





"""simple docstring"""





import inspect
import unittest

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
       import jax

       from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class 		lowercase					(					unittest.TestCase					):





      def __init__(       self      ,						lowercase      ,						lowercase=13      ,						lowercase=30      ,						lowercase=2      ,						lowercase=3      ,						lowercase=True      ,						lowercase=True      ,						lowercase=32      ,						lowercase=5      ,						lowercase=4      ,						lowercase=37      ,						lowercase="gelu"      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=10      ,						lowercase=0.02      ,						)		->     int:
            lowerCAmelCase  						=       parent
            lowerCAmelCase  						=       batch_size
            lowerCAmelCase  						=       image_size
            lowerCAmelCase  						=       patch_size
            lowerCAmelCase  						=       num_channels
            lowerCAmelCase  						=       is_training
            lowerCAmelCase  						=       use_labels
            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  						=       type_sequence_label_size
            lowerCAmelCase  						=       initializer_range

            # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
            lowerCAmelCase  						=       (image_size // patch_size) ** 2
            lowerCAmelCase  						=       num_patches + 1





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

            lowerCAmelCase  						=       ViTConfig(
                image_size=self.image_size      ,						patch_size=self.patch_size      ,						num_channels=self.num_channels      ,						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      ,						is_decoder=A_      ,						initializer_range=self.initializer_range      ,						)

            return config, pixel_values





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     List[str]:
            lowerCAmelCase  						=       FlaxViTModel(config=A_ )
            lowerCAmelCase  						=       model(A_ )
            # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
            lowerCAmelCase  						=       (self.image_size, self.image_size)
            lowerCAmelCase  						=       (self.patch_size, self.patch_size)
            lowerCAmelCase  						=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
            self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, num_patches + 1, self.hidden_size) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     Dict:
            lowerCAmelCase  						=       self.type_sequence_label_size
            lowerCAmelCase  						=       FlaxViTForImageClassification(config=A_ )
            lowerCAmelCase  						=       model(A_ )
            self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.type_sequence_label_size) )

            # test greyscale images
            lowerCAmelCase  						=       1
            lowerCAmelCase  						=       FlaxViTForImageClassification(A_ )

            lowerCAmelCase  						=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
            lowerCAmelCase  						=       model(A_ )





      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.prepare_config_and_inputs()
            (
                lowerCAmelCase							
            )  						=       config_and_inputs
            lowerCAmelCase  						=       {'''pixel_values''': pixel_values}
            return config, inputs_dict







@require_flax
class 		lowercase					(					_UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





      def     _snake_case			(       self )		->     None:
            lowerCAmelCase  						=       FlaxViTModelTester(self )
            lowerCAmelCase  						=       ConfigTester(self      ,						config_class=A_      ,						has_text_modality=A_      ,						hidden_size=37 )





      def     _snake_case			(       self )		->     Dict:
            self.config_tester.run_common_tests()





      def     _snake_case			(       self )		->     Any:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*A_ )





      def     _snake_case			(       self )		->     Any:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*A_ )





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowerCAmelCase  						=       model_class(A_ )
                  lowerCAmelCase  						=       inspect.signature(model.__call__ )
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  lowerCAmelCase  						=       [*signature.parameters.keys()]

                  lowerCAmelCase  						=       ['''pixel_values''']
                  self.assertListEqual(arg_names[:1]      ,						A_ )





      def     _snake_case			(       self )		->     Any:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__ ):
                        lowerCAmelCase  						=       self._prepare_for_class(A_      ,						A_ )
                        lowerCAmelCase  						=       model_class(A_ )

                        @jax.jit
                        def model_jitted(lowercase      ,						**lowercase ):
                              return model(pixel_values=A_      ,						**A_ )

                        with self.subTest("""JIT Enabled""" ):
                              lowerCAmelCase  						=       model_jitted(**A_ ).to_tuple()

                        with self.subTest("""JIT Disabled""" ):
                              with jax.disable_jit():
                                    lowerCAmelCase  						=       model_jitted(**A_ ).to_tuple()

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





      @slow
      def     _snake_case			(       self )		->     int:





            for model_class_name in self.all_model_classes:
                  lowerCAmelCase  						=       model_class_name.from_pretrained("""google/vit-base-patch16-224""" )
                  lowerCAmelCase  						=       model(np.ones((1, 3, 224, 224) ) )
                  self.assertIsNotNone(A_ )









import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor







@require_torch
@require_vision
class   __lowercase      (unittest.TestCase ):
 _UpperCamelCase			  =  ViTImageProcessor if is_vision_available() else None
 @property
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					(3, 32, 128)
       __lowerCAmelCase					:						List[str]					=					tempfile.mkdtemp()

       # fmt: off
       __lowerCAmelCase					:						List[str]					=					['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
       # fmt: on
       __lowerCAmelCase					:						Optional[int]					=					dict(zip(A_      ,							range(len(A_					)					)					)					)

       __lowerCAmelCase					:						Tuple					=					os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['''vocab_file''']					)
       with open(self.vocab_file      ,							'''w'''      ,							encoding='''utf-8'''					) as fp:
             fp.write(json.dumps(A_					) + '''\n'''					)

       __lowerCAmelCase					:						Union[str, Any]					=					{
           '''do_normalize''': False,
           '''do_resize''': True,
           '''image_processor_type''': '''ViTImageProcessor''',
           '''resample''': 3,
           '''size''': {'''height''': 32, '''width''': 128},
       }
       __lowerCAmelCase					:						Optional[Any]					=					os.path.join(self.tmpdirname      ,							A_					)
       with open(self.image_processor_file      ,							'''w'''      ,							encoding='''utf-8'''					) as fp:
             json.dump(A_      ,							A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return ViTImageProcessor.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       shutil.rmtree(self.tmpdirname					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta					)

       __lowerCAmelCase					:						str					=					Image.fromarray(np.moveaxis(A_      ,							0      ,							-1					)					)

       return image_input
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.get_tokenizer()
       __lowerCAmelCase					:						List[Any]					=					self.get_image_processor()

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)
       __lowerCAmelCase					:						Union[str, Any]					=					MgpstrProcessor.from_pretrained(self.tmpdirname      ,							use_fast=A_					)

       self.assertEqual(processor.char_tokenizer.get_vocab()      ,							tokenizer.get_vocab()					)
       self.assertIsInstance(processor.char_tokenizer      ,							A_					)

       self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor.to_json_string()					)
       self.assertIsInstance(processor.image_processor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.get_tokenizer()
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_image_processor()

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)

       __lowerCAmelCase					:						List[Any]					=					self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''					)
       __lowerCAmelCase					:						int					=					self.get_image_processor(do_normalize=A_      ,							padding_value=1.0					)

       __lowerCAmelCase					:						int					=					MgpstrProcessor.from_pretrained(
           self.tmpdirname      ,							bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''      ,							do_normalize=A_      ,							padding_value=1.0					)

       self.assertEqual(processor.char_tokenizer.get_vocab()      ,							tokenizer_add_kwargs.get_vocab()					)
       self.assertIsInstance(processor.char_tokenizer      ,							A_					)

       self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()					)
       self.assertIsInstance(processor.image_processor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.get_image_processor()
       __lowerCAmelCase					:						Optional[Any]					=					self.get_tokenizer()

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Optional[int]					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						Optional[Any]					=					image_processor(A_      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						Tuple					=					processor(images=A_      ,							return_tensors='''np'''					)

       for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum()      ,							input_processor[key].sum()      ,							delta=1e-2					)
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.get_image_processor()
       __lowerCAmelCase					:						Union[str, Any]					=					self.get_tokenizer()

       __lowerCAmelCase					:						Optional[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Any					=					'''test'''

       __lowerCAmelCase					:						Dict					=					processor(text=A_					)

       __lowerCAmelCase					:						str					=					tokenizer(A_					)
       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						str					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[Any]					=					'''test'''
       __lowerCAmelCase					:						int					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						int					=					processor(text=A_      ,							images=A_					)

       self.assertListEqual(list(inputs.keys()					)      ,							['''pixel_values''', '''labels''']					)

       # test if it raises when no input is passed
       with pytest.raises(A_					):
             processor()
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					self.get_image_processor()
       __lowerCAmelCase					:						int					=					self.get_tokenizer()

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[str]					=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

       __lowerCAmelCase					:						Optional[int]					=					processor.char_decode(A_					)
       __lowerCAmelCase					:						Tuple					=					tokenizer.batch_decode(A_					)
       __lowerCAmelCase					:						Any					=					[seq.replace(''' '''      ,							''''''					) for seq in decoded_tok]

       self.assertListEqual(A_      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Union[str, Any]					=					None
       __lowerCAmelCase					:						Optional[Any]					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						List[Any]					=					processor(text=A_      ,							images=A_					)

       self.assertListEqual(list(inputs.keys()					)      ,							processor.model_input_names					)





 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.get_image_processor()
       __lowerCAmelCase					:						List[str]					=					self.get_tokenizer()

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						List[Any]					=					torch.randn(1      ,							27      ,							38					)
       __lowerCAmelCase					:						Optional[int]					=					torch.randn(1      ,							27      ,							5_0257					)
       __lowerCAmelCase					:						Optional[Any]					=					torch.randn(1      ,							27      ,							3_0522					)

       __lowerCAmelCase					:						List[str]					=					processor.batch_decode([char_input, bpe_input, wp_input]					)

       self.assertListEqual(list(results.keys()					)      ,							['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']					)


