





import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

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


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor

class 		__a							(	unittest.TestCase      ):



       def __init__(     self	:   Tuple					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   List[str]=3					,			UpperCAmelCase	:   Optional[int]=32					,			UpperCAmelCase	:   List[str]=3					,			UpperCAmelCase	:   List[Any]=10					,			UpperCAmelCase	:   Any=[10, 20, 30, 40]					,			UpperCAmelCase	:   List[Any]=[1, 1, 2, 1]					,			UpperCAmelCase	:   List[str]=True					,			UpperCAmelCase	:   List[Any]=True					,			UpperCAmelCase	:   Optional[int]="relu"					,			UpperCAmelCase	:   Tuple=3					,			UpperCAmelCase	:   Optional[int]=None					,			):
           lowerCAmelCase_					:	Optional[int]						=   parent
           lowerCAmelCase_					:	List[str]						=   batch_size
           lowerCAmelCase_					:	Optional[Any]						=   image_size
           lowerCAmelCase_					:	Optional[int]						=   num_channels
           lowerCAmelCase_					:	Optional[int]						=   embeddings_size
           lowerCAmelCase_					:	Any						=   hidden_sizes
           lowerCAmelCase_					:	Optional[int]						=   depths
           lowerCAmelCase_					:	int						=   is_training
           lowerCAmelCase_					:	List[Any]						=   use_labels
           lowerCAmelCase_					:	List[str]						=   hidden_act
           lowerCAmelCase_					:	Any						=   num_labels
           lowerCAmelCase_					:	Dict						=   scope
           lowerCAmelCase_					:	Optional[int]						=   len(UpperCAmelCase			)



       def  A							(     self	:   Union[str, Any]			):
           lowerCAmelCase_					:	Tuple						=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

           lowerCAmelCase_					:	Optional[Any]						=   self.get_config()

           return config, pixel_values



       def  A							(     self	:   Any			):
           return ResNetConfig(
               num_channels=self.num_channels					,			embeddings_size=self.embeddings_size					,			hidden_sizes=self.hidden_sizes					,			depths=self.depths					,			hidden_act=self.hidden_act					,			num_labels=self.num_labels					,			image_size=self.image_size					,			)



       def  A							(     self	:   int					,			UpperCAmelCase	:   Optional[Any]					,			UpperCAmelCase	:   Optional[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   FlaxResNetModel(config=UpperCAmelCase			)
           lowerCAmelCase_					:	Optional[int]						=   model(UpperCAmelCase			)

           # Output shape (b, c, h, w)
           self.parent.assertEqual(
               result.last_hidden_state.shape					,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,			)



       def  A							(     self	:   Tuple					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   Optional[int]			):
           lowerCAmelCase_					:	List[Any]						=   self.num_labels
           lowerCAmelCase_					:	List[str]						=   FlaxResNetForImageClassification(config=UpperCAmelCase			)
           lowerCAmelCase_					:	Dict						=   model(UpperCAmelCase			)
           self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.num_labels)			)







       def  A							(     self	:   int			):
           lowerCAmelCase_					:	Tuple						=   self.prepare_config_and_inputs()
           lowerCAmelCase_					:	Optional[Any]						=   config_and_inputs
           lowerCAmelCase_					:	int						=   {'pixel_values': pixel_values}
           return config, inputs_dict






@require_flax
class 		__a							(	a_			,unittest.TestCase      ):
       __snake_case						:   Optional[Any]      =		(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

       __snake_case						:   List[Any]      =		False
       __snake_case						:   str      =		False
       __snake_case						:   str      =		False



       def  A							(     self	:   Optional[int]			):
           lowerCAmelCase_					:	Dict						=   FlaxResNetModelTester(self			)
           lowerCAmelCase_					:	List[Any]						=   ConfigTester(self					,			config_class=UpperCAmelCase					,			has_text_modality=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()



       def  A							(     self	:   Dict			):
           return



       def  A							(     self	:   int			):
           lowerCAmelCase_					:	Union[str, Any]						=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	Optional[int]						=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase			)



       @unittest.skip(reason="""ResNet does not use inputs_embeds"""			)
       def  A							(     self	:   Dict			):
           pass



       @unittest.skip(reason="""ResNet does not support input and output embeddings"""			)
       def  A							(     self	:   Union[str, Any]			):
           pass



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Dict						=   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               lowerCAmelCase_					:	Any						=   model_class(UpperCAmelCase			)
               lowerCAmelCase_					:	List[str]						=   inspect.signature(model.__call__			)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowerCAmelCase_					:	Dict						=   [*signature.parameters.keys()]

               lowerCAmelCase_					:	Dict						=   ['pixel_values']
               self.assertListEqual(arg_names[:1]					,			UpperCAmelCase			)



       def  A							(     self	:   List[Any]			):
           def check_hidden_states_output(UpperCAmelCase	:   Dict					,			UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   Optional[int]			):
               lowerCAmelCase_					:	Optional[int]						=   model_class(UpperCAmelCase			)

               lowerCAmelCase_					:	Optional[Any]						=   model(**self._prepare_for_class(UpperCAmelCase					,			UpperCAmelCase			)			)

               lowerCAmelCase_					:	Tuple						=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               lowerCAmelCase_					:	str						=   self.model_tester.num_stages
               self.assertEqual(len(UpperCAmelCase			)					,			expected_num_stages + 1			)



       @unittest.skip(reason="""ResNet does not use feedforward chunking"""			)
       def  A							(     self	:   int			):
           pass







       def  A							(     self	:   Union[str, Any]			):
           lowerCAmelCase_					:	List[Any]						=   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__			):
                   lowerCAmelCase_					:	Any						=   self._prepare_for_class(UpperCAmelCase					,			UpperCAmelCase			)
                   lowerCAmelCase_					:	List[str]						=   model_class(UpperCAmelCase			)

                   @jax.jit
                   def model_jitted(UpperCAmelCase	:   int					,			**UpperCAmelCase	:   str			):
                       return model(pixel_values=UpperCAmelCase					,			**UpperCAmelCase			)

                   with self.subTest("""JIT Enabled"""			):
                       lowerCAmelCase_					:	Dict						=   model_jitted(**UpperCAmelCase			).to_tuple()

                   with self.subTest("""JIT Disabled"""			):
                       with jax.disable_jit():
                           lowerCAmelCase_					:	Optional[int]						=   model_jitted(**UpperCAmelCase			).to_tuple()

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






def 			__UpperCamelCase				(			)	->      Optional[int]:
    '''simple docstring'''





    lowerCAmelCase_					:	Any						=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
    return image

@require_flax
class 		__a							(	unittest.TestCase      ):



       @cached_property
       def  A							(     self	:   int			):
           return AutoImageProcessor.from_pretrained("""microsoft/resnet-50"""			) if is_vision_available() else None







       @slow
       def  A							(     self	:   Any			):
           lowerCAmelCase_					:	Optional[Any]						=   FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""			)

           lowerCAmelCase_					:	str						=   self.default_image_processor
           lowerCAmelCase_					:	List[str]						=   prepare_img()
           lowerCAmelCase_					:	Optional[Any]						=   image_processor(images=UpperCAmelCase					,			return_tensors="""np"""			)

           lowerCAmelCase_					:	str						=   model(**UpperCAmelCase			)

           # verify the logits
           lowerCAmelCase_					:	Optional[int]						=   (1, 10_00)
           self.assertEqual(outputs.logits.shape					,			UpperCAmelCase			)

           lowerCAmelCase_					:	Dict						=   jnp.array([-11.10_69, -9.7877, -8.3777]			)

           self.assertTrue(jnp.allclose(outputs.logits[0, :3]					,			UpperCAmelCase					,			atol=1e-4			)			)









import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import FlavaImageProcessor, FlavaProcessor
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )

@require_vision
class 		__a							(	unittest.TestCase      ):



       def  A							(     self	:   List[str]			):
           lowerCAmelCase_					:	Dict						=   tempfile.mkdtemp()

           # fmt: off
           lowerCAmelCase_					:	Dict						=   ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
           # fmt: on
           lowerCAmelCase_					:	Any						=   os.path.join(self.tmpdirname					,			VOCAB_FILES_NAMES["""vocab_file"""]			)

           with open(self.vocab_file					,			"""w"""					,			encoding="""utf-8"""			) as fp:
               fp.write("""""".join([x + """\n""" for x in vocab_tokens]			)			)

           lowerCAmelCase_					:	int						=   {
               """image_mean""": FLAVA_IMAGE_MEAN,
               """image_std""": FLAVA_IMAGE_STD,
               """do_normalize""": True,
               """do_resize""": True,
               """size""": 2_24,
               """do_center_crop""": True,
               """crop_size""": 2_24,
               """input_size_patches""": 14,
               """total_mask_patches""": 75,
               """mask_group_max_patches""": None,
               """mask_group_min_patches""": 16,
               """mask_group_min_aspect_ratio""": 0.3,
               """mask_group_max_aspect_ratio""": None,
               """codebook_do_resize""": True,
               """codebook_size""": 1_12,
               """codebook_do_center_crop""": True,
               """codebook_crop_size""": 1_12,
               """codebook_do_map_pixels""": True,
               """codebook_do_normalize""": True,
               """codebook_image_mean""": FLAVA_CODEBOOK_MEAN,
               """codebook_image_std""": FLAVA_CODEBOOK_STD,
           }

           lowerCAmelCase_					:	Union[str, Any]						=   os.path.join(self.tmpdirname					,			UpperCAmelCase			)
           with open(self.image_processor_file					,			"""w"""					,			encoding="""utf-8"""			) as fp:
               json.dump(UpperCAmelCase					,			UpperCAmelCase			)



       def  A							(     self	:   Any					,			**UpperCAmelCase	:   Union[str, Any]			):
           return BertTokenizer.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   List[str]					,			**UpperCAmelCase	:   str			):
           return BertTokenizerFast.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   Any					,			**UpperCAmelCase	:   int			):
           return FlavaImageProcessor.from_pretrained(self.tmpdirname					,			**UpperCAmelCase			)



       def  A							(     self	:   Optional[int]			):
           shutil.rmtree(self.tmpdirname			)



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Union[str, Any]						=   [np.random.randint(2_55					,			size=(3, 30, 4_00)					,			dtype=np.uinta			)]

           lowerCAmelCase_					:	int						=   [Image.fromarray(np.moveaxis(UpperCAmelCase					,			0					,			-1			)			) for x in image_inputs]

           return image_inputs



       def  A							(     self	:   Tuple			):
           lowerCAmelCase_					:	str						=   self.get_tokenizer()
           lowerCAmelCase_					:	Dict						=   self.get_rust_tokenizer()
           lowerCAmelCase_					:	Optional[int]						=   self.get_image_processor()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)
           processor_slow.save_pretrained(self.tmpdirname			)
           lowerCAmelCase_					:	List[str]						=   FlavaProcessor.from_pretrained(self.tmpdirname					,			use_fast=UpperCAmelCase			)

           lowerCAmelCase_					:	List[Any]						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)
           processor_fast.save_pretrained(self.tmpdirname			)
           lowerCAmelCase_					:	List[str]						=   FlavaProcessor.from_pretrained(self.tmpdirname			)

           self.assertEqual(processor_slow.tokenizer.get_vocab()					,			tokenizer_slow.get_vocab()			)
           self.assertEqual(processor_fast.tokenizer.get_vocab()					,			tokenizer_fast.get_vocab()			)
           self.assertEqual(tokenizer_slow.get_vocab()					,			tokenizer_fast.get_vocab()			)
           self.assertIsInstance(processor_slow.tokenizer					,			UpperCAmelCase			)
           self.assertIsInstance(processor_fast.tokenizer					,			UpperCAmelCase			)

           self.assertEqual(processor_slow.image_processor.to_json_string()					,			image_processor.to_json_string()			)
           self.assertEqual(processor_fast.image_processor.to_json_string()					,			image_processor.to_json_string()			)
           self.assertIsInstance(processor_slow.image_processor					,			UpperCAmelCase			)
           self.assertIsInstance(processor_fast.image_processor					,			UpperCAmelCase			)



       def  A							(     self	:   Any			):
           lowerCAmelCase_					:	Any						=   FlavaProcessor(tokenizer=self.get_tokenizer()					,			image_processor=self.get_image_processor()			)
           processor.save_pretrained(self.tmpdirname			)

           lowerCAmelCase_					:	Union[str, Any]						=   self.get_tokenizer(bos_token="""(BOS)"""					,			eos_token="""(EOS)"""			)
           lowerCAmelCase_					:	Optional[Any]						=   self.get_image_processor(do_normalize=UpperCAmelCase					,			padding_value=1.0			)

           lowerCAmelCase_					:	Any						=   FlavaProcessor.from_pretrained(
               self.tmpdirname					,			bos_token="""(BOS)"""					,			eos_token="""(EOS)"""					,			do_normalize=UpperCAmelCase					,			padding_value=1.0			)

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

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



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Tuple						=   self.get_image_processor()
           lowerCAmelCase_					:	Union[str, Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	Tuple						=   self.prepare_image_inputs()

           lowerCAmelCase_					:	List[str]						=   image_processor(UpperCAmelCase					,			return_tensors="""np"""			)
           lowerCAmelCase_					:	Tuple						=   processor(images=UpperCAmelCase					,			return_tensors="""np"""			)

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

           # With rest of the args
           random.seed(12_34			)
           lowerCAmelCase_					:	Optional[Any]						=   image_processor(
               UpperCAmelCase					,			return_image_mask=UpperCAmelCase					,			return_codebook_pixels=UpperCAmelCase					,			return_tensors="""np"""			)
           random.seed(12_34			)
           lowerCAmelCase_					:	Dict						=   processor(
               images=UpperCAmelCase					,			return_image_mask=UpperCAmelCase					,			return_codebook_pixels=UpperCAmelCase					,			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  A							(     self	:   str			):
           lowerCAmelCase_					:	Tuple						=   self.get_image_processor()
           lowerCAmelCase_					:	List[Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	str						=   """lower newer"""

           lowerCAmelCase_					:	Dict						=   processor(text=UpperCAmelCase			)

           lowerCAmelCase_					:	List[Any]						=   tokenizer(UpperCAmelCase			)

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



       def  A							(     self	:   Optional[Any]			):
           lowerCAmelCase_					:	Optional[int]						=   self.get_image_processor()
           lowerCAmelCase_					:	List[Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	str						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	str						=   """lower newer"""
           lowerCAmelCase_					:	Any						=   self.prepare_image_inputs()

           lowerCAmelCase_					:	Optional[int]						=   processor(text=UpperCAmelCase					,			images=UpperCAmelCase			)

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

           # add extra args
           lowerCAmelCase_					:	str						=   processor(text=UpperCAmelCase					,			images=UpperCAmelCase					,			return_codebook_pixels=UpperCAmelCase					,			return_image_mask=UpperCAmelCase			)

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

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



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	str						=   self.get_image_processor()
           lowerCAmelCase_					:	int						=   self.get_tokenizer()

           lowerCAmelCase_					:	Union[str, Any]						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	Optional[Any]						=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowerCAmelCase_					:	List[str]						=   processor.batch_decode(UpperCAmelCase			)
           lowerCAmelCase_					:	Tuple						=   tokenizer.batch_decode(UpperCAmelCase			)

           self.assertListEqual(UpperCAmelCase					,			UpperCAmelCase			)



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Tuple						=   self.get_image_processor()
           lowerCAmelCase_					:	Union[str, Any]						=   self.get_tokenizer()

           lowerCAmelCase_					:	int						=   FlavaProcessor(tokenizer=UpperCAmelCase					,			image_processor=UpperCAmelCase			)

           lowerCAmelCase_					:	List[str]						=   """lower newer"""
           lowerCAmelCase_					:	Dict						=   self.prepare_image_inputs()

           lowerCAmelCase_					:	Optional[int]						=   processor(text=UpperCAmelCase					,			images=UpperCAmelCase			)

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


