



"""simple docstring"""






import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
         LiltForQuestionAnswering,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltModel,
     )
     from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST



class     UpperCAmelCase    :



   def __init__(       self    :	Any							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	Any=1_3							,       __lowerCamelCase    :	List[Any]=7							,       __lowerCamelCase    :	int=True							,       __lowerCamelCase    :	Optional[int]=True							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	Union[str, Any]=True							,       __lowerCamelCase    :	Optional[Any]=9_9							,       __lowerCamelCase    :	int=2_4							,       __lowerCamelCase    :	Optional[int]=2							,       __lowerCamelCase    :	int=6							,       __lowerCamelCase    :	str=3_7							,       __lowerCamelCase    :	int="gelu"							,       __lowerCamelCase    :	Optional[Any]=0.1							,       __lowerCamelCase    :	Union[str, Any]=0.1							,       __lowerCamelCase    :	Optional[int]=5_1_2							,       __lowerCamelCase    :	Optional[Any]=1_6							,       __lowerCamelCase    :	Dict=2							,       __lowerCamelCase    :	List[str]=0.0_2							,       __lowerCamelCase    :	Dict=3							,       __lowerCamelCase    :	int=None							,       __lowerCamelCase    :	Any=1_0_0_0							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      seq_length
     _snake_case   	=      is_training
     _snake_case   	=      use_input_mask
     _snake_case   	=      use_token_type_ids
     _snake_case   	=      use_labels
     _snake_case   	=      vocab_size
     _snake_case   	=      hidden_size
     _snake_case   	=      num_hidden_layers
     _snake_case   	=      num_attention_heads
     _snake_case   	=      intermediate_size
     _snake_case   	=      hidden_act
     _snake_case   	=      hidden_dropout_prob
     _snake_case   	=      attention_probs_dropout_prob
     _snake_case   	=      max_position_embeddings
     _snake_case   	=      type_vocab_size
     _snake_case   	=      type_sequence_label_size
     _snake_case   	=      initializer_range
     _snake_case   	=      num_labels
     _snake_case   	=      scope
     _snake_case   	=      range_bbox



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



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

     _snake_case   	=      ids_tensor([self.batch_size, self.seq_length, 4]							,       self.range_bbox				)
     # Ensure that bbox is legal
     for i in range(bbox.shape[0]				):
       for j in range(bbox.shape[1]				):
         if bbox[i, j, 3] < bbox[i, j, 1]:
           _snake_case   	=      bbox[i, j, 3]
           _snake_case   	=      bbox[i, j, 1]
           _snake_case   	=      t
         if bbox[i, j, 2] < bbox[i, j, 0]:
           _snake_case   	=      bbox[i, j, 2]
           _snake_case   	=      bbox[i, j, 0]
           _snake_case   	=      t

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

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

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

     _snake_case   	=      self.get_config()

     return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     return LiltConfig(
         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							,       )



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Optional[int]							,       ):



     """simple docstring"""
     _snake_case   	=      LiltModel(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase							,       bbox=__lowerCamelCase							,       attention_mask=__lowerCamelCase							,       token_type_ids=__lowerCamelCase				)
     _snake_case   	=      model(__lowerCamelCase							,       bbox=__lowerCamelCase							,       token_type_ids=__lowerCamelCase				)
     _snake_case   	=      model(__lowerCamelCase							,       bbox=__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    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	Tuple							,       __lowerCamelCase    :	Optional[int]							,       ):



     """simple docstring"""
     _snake_case   	=      self.num_labels
     _snake_case   	=      LiltForTokenClassification(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(
         __lowerCamelCase							,       bbox=__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    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Tuple							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Dict							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	List[str]							,       ):



     """simple docstring"""
     _snake_case   	=      LiltForQuestionAnswering(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(
         __lowerCamelCase							,       bbox=__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    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.prepare_config_and_inputs()
     (
         (
         _snake_case
     )	,     (
         _snake_case
     )	,     (
         _snake_case
     )	,     (
         _snake_case
     )	,     (
         _snake_case
     )	,     (
         _snake_case
     )	,     (
         _snake_case
     )	,     
     )   	=      config_and_inputs
     _snake_case   	=      {
         '''input_ids''': input_ids,
         '''bbox''': bbox,
         '''token_type_ids''': token_type_ids,
         '''attention_mask''': input_mask,
     }
     return config, inputs_dict



@require_torch
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Any           =  (
       (
           LiltModel,
           LiltForSequenceClassification,
           LiltForTokenClassification,
           LiltForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   A__		:	Any           =  (
       {
           '''feature-extraction''': LiltModel,
           '''question-answering''': LiltForQuestionAnswering,
           '''text-classification''': LiltForSequenceClassification,
           '''token-classification''': LiltForTokenClassification,
           '''zero-shot''': LiltForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   A__		:	Tuple           =  False
   A__		:	Optional[Any]           =  False



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     return True



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      LiltModelTester(self				)
     _snake_case   	=      ConfigTester(self							,       config_class=__lowerCamelCase							,       hidden_size=3_7				)



   def    __UpperCAmelCase    (       self    :	str				):



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



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
       _snake_case   	=      type
       self.model_tester.create_and_check_model(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase				)





   @slow
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _snake_case   	=      LiltModel.from_pretrained(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)



@require_torch
@slow
class     UpperCAmelCase    (	unittest.TestCase  ):



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''				).to(__lowerCamelCase				)

     _snake_case   	=      torch.tensor([[1, 2]]							,       device=__lowerCamelCase				)
     _snake_case   	=      torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]							,       device=__lowerCamelCase				)

     # forward pass
     with torch.no_grad():
       _snake_case   	=      model(input_ids=__lowerCamelCase							,       bbox=__lowerCamelCase				)

     _snake_case   	=      torch.Size([1, 2, 7_6_8]				)
     _snake_case   	=      torch.tensor(
         [[-0.0_6_5_3, 0.0_9_5_0, -0.0_0_6_1], [-0.0_5_4_5, 0.0_9_2_6, -0.0_3_2_4]]							,       device=__lowerCamelCase							,       )

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










"""simple docstring"""






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

snake_case   					=				'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
snake_case   					=				[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
snake_case   					=				dict(zip(vocab, range(len(vocab))))
snake_case   					=				['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
     snake_case   					=				Path(tmpdirname)
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''merges_file''']
     with open(src_vocab_file, '''w''') as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, '''w''') as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, '''w''') as fp:
          fp.write('''\n'''.join(merges))

     snake_case   					=				FSMTTokenizer(
         langs=['''en''', '''ru'''],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

snake_case   					=				FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

snake_case   					=				FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
snake_case   					=				tokenizer(['''Making tiny model'''], return_tensors='''pt''')
snake_case   					=				tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru





