





'''simple docstring'''



from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=13						,   lowerCamelCase=30						,   lowerCamelCase=2						,   lowerCamelCase=3						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=32						,   lowerCamelCase=2						,   lowerCamelCase=4						,   lowerCamelCase=37						,   lowerCamelCase="gelu"						,   lowerCamelCase=0.1						,   lowerCamelCase=0.1						,   lowerCamelCase=10						,   lowerCamelCase=0.02						,   lowerCamelCase=3						,   lowerCamelCase=None						,   ):
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				image_size
       _snake_case     		=				patch_size
       _snake_case     		=				num_channels
       _snake_case     		=				is_training
       _snake_case     		=				use_labels
       _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     		=				type_sequence_label_size
       _snake_case     		=				initializer_range
       _snake_case     		=				scope

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

       _snake_case     		=				self.get_config()

       return config, pixel_values, labels

     def 	UpperCamelCase(					self	):
       return 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=lowerCamelCase						,   initializer_range=self.initializer_range						,   )

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				TFViTModel(config=lowerCamelCase	)
       _snake_case     		=				model(lowerCamelCase						,   training=lowerCamelCase	)
       self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size)	)

       # Test with an image with different size than the one specified in config.
       _snake_case     		=				self.image_size // 2
       _snake_case     		=				pixel_values[:, :, :image_size, :image_size]
       _snake_case     		=				model(lowerCamelCase						,   interpolate_pos_encoding=lowerCamelCase						,   training=lowerCamelCase	)
       _snake_case     		=				(image_size // self.patch_size) ** 2 + 1
       self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, seq_length, self.hidden_size)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				self.type_sequence_label_size
       _snake_case     		=				TFViTForImageClassification(lowerCamelCase	)
       _snake_case     		=				model(lowerCamelCase						,   labels=lowerCamelCase						,   training=lowerCamelCase	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.type_sequence_label_size)	)

       # Test with an image with different size than the one specified in config.
       _snake_case     		=				self.image_size // 2
       _snake_case     		=				pixel_values[:, :, :image_size, :image_size]
       _snake_case     		=				model(lowerCamelCase						,   interpolate_pos_encoding=lowerCamelCase						,   training=lowerCamelCase	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.type_sequence_label_size)	)

       # test greyscale images
       _snake_case     		=				1
       _snake_case     		=				TFViTForImageClassification(lowerCamelCase	)
       _snake_case     		=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
       _snake_case     		=				model(lowerCamelCase	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.type_sequence_label_size)	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.prepare_config_and_inputs()
       _snake_case		,		_snake_case		,		_snake_case     		=				config_and_inputs
       _snake_case     		=				{"pixel_values": pixel_values}
       return config, inputs_dict







@require_tf
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    __UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[int]      =   (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
     UpperCAmelCase__       :						int      =   (
         {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
         if is_tf_available()
         else {}
     )

     UpperCAmelCase__       :						Union[str, Any]      =   False
     UpperCAmelCase__       :						str      =   False
     UpperCAmelCase__       :						Optional[Any]      =   False

     def 	UpperCamelCase(					self	):
       _snake_case     		=				TFViTModelTester(self	)
       _snake_case     		=				ConfigTester(self						,   config_class=lowerCamelCase						,   has_text_modality=lowerCamelCase						,   hidden_size=37	)

     def 	UpperCamelCase(					self	):
       self.config_tester.run_common_tests()

     @unittest.skip(reason="ViT does not use inputs_embeds"	)
     def 	UpperCamelCase(					self	):
       pass

     @unittest.skip(reason="ViT does not use inputs_embeds"	)
     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _snake_case     		=				model_class(lowerCamelCase	)
         self.assertIsInstance(model.get_input_embeddings()						,   (tf.keras.layers.Layer)	)
         _snake_case     		=				model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowerCamelCase						,   tf.keras.layers.Layer	)	)

     def 	UpperCamelCase(					self	):
       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

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

         _snake_case     		=				["pixel_values"]
         self.assertListEqual(arg_names[:1]						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase	)






     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				TFViTModel.from_pretrained("google/vit-base-patch16-224"	)
       self.assertIsNotNone(lowerCamelCase	)







def 				snake_case_      (		):







  '''simple docstring'''



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




@require_tf
@require_vision
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     @cached_property
     def 	UpperCamelCase(					self	):
       return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"	) if is_vision_available() else None






     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"	)

       _snake_case     		=				self.default_image_processor
       _snake_case     		=				prepare_img()
       _snake_case     		=				image_processor(images=lowerCamelCase						,   return_tensors="tf"	)

       # forward pass
       _snake_case     		=				model(**lowerCamelCase	)

       # verify the logits
       _snake_case     		=				tf.TensorShape((1, 1_000)	)
       self.assertEqual(outputs.logits.shape						,   lowerCamelCase	)

       _snake_case     		=				tf.constant([-0.2744, 0.8215, -0.0836]	)

       tf.debugging.assert_near(outputs.logits[0, :3]						,   lowerCamelCase						,   atol=1e-4	)







'''simple docstring'''



import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__   :						Optional[Any]    =			get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Any      =   ReformerTokenizer
     UpperCAmelCase__       :						List[str]      =   ReformerTokenizerFast
     UpperCAmelCase__       :						Any      =   True
     UpperCAmelCase__       :						Union[str, Any]      =   False
     UpperCAmelCase__       :						Optional[Any]      =   True

     def 	UpperCamelCase(					self	):
       super().setUp()

       _snake_case     		=				ReformerTokenizer(lowerCamelCase						,   keep_accents=lowerCamelCase	)
       tokenizer.save_pretrained(self.tmpdirname	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"<s>"
       _snake_case     		=				1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase	)						,   lowerCamelCase	)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase	)						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				list(self.get_tokenizer().get_vocab().keys()	)

       self.assertEqual(vocab_keys[0]						,   "<unk>"	)
       self.assertEqual(vocab_keys[1]						,   "<s>"	)
       self.assertEqual(vocab_keys[-1]						,   "j"	)
       self.assertEqual(len(lowerCamelCase	)						,   1_000	)

     def 	UpperCamelCase(					self	):
       self.assertEqual(self.get_tokenizer().vocab_size						,   1_000	)

     def 	UpperCamelCase(					self	):
       if not self.test_rust_tokenizer:
         return

       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_rust_tokenizer()

       _snake_case     		=				"I was born in 92000, and this is falsé."

       _snake_case     		=				tokenizer.tokenize(lowerCamelCase	)
       _snake_case     		=				rust_tokenizer.tokenize(lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
       _snake_case     		=				rust_tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				self.get_rust_tokenizer()
       _snake_case     		=				tokenizer.encode(lowerCamelCase	)
       _snake_case     		=				rust_tokenizer.encode(lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase=15	):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)

           # Simple input
           _snake_case     		=				"This is a simple input"
           _snake_case     		=				["This is a simple input 1", "This is a simple input 2"]
           _snake_case     		=				("This is a simple input", "This is a pair")
           _snake_case     		=				[
               ("This is a simple input 1", "This is a simple input 2"),
               ("This is a simple pair 1", "This is a simple pair 2"),
           ]

           # Simple input tests
           self.assertRaises(lowerCamelCase						,   tokenizer_r.encode						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Simple input
           self.assertRaises(lowerCamelCase						,   tokenizer_r.encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Simple input
           self.assertRaises(
               lowerCamelCase						,   tokenizer_r.batch_encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"						,   )

           # Pair input
           self.assertRaises(lowerCamelCase						,   tokenizer_r.encode						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Pair input
           self.assertRaises(lowerCamelCase						,   tokenizer_r.encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Pair input
           self.assertRaises(
               lowerCamelCase						,   tokenizer_r.batch_encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"						,   )

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       _snake_case     		=				ReformerTokenizer(lowerCamelCase						,   keep_accents=lowerCamelCase	)

       _snake_case     		=				tokenizer.tokenize("This is a test"	)
       self.assertListEqual(lowerCamelCase						,   ["▁This", "▁is", "▁a", "▁t", "est"]	)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,   [285, 46, 10, 170, 382]						,   )

       _snake_case     		=				tokenizer.tokenize("I was born in 92000, and this is falsé."	)
       self.assertListEqual(
           lowerCamelCase						,   [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "9",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "é",
               ".",
           ]						,   )
       _snake_case     		=				tokenizer.convert_tokens_to_ids(lowerCamelCase	)
       self.assertListEqual(
           lowerCamelCase						,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,   )

       _snake_case     		=				tokenizer.convert_ids_to_tokens(lowerCamelCase	)
       self.assertListEqual(
           lowerCamelCase						,   [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "<unk>",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "<unk>",
               ".",
           ]						,   )

     @cached_property
     def 	UpperCamelCase(					self	):
       return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"	)

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				"Hello World!"
       _snake_case     		=				[126, 32, 262, 152, 38, 72, 287]

       self.assertListEqual(lowerCamelCase						,   self.big_tokenizer.encode(lowerCamelCase	)	)

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				(
           "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
           " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
       )
       _snake_case     		=				[
           108,
           265,
           24,
           111,
           4,
           258,
           156,
           35,
           28,
           275,
           3,
           259,
           297,
           260,
           84,
           4,
           35,
           110,
           44,
           8,
           259,
           91,
           268,
           21,
           11,
           209,
           274,
           109,
           266,
           277,
           117,
           86,
           93,
           315,
           258,
           278,
           258,
           277,
           258,
           0,
           258,
           288,
           258,
           319,
           258,
           0,
           258,
           0,
           258,
           0,
           258,
           0,
           258,
           287,
           258,
           315,
           258,
           289,
           258,
           278,
           99,
           269,
           266,
           262,
           8,
           259,
           241,
           4,
           217,
           230,
           268,
           266,
           55,
           168,
           106,
           75,
           193,
           266,
           223,
           27,
           49,
           26,
           282,
           25,
           264,
           299,
           19,
           26,
           0,
           258,
           277,
           117,
           86,
           93,
           176,
           183,
           270,
           11,
           262,
           42,
           61,
           265,
       ]

       self.assertListEqual(lowerCamelCase						,   self.big_tokenizer.encode(lowerCamelCase	)	)

     @require_torch
     @slow
     def 	UpperCamelCase(					self	):
       import torch

       from transformers import ReformerConfig, ReformerModel

       # Build sequence
       _snake_case     		=				list(self.big_tokenizer.get_vocab().keys()	)[:10]
       _snake_case     		=				" ".join(lowerCamelCase	)
       _snake_case     		=				self.big_tokenizer.encode_plus(lowerCamelCase						,   return_tensors="pt"	)
       _snake_case     		=				self.big_tokenizer.batch_encode_plus([sequence, sequence]						,   return_tensors="pt"	)

       _snake_case     		=				ReformerConfig()
       # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
       _snake_case     		=				encoded_sequence["input_ids"].shape
       _snake_case     		=				ReformerModel(lowerCamelCase	)

       # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
       assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

       with torch.no_grad():
         model(**lowerCamelCase	)
         model(**lowerCamelCase	)






     @slow
     def 	UpperCamelCase(					self	):
       # fmt: off
       _snake_case     		=				{"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # This tokenizer does not know some characters like ")".
       # That is the reason why we use very simple texts here.
       # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
       _snake_case     		=				[
           "This is a very simple sentence.",
           "The quick brown fox jumps over the lazy dog.",
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=lowerCamelCase						,   model_name="google/reformer-crime-and-punishment"						,   revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"						,   padding=lowerCamelCase						,   sequences=lowerCamelCase						,   )


