config, inputs_dict
   def 			__a       (		self			:  Union[str, Any]       ):


        '''simple docstring'''
        UpperCAmelCase__       ,							UpperCAmelCase__	:						str       =       self.prepare_config_and_inputs()
        return config, inputs_dict
   def 			__a       (		self			:  List[str]       , snake_case__			:  Dict       , snake_case__			:  Optional[Any]       , snake_case__			:  Dict       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       2_0
        UpperCAmelCase__	:						Any       =       model_class_name(snake_case__       )

        UpperCAmelCase__	:						Any       =       model.encode(inputs_dict["input_ids"]       )

        UpperCAmelCase__       ,							UpperCAmelCase__	:						Union[str, Any]       =       (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        UpperCAmelCase__	:						List[Any]       =       model.init_cache(decoder_input_ids.shape[0]       , snake_case__       , snake_case__       )
        UpperCAmelCase__	:						Union[str, Any]       =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       , dtype="i4"       )

        UpperCAmelCase__	:						str       =       jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]       , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       , )
        UpperCAmelCase__	:						Dict       =       model.decode(
            decoder_input_ids[:, :-1]       , snake_case__       , decoder_attention_mask=snake_case__       , past_key_values=snake_case__       , decoder_position_ids=snake_case__       , )

        UpperCAmelCase__	:						Union[str, Any]       =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       , dtype="i4"       )
        UpperCAmelCase__	:						int       =       model.decode(
            decoder_input_ids[:, -1:]       , snake_case__       , decoder_attention_mask=snake_case__       , past_key_values=outputs_cache.past_key_values       , decoder_position_ids=snake_case__       , )

        UpperCAmelCase__	:						List[Any]       =       model.decode(snake_case__       , snake_case__       )

        UpperCAmelCase__	:						Optional[Any]       =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
        self.parent.assertTrue(diff < 1e-3       , msg=f'Max diff is {diff}'       )




   def 			__a       (		self			:  Optional[Any]       , snake_case__			:  Union[str, Any]       , snake_case__			:  Optional[int]       , snake_case__			:  str       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[Any]       =       2_0
        UpperCAmelCase__	:						List[Any]       =       model_class_name(snake_case__       )

        UpperCAmelCase__	:						int       =       model.encode(inputs_dict["input_ids"]       )

        UpperCAmelCase__       ,							UpperCAmelCase__	:						Union[str, Any]       =       (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        UpperCAmelCase__	:						Any       =       jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
            ]       , axis=-1       , )

        UpperCAmelCase__	:						Tuple       =       model.init_cache(decoder_input_ids.shape[0]       , snake_case__       , snake_case__       )
        UpperCAmelCase__	:						List[Any]       =       jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]       , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       , )

        UpperCAmelCase__	:						str       =       model.decode(
            decoder_input_ids[:, :-1]       , snake_case__       , decoder_attention_mask=snake_case__       , past_key_values=snake_case__       , decoder_position_ids=snake_case__       , )
        UpperCAmelCase__	:						int       =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       , dtype="i4"       )
        UpperCAmelCase__	:						int       =       model.decode(
            decoder_input_ids[:, -1:]       , snake_case__       , past_key_values=outputs_cache.past_key_values       , decoder_attention_mask=snake_case__       , decoder_position_ids=snake_case__       , )

        UpperCAmelCase__	:						int       =       model.decode(snake_case__       , snake_case__       , decoder_attention_mask=snake_case__       )

        UpperCAmelCase__	:						int       =       np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
        self.parent.assertTrue(diff < 1e-3       , msg=f'Max diff is {diff}'       )




@require_flax
class  lowerCAmelCase__ (						unittest.TestCase      ):
   SCREAMING_SNAKE_CASE_         =99
   def 			__a       (		self			:  Union[str, Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       np.array(
            [
                [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
            ]       , dtype=np.intaa       , )

        UpperCAmelCase__	:						int       =       input_ids.shape[0]
        UpperCAmelCase__	:						List[Any]       =       MBartConfig(
            vocab_size=self.vocab_size       , d_model=2_4       , encoder_layers=2       , decoder_layers=2       , encoder_attention_heads=2       , decoder_attention_heads=2       , encoder_ffn_dim=3_2       , decoder_ffn_dim=3_2       , max_position_embeddings=4_8       , eos_token_id=2       , pad_token_id=1       , bos_token_id=0       , )
        return config, input_ids, batch_size
   def 			__a       (		self			:  Tuple       ):


        '''simple docstring'''
        UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__	:						List[Any]       =       self._get_config_and_data()
        UpperCAmelCase__	:						str       =       FlaxMBartForSequenceClassification(snake_case__       )
        UpperCAmelCase__	:						List[str]       =       model(input_ids=snake_case__       , decoder_input_ids=snake_case__       )
        UpperCAmelCase__	:						Optional[int]       =       (batch_size, config.num_labels)
        self.assertEqual(outputs["logits"].shape       , snake_case__       )
   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__	:						Tuple       =       self._get_config_and_data()
        UpperCAmelCase__	:						Dict       =       FlaxMBartForQuestionAnswering(snake_case__       )
        UpperCAmelCase__	:						Optional[int]       =       model(input_ids=snake_case__       )

        self.assertEqual(outputs["start_logits"].shape       , input_ids.shape       )
        self.assertEqual(outputs["end_logits"].shape       , input_ids.shape       )
   def 			__a       (		self			:  Dict       ):


        '''simple docstring'''
        UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__	:						Any       =       self._get_config_and_data()
        UpperCAmelCase__	:						int       =       FlaxMBartForConditionalGeneration(snake_case__       )
        UpperCAmelCase__	:						List[Any]       =       lm_model(input_ids=snake_case__       )
        UpperCAmelCase__	:						Optional[int]       =       (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["logits"].shape       , snake_case__       )
   def 			__a       (		self			:  Dict       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[Any]       =       MBartConfig(
            vocab_size=self.vocab_size       , d_model=1_4       , encoder_layers=2       , decoder_layers=2       , encoder_attention_heads=2       , decoder_attention_heads=2       , encoder_ffn_dim=8       , decoder_ffn_dim=8       , max_position_embeddings=4_8       , )
        UpperCAmelCase__	:						Optional[int]       =       FlaxMBartForConditionalGeneration(snake_case__       )
        UpperCAmelCase__	:						str       =       np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]       , dtype=np.intaa       )
        UpperCAmelCase__	:						Union[str, Any]       =       np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]       , dtype=np.intaa       )
        UpperCAmelCase__	:						Any       =       lm_model(input_ids=snake_case__       , decoder_input_ids=snake_case__       )
        UpperCAmelCase__	:						Optional[Any]       =       (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["logits"].shape       , snake_case__       )




   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Dict       =       np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]       , dtype=np.intaa       )
        UpperCAmelCase__	:						str       =       shift_tokens_right(snake_case__       , 1       )
        UpperCAmelCase__	:						List[Any]       =       np.equal(snake_case__       , 1       ).astype(np.floataa       ).sum()
        UpperCAmelCase__	:						int       =       np.equal(snake_case__       , 1       ).astype(np.floataa       ).sum()
        self.assertEqual(shifted.shape       , input_ids.shape       )
        self.assertEqual(snake_case__       , n_pad_before - 1       )
        self.assertTrue(np.equal(shifted[:, 0]       , 2       ).all()       )




@require_flax
class  lowerCAmelCase__ (						__magic_name__       ,	unittest.TestCase       ,	__magic_name__      ):
   SCREAMING_SNAKE_CASE_         =True
   SCREAMING_SNAKE_CASE_         =(
       (
           FlaxMBartModel,
           FlaxMBartForConditionalGeneration,
           FlaxMBartForSequenceClassification,
           FlaxMBartForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   SCREAMING_SNAKE_CASE_         =(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
   def 			__a       (		self			:  List[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Optional[Any]       =       FlaxMBartModelTester(self       )
   def 			__a       (		self			:  Dict       ):


        '''simple docstring'''
        UpperCAmelCase__       ,							UpperCAmelCase__	:						Optional[int]       =       self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(snake_case__       , snake_case__       , snake_case__       )
   def 			__a       (		self			:  Tuple       ):


        '''simple docstring'''
        UpperCAmelCase__       ,							UpperCAmelCase__	:						Any       =       self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(snake_case__       , snake_case__       , snake_case__       )
   def 			__a       (		self			:  Optional[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__       ,							UpperCAmelCase__	:						Tuple       =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__       ):
                  UpperCAmelCase__	:						Union[str, Any]       =       self._prepare_for_class(snake_case__       , snake_case__       )
                  UpperCAmelCase__	:						Union[str, Any]       =       model_class(snake_case__       )

                  @jax.jit
                  def encode_jitted(snake_case__			:  Any       , snake_case__			:  List[Any]=None       , **snake_case__			:  Optional[int]       ):
                       return model.encode(input_ids=snake_case__       , attention_mask=snake_case__       )

                  with self.subTest("JIT Enabled"       ):
                       UpperCAmelCase__	:						str       =       encode_jitted(**snake_case__       ).to_tuple()

                  with self.subTest("JIT Disabled"       ):
                       with jax.disable_jit():
                            UpperCAmelCase__	:						Union[str, Any]       =       encode_jitted(**snake_case__       ).to_tuple()

                  self.assertEqual(len(snake_case__       )       , len(snake_case__       )       )
                  for jitted_output, output in zip(snake_case__       , snake_case__       ):
                       self.assertEqual(jitted_output.shape       , output.shape       )
   def 			__a       (		self			:  List[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__       ,							UpperCAmelCase__	:						str       =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__       ):
                  UpperCAmelCase__	:						Any       =       model_class(snake_case__       )
                  UpperCAmelCase__	:						str       =       model.encode(inputs_dict["input_ids"]       , inputs_dict["attention_mask"]       )

                  UpperCAmelCase__	:						Tuple       =       {
                      "decoder_input_ids": inputs_dict["decoder_input_ids"],
                      "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                      "encoder_outputs": encoder_outputs,
                  }

                  @jax.jit
                  def decode_jitted(snake_case__			:  Union[str, Any]       , snake_case__			:  Optional[Any]       , snake_case__			:  Tuple       ):
                       return model.decode(
                           decoder_input_ids=snake_case__       , decoder_attention_mask=snake_case__       , encoder_outputs=snake_case__       , )

                  with self.subTest("JIT Enabled"       ):
                       UpperCAmelCase__	:						str       =       decode_jitted(**snake_case__       ).to_tuple()

                  with self.subTest("JIT Disabled"       ):
                       with jax.disable_jit():
                            UpperCAmelCase__	:						Tuple       =       decode_jitted(**snake_case__       ).to_tuple()

                  self.assertEqual(len(snake_case__       )       , len(snake_case__       )       )
                  for jitted_output, output in zip(snake_case__       , snake_case__       ):
                       self.assertEqual(jitted_output.shape       , output.shape       )




   @slow
   def 			__a       (		self			:  Tuple       ):


        '''simple docstring'''
        for model_class_name in self.all_model_classes:
             UpperCAmelCase__	:						Tuple       =       model_class_name.from_pretrained("facebook/mbart-large-cc25"       , from_pt=snake_case__       )
             # FlaxMBartForSequenceClassification expects eos token in input_ids
             UpperCAmelCase__	:						Union[str, Any]       =       np.ones((1, 1)       ) * model.config.eos_token_id
             UpperCAmelCase__	:						Optional[int]       =       model(snake_case__       )
             self.assertIsNotNone(snake_case__       )




@require_flax
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__ (						unittest.TestCase      ):
   SCREAMING_SNAKE_CASE_         =[
       ''' UN Chief Says There Is No Military Solution in Syria''',
   ]
   SCREAMING_SNAKE_CASE_         =[
       '''Şeful ONU declară că nu există o soluţie militară în Siria''',
   ]
   SCREAMING_SNAKE_CASE_         ='''facebook/mbart-large-en-ro'''
   @cached_property
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        return AutoTokenizer.from_pretrained(self.model_name       )
   @cached_property
   def 			__a       (		self			:  Union[str, Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       FlaxMBartForConditionalGeneration.from_pretrained(self.model_name       , from_pt=snake_case__       )
        return model
   def 			__a       (		self			:  Dict       , **snake_case__			:  Union[str, Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Union[str, Any]       =       self.translate_src_text(**snake_case__       )
        self.assertListEqual(self.expected_text       , snake_case__       )
   def 			__a       (		self			:  Optional[int]       , **snake_case__			:  List[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Any       =       self.tokenizer(self.src_text       , **snake_case__       , return_tensors="np"       )
        UpperCAmelCase__	:						Optional[Any]       =       self.model.generate(
            model_inputs.input_ids       , attention_mask=model_inputs.attention_mask       , decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"]       , early_stopping=snake_case__       , num_beams=2       , ).sequences
        UpperCAmelCase__	:						List[str]       =       self.tokenizer.batch_decode(snake_case__       , skip_special_tokens=snake_case__       )
        return generated_words




   @slow
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        self._assert_generated_batch_equal_expected()










"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase  :  List[Any]   =			{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  str   =			["""MarkupLMTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  List[str]   =			[
     """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """MarkupLMForQuestionAnswering""",
     """MarkupLMForSequenceClassification""",
     """MarkupLMForTokenClassification""",
     """MarkupLMModel""",
     """MarkupLMPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
 from .feature_extraction_markuplm import MarkupLMFeatureExtractor
 from .processing_markuplm import MarkupLMProcessor
 from .tokenization_markuplm import MarkupLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_markuplm_fast import MarkupLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_markuplm import (
      MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
      MarkupLMPreTrainedModel,
  )


else:
 import sys

 _lowerCAmelCase  :  Dict   =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


