






'''simple docstring'''







import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )




class 		UpperCamelCase__    (unittest.TestCase			):




  '''simple docstring'''





  def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=13   ,_lowerCAmelCase=7   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=99   ,_lowerCAmelCase=32   ,_lowerCAmelCase=5   ,_lowerCAmelCase=4   ,_lowerCAmelCase=37   ,_lowerCAmelCase="gelu"   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=5_12   ,_lowerCAmelCase=16   ,_lowerCAmelCase=2   ,_lowerCAmelCase=0.02   ,_lowerCAmelCase=4   ,):
        lowerCamelCase__         =  parent
        lowerCamelCase__         =  batch_size
        lowerCamelCase__         =  seq_length
        lowerCamelCase__         =  is_training
        lowerCamelCase__         =  use_attention_mask
        lowerCamelCase__         =  use_token_type_ids
        lowerCamelCase__         =  use_labels
        lowerCamelCase__         =  vocab_size
        lowerCamelCase__         =  hidden_size
        lowerCamelCase__         =  num_hidden_layers
        lowerCamelCase__         =  num_attention_heads
        lowerCamelCase__         =  intermediate_size
        lowerCamelCase__         =  hidden_act
        lowerCamelCase__         =  hidden_dropout_prob
        lowerCamelCase__         =  attention_probs_dropout_prob
        lowerCamelCase__         =  max_position_embeddings
        lowerCamelCase__         =  type_vocab_size
        lowerCamelCase__         =  type_sequence_label_size
        lowerCamelCase__         =  initializer_range
        lowerCamelCase__         =  num_choices






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size       )

        lowerCamelCase__         =  None
        if self.use_attention_mask:
              lowerCamelCase__         =  random_attention_mask([self.batch_size, self.seq_length]       )

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

        lowerCamelCase__         =  RoFormerConfig(
            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   ,is_decoder=_lowerCAmelCase   ,initializer_range=self.initializer_range   ,)

        return config, input_ids, token_type_ids, attention_mask






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  self.prepare_config_and_inputs()
        lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__         =  config_and_inputs
        lowerCamelCase__         =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
        return config, inputs_dict




@require_flax
class 		UpperCamelCase__    (a   ,unittest.TestCase			):




  '''simple docstring'''





  _UpperCamelCase				  =					True

  _UpperCamelCase				  =					(
      (
          FlaxRoFormerModel,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )






  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  FlaxRoFormerModelTester(self       )






  @slow
  def 			UpperCamelCase_					(      self       ):
        for model_class_name in self.all_model_classes:
              lowerCamelCase__         =  model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""   ,from_pt=_lowerCAmelCase       )
              lowerCamelCase__         =  model(np.ones((1, 1)       )       )
              self.assertIsNotNone(_lowerCAmelCase       )




@require_flax
class 		UpperCamelCase__    (unittest.TestCase			):




  '''simple docstring'''





  @slow
  def 			UpperCamelCase_					(      self       ):
        lowerCamelCase__         =  FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""       )
        lowerCamelCase__         =  jnp.array([[0, 1, 2, 3, 4, 5]]       )
        lowerCamelCase__         =  model(_lowerCAmelCase       )[0]

        lowerCamelCase__         =  5_00_00

        lowerCamelCase__         =  (1, 6, vocab_size)
        self.assertEqual(output.shape   ,_lowerCAmelCase       )

        lowerCamelCase__         =  jnp.array(
            [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]       )

        self.assertTrue(jnp.allclose(output[:, :3, :3]   ,_lowerCAmelCase   ,atol=1E-4       )       )









'''simple docstring'''







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  tempfile.mkdtemp()

								lowerCamelCase__         =  BlipImageProcessor()
								lowerCamelCase__         =  BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""       )

								lowerCamelCase__         =  BlipProcessor(_lowerCAmelCase   ,_lowerCAmelCase       )

								processor.save_pretrained(self.tmpdirname       )






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return AutoProcessor.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       ).tokenizer






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return AutoProcessor.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       ).image_processor






		def 			UpperCamelCase_					(      self       ):
								shutil.rmtree(self.tmpdirname       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  [np.random.randint(2_55   ,size=(3, 30, 4_00)   ,dtype=np.uinta       )]

								lowerCamelCase__         =  [Image.fromarray(np.moveaxis(_lowerCAmelCase   ,0   ,-1       )       ) for x in image_inputs]

								return image_inputs






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  BlipProcessor(tokenizer=self.get_tokenizer()   ,image_processor=self.get_image_processor()       )
								processor.save_pretrained(self.tmpdirname       )

								lowerCamelCase__         =  self.get_tokenizer(bos_token="""(BOS)"""   ,eos_token="""(EOS)"""       )
								lowerCamelCase__         =  self.get_image_processor(do_normalize=_lowerCAmelCase   ,padding_value=1.0       )

								lowerCamelCase__         =  BlipProcessor.from_pretrained(
								    self.tmpdirname   ,bos_token="""(BOS)"""   ,eos_token="""(EOS)"""   ,do_normalize=_lowerCAmelCase   ,padding_value=1.0       )

								self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer_add_kwargs.get_vocab()       )
								self.assertIsInstance(processor.tokenizer   ,_lowerCAmelCase       )

								self.assertEqual(processor.image_processor.to_json_string()   ,image_processor_add_kwargs.to_json_string()       )
								self.assertIsInstance(processor.image_processor   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  image_processor(_lowerCAmelCase   ,return_tensors="""np"""       )
								lowerCamelCase__         =  processor(images=_lowerCAmelCase   ,return_tensors="""np"""       )

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()   ,input_processor[key].sum()   ,delta=1E-2       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""

								lowerCamelCase__         =  processor(text=_lowerCAmelCase       )

								lowerCamelCase__         =  tokenizer(_lowerCAmelCase   ,return_token_type_ids=_lowerCAmelCase       )

								for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]   ,encoded_processor[key]       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""
								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  processor(text=_lowerCAmelCase   ,images=_lowerCAmelCase       )

								self.assertListEqual(list(inputs.keys()       )   ,["""pixel_values""", """input_ids""", """attention_mask"""]       )

								# test if it raises when no input is passed
								with pytest.raises(_lowerCAmelCase       ):
														processor()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								lowerCamelCase__         =  processor.batch_decode(_lowerCAmelCase       )
								lowerCamelCase__         =  tokenizer.batch_decode(_lowerCAmelCase       )

								self.assertListEqual(_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""
								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  processor(text=_lowerCAmelCase   ,images=_lowerCAmelCase       )

								# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
								self.assertListEqual(list(inputs.keys()       )   ,["""pixel_values""", """input_ids""", """attention_mask"""]       )

