



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	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_=13		,       lowerCamelCase_=30		,       lowerCamelCase_=2		,       lowerCamelCase_=3		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=32		,       lowerCamelCase_=5		,       lowerCamelCase_=4		,       lowerCamelCase_=37		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.1		,       lowerCamelCase_=10		,       lowerCamelCase_=0.02		,       ):
						"""simple docstring"""

						a					     =   parent
						a					     =   batch_size
						a					     =   image_size
						a					     =   patch_size
						a					     =   num_channels
						a					     =   is_training
						a					     =   use_labels
						a					     =   hidden_size
						a					     =   num_hidden_layers
						a					     =   num_attention_heads
						a					     =   intermediate_size
						a					     =   hidden_act
						a					     =   hidden_dropout_prob
						a					     =   attention_probs_dropout_prob
						a					     =   type_sequence_label_size
						a					     =   initializer_range

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						a					     =   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		,       )

						return config, pixel_values




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   FlaxViTModel(config=lowerCamelCase_		)
						a					     =   model(lowerCamelCase_		)
						# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
						a					     =   (self.image_size, self.image_size)
						a					     =   (self.patch_size, self.patch_size)
						a					     =   (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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   self.type_sequence_label_size
						a					     =   FlaxViTForImageClassification(config=lowerCamelCase_		)
						a					     =   model(lowerCamelCase_		)
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.type_sequence_label_size)		)

						# test greyscale images
						a					     =   1
						a					     =   FlaxViTForImageClassification(lowerCamelCase_		)

						a					     =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						a					     =   model(lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




@require_flax
class 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   FlaxViTModelTester(self		)
						a					     =   ConfigTester(self		,       config_class=lowerCamelCase_		,       has_text_modality=lowerCamelCase_		,       hidden_size=37		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.config_tester.run_common_tests()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()

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

											a					     =   ["pixel_values"]
											self.assertListEqual(arg_names[:1]		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a     ,				a					     =   self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__		):
																a					     =   self._prepare_for_class(lowerCamelCase_		,       lowerCamelCase_		)
																a					     =   model_class(lowerCamelCase_		)

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

																with self.subTest("JIT Enabled"		):
																					a					     =   model_jitted(**lowerCamelCase_		).to_tuple()

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

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




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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





import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase: Optional[int]      			=		get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
class 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							XLMProphetNetTokenizer
	__A							=							False
	__A							=							True




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()

						# We have a SentencePiece fixture for testing
						a					     =   XLMProphetNetTokenizer(lowerCamelCase_		,       keep_accents=lowerCamelCase_		)
						tokenizer.save_pretrained(self.tmpdirname		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "[PAD]"
						a					     =   0

						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		):
						"""simple docstring"""

						a					     =   list(self.get_tokenizer().get_vocab().keys()		)

						self.assertEqual(vocab_keys[0]		,       "[PAD]"		)
						self.assertEqual(vocab_keys[1]		,       "[CLS]"		)
						self.assertEqual(vocab_keys[-1]		,       "j"		)
						self.assertEqual(len(lowerCamelCase_		)		,       1012		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.assertEqual(self.get_tokenizer().vocab_size		,       1012		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   XLMProphetNetTokenizer(lowerCamelCase_		,       keep_accents=lowerCamelCase_		)

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

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(lowerCamelCase_		)		,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,       )

						a					     =   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",
						        ".",
						    ]		,       )
						a					     =   tokenizer.convert_tokens_to_ids(lowerCamelCase_		)
						self.assertListEqual(
						    lowerCamelCase_		,       [
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ]		,       )

						a					     =   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		):
						"""simple docstring"""

						return XLMProphetNetTokenizer.from_pretrained("microsoft/xprophetnet-large-wiki100-cased"		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   "Hello World!"
						a					     =   [35389, 6672, 49, 2]
						self.assertListEqual(lowerCamelCase_		,       self.big_tokenizer.encode(lowerCamelCase_		)		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   {"input_ids": [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=lowerCamelCase_		,       model_name="microsoft/xprophetnet-large-wiki100-cased"		,       revision="1acad1643ddd54a44df6a1b797ada8373685d90e"		,       )
