





import inspect
import unittest

import numpy as np

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

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


if is_flax_available():
		import jax

		from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor






class 						UpperCamelCase_  (  unittest.TestCase							):



					def __init__(		self     :     int       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     Dict=1_00       ,      lowerCamelCase     :     Tuple=13       ,      lowerCamelCase     :     List[Any]=30       ,      lowerCamelCase     :     Optional[Any]=2       ,      lowerCamelCase     :     int=3       ,      lowerCamelCase     :     Optional[Any]=True       ,      lowerCamelCase     :     Any=True       ,      lowerCamelCase     :     List[str]=32       ,      lowerCamelCase     :     int=5       ,      lowerCamelCase     :     List[Any]=4       ,      lowerCamelCase     :     Optional[int]=37       ,      lowerCamelCase     :     Optional[Any]="gelu"       ,      lowerCamelCase     :     Tuple=0.1       ,      lowerCamelCase     :     Dict=0.1       ,      lowerCamelCase     :     int=10       ,      lowerCamelCase     :     List[Any]=0.02       ,      lowerCamelCase     :     Optional[Any]=3       ,      ):
							lowerCamelCase_	:     List[str] 						=      parent
							lowerCamelCase_	:     Union[str, Any] 						=      vocab_size
							lowerCamelCase_	:     List[str] 						=      batch_size
							lowerCamelCase_	:     Union[str, Any] 						=      image_size
							lowerCamelCase_	:     int 						=      patch_size
							lowerCamelCase_	:     Tuple 						=      num_channels
							lowerCamelCase_	:     Union[str, Any] 						=      is_training
							lowerCamelCase_	:     Tuple 						=      use_labels
							lowerCamelCase_	:     Any 						=      hidden_size
							lowerCamelCase_	:     Union[str, Any] 						=      num_hidden_layers
							lowerCamelCase_	:     List[Any] 						=      num_attention_heads
							lowerCamelCase_	:     Any 						=      intermediate_size
							lowerCamelCase_	:     int 						=      hidden_act
							lowerCamelCase_	:     int 						=      hidden_dropout_prob
							lowerCamelCase_	:     int 						=      attention_probs_dropout_prob
							lowerCamelCase_	:     Union[str, Any] 						=      type_sequence_label_size
							lowerCamelCase_	:     int 						=      initializer_range

							# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							lowerCamelCase_	:     Optional[Any] 						=      (image_size // patch_size) ** 2
							lowerCamelCase_	:     Union[str, Any] 						=      num_patches + 1



					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     Dict 						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							lowerCamelCase_	:     Union[str, Any] 						=      None
							if self.use_labels:
									lowerCamelCase_	:     Optional[Any] 						=      ids_tensor([self.batch_size]       ,      self.type_sequence_label_size						)

							lowerCamelCase_	:     Dict 						=      BeitConfig(
							    vocab_size=self.vocab_size       ,      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, labels



					def   __a     (		self     :     Optional[int]       ,      lowerCamelCase     :     Optional[Any]       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     str						):
							lowerCamelCase_	:     Tuple 						=      FlaxBeitModel(config=lowerCamelCase						)
							lowerCamelCase_	:     str 						=      model(lowerCamelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)						)



					def   __a     (		self     :     str       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     Dict       ,      lowerCamelCase     :     List[str]						):
							lowerCamelCase_	:     str 						=      FlaxBeitForMaskedImageModeling(config=lowerCamelCase						)
							lowerCamelCase_	:     str 						=      model(lowerCamelCase						)
							self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length - 1, self.vocab_size)						)



					def   __a     (		self     :     Dict       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     List[str]						):
							lowerCamelCase_	:     List[str] 						=      self.type_sequence_label_size
							lowerCamelCase_	:     Any 						=      FlaxBeitForImageClassification(config=lowerCamelCase						)
							lowerCamelCase_	:     Optional[int] 						=      model(lowerCamelCase						)
							self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.type_sequence_label_size)						)

							# test greyscale images
							lowerCamelCase_	:     Tuple 						=      1
							lowerCamelCase_	:     List[Any] 						=      FlaxBeitForImageClassification(lowerCamelCase						)

							lowerCamelCase_	:     Optional[int] 						=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
							lowerCamelCase_	:     Optional[int] 						=      model(lowerCamelCase						)





					def   __a     (		self     :     str						):
							lowerCamelCase_	:     List[str] 						=      self.prepare_config_and_inputs()
							(
							    (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      
							)	:     int 						=      config_and_inputs
							lowerCamelCase_	:     Union[str, Any] 						=      {'pixel_values': pixel_values}
							return config, inputs_dict






@require_flax
class 						UpperCamelCase_  (  snake_case__  ,     unittest.TestCase							):
					_a      :						str						    =					(
					    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
					)



					def   __a     (		self     :     Tuple						):
							lowerCamelCase_	:     str 						=      FlaxBeitModelTester(self						)
							lowerCamelCase_	:     Tuple 						=      ConfigTester(self       ,      config_class=lowerCamelCase       ,      has_text_modality=lowerCamelCase       ,      hidden_size=37						)



					def   __a     (		self     :     Optional[int]						):
							self.config_tester.run_common_tests()



					def   __a     (		self     :     List[Any]						):
							lowerCamelCase_						,      lowerCamelCase_	:     Any 						=      self.model_tester.prepare_config_and_inputs_for_common()

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

									lowerCamelCase_	:     Optional[Any] 						=      ['pixel_values']
									self.assertListEqual(arg_names[:1]       ,      lowerCamelCase						)



					def   __a     (		self     :     Dict						):
							lowerCamelCase_						,      lowerCamelCase_	:     int 						=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

											with self.subTest('JIT Enabled'						):
													lowerCamelCase_	:     List[str] 						=      model_jitted(**lowerCamelCase						).to_tuple()

											with self.subTest('JIT Disabled'						):
													with jax.disable_jit():
															lowerCamelCase_	:     List[Any] 						=      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						)



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     Dict 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase						)



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     str 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase						)



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Optional[int] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase						)





					@slow
					def   __a     (		self     :     Union[str, Any]						):
							for model_class_name in self.all_model_classes:
									lowerCamelCase_	:     Optional[int] 						=      model_class_name.from_pretrained('microsoft/beit-base-patch16-224'						)
									lowerCamelCase_	:     Union[str, Any] 						=      model(np.ones((1, 3, 2_24, 2_24)						)						)
									self.assertIsNotNone(lowerCamelCase						)


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     Optional[Any] 						=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
		return image






@require_vision
@require_flax
class 						UpperCamelCase_  (  unittest.TestCase							):



					@cached_property
					def   __a     (		self     :     Optional[Any]						):
							return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'						) if is_vision_available() else None



					@slow
					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     Optional[int] 						=      FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'						)

							lowerCamelCase_	:     Any 						=      self.default_image_processor
							lowerCamelCase_	:     List[Any] 						=      prepare_img()
							lowerCamelCase_	:     List[Any] 						=      image_processor(images=lowerCamelCase       ,      return_tensors='np'						).pixel_values

							# prepare bool_masked_pos
							lowerCamelCase_	:     Any 						=      np.ones((1, 1_96)       ,      dtype=lowerCamelCase						)

							# forward pass
							lowerCamelCase_	:     Optional[Any] 						=      model(pixel_values=lowerCamelCase       ,      bool_masked_pos=lowerCamelCase						)
							lowerCamelCase_	:     int 						=      outputs.logits

							# verify the logits
							lowerCamelCase_	:     List[str] 						=      (1, 1_96, 81_92)
							self.assertEqual(logits.shape       ,      lowerCamelCase						)

							lowerCamelCase_	:     Union[str, Any] 						=      np.array(
							    [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]						)

							self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]       ,      lowerCamelCase       ,      atol=1E-2						)						)



					@slow
					def   __a     (		self     :     Tuple						):
							lowerCamelCase_	:     Union[str, Any] 						=      FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'						)

							lowerCamelCase_	:     List[str] 						=      self.default_image_processor
							lowerCamelCase_	:     Union[str, Any] 						=      prepare_img()
							lowerCamelCase_	:     List[Any] 						=      image_processor(images=lowerCamelCase       ,      return_tensors='np'						)

							# forward pass
							lowerCamelCase_	:     Dict 						=      model(**lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      outputs.logits

							# verify the logits
							lowerCamelCase_	:     List[Any] 						=      (1, 10_00)
							self.assertEqual(logits.shape       ,      lowerCamelCase						)

							lowerCamelCase_	:     List[str] 						=      np.array([-1.2_385, -1.0_987, -1.0_108]						)

							self.assertTrue(np.allclose(logits[0, :3]       ,      lowerCamelCase       ,      atol=1E-4						)						)

							lowerCamelCase_	:     Optional[int] 						=      2_81
							self.assertEqual(logits.argmax(-1						).item()       ,      lowerCamelCase						)





					@slow
					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     List[str] 						=      FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'						)

							lowerCamelCase_	:     List[Any] 						=      self.default_image_processor
							lowerCamelCase_	:     Optional[Any] 						=      prepare_img()
							lowerCamelCase_	:     Tuple 						=      image_processor(images=lowerCamelCase       ,      return_tensors='np'						)

							# forward pass
							lowerCamelCase_	:     Union[str, Any] 						=      model(**lowerCamelCase						)
							lowerCamelCase_	:     Dict 						=      outputs.logits

							# verify the logits
							lowerCamelCase_	:     Optional[int] 						=      (1, 2_18_41)
							self.assertEqual(logits.shape       ,      lowerCamelCase						)

							lowerCamelCase_	:     Tuple 						=      np.array([1.6_881, -0.2_787, 0.5_901]						)

							self.assertTrue(np.allclose(logits[0, :3]       ,      lowerCamelCase       ,      atol=1E-4						)						)

							lowerCamelCase_	:     Optional[int] 						=      23_96
							self.assertEqual(logits.argmax(-1						).item()       ,      lowerCamelCase						)







import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						UpperCamelCase_  (  snake_case__  ,     unittest.TestCase							):
					_a      :						Optional[Any]						    =					CodeGenTokenizer
					_a      :						Optional[Any]						    =					CodeGenTokenizerFast
					_a      :						Union[str, Any]						    =					True
					_a      :						Dict						    =					{'add_prefix_space': True}
					_a      :						Dict						    =					False



					def   __a     (		self     :     Union[str, Any]						):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowerCamelCase_	:     Optional[Any] 						=      [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							    '<|endoftext|>',
							]
							lowerCamelCase_	:     List[Any] 						=      dict(zip(lowerCamelCase       ,      range(len(lowerCamelCase						)						)						)						)
							lowerCamelCase_	:     Tuple 						=      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							lowerCamelCase_	:     Optional[int] 						=      {'unk_token': '<unk>'}

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



					def   __a     (		self     :     Optional[int]       ,      **lowerCamelCase     :     Dict						):
							kwargs.update(self.special_tokens_map						)
							return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,      **lowerCamelCase						)



					def   __a     (		self     :     List[Any]       ,      **lowerCamelCase     :     int						):
							kwargs.update(self.special_tokens_map						)
							return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,      **lowerCamelCase						)



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     List[str]						):
							lowerCamelCase_	:     List[str] 						=      'lower newer'
							lowerCamelCase_	:     str 						=      'lower newer'
							return input_text, output_text



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     List[Any] 						=      CodeGenTokenizer(self.vocab_file       ,      self.merges_file       ,      **self.special_tokens_map						)
							lowerCamelCase_	:     Union[str, Any] 						=      'lower newer'
							lowerCamelCase_	:     List[str] 						=      ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
							lowerCamelCase_	:     Dict 						=      tokenizer.tokenize(lowerCamelCase       ,      add_prefix_space=lowerCamelCase						)
							self.assertListEqual(lowerCamelCase       ,      lowerCamelCase						)

							lowerCamelCase_	:     Dict 						=      tokens + [tokenizer.unk_token]
							lowerCamelCase_	:     Dict 						=      [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase						)       ,      lowerCamelCase						)



					def   __a     (		self     :     str						):
							if not self.test_rust_tokenizer:
									return

							lowerCamelCase_	:     Dict 						=      self.get_tokenizer()
							lowerCamelCase_	:     Optional[Any] 						=      self.get_rust_tokenizer(add_prefix_space=lowerCamelCase						)

							lowerCamelCase_	:     int 						=      'lower newer'

							# Testing tokenization
							lowerCamelCase_	:     List[str] 						=      tokenizer.tokenize(lowerCamelCase       ,      add_prefix_space=lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      rust_tokenizer.tokenize(lowerCamelCase						)
							self.assertListEqual(lowerCamelCase       ,      lowerCamelCase						)

							# Testing conversion to ids without special tokens
							lowerCamelCase_	:     List[str] 						=      tokenizer.encode(lowerCamelCase       ,      add_special_tokens=lowerCamelCase       ,      add_prefix_space=lowerCamelCase						)
							lowerCamelCase_	:     str 						=      rust_tokenizer.encode(lowerCamelCase       ,      add_special_tokens=lowerCamelCase						)
							self.assertListEqual(lowerCamelCase       ,      lowerCamelCase						)

							# Testing conversion to ids with special tokens
							lowerCamelCase_	:     Dict 						=      self.get_rust_tokenizer(add_prefix_space=lowerCamelCase						)
							lowerCamelCase_	:     Tuple 						=      tokenizer.encode(lowerCamelCase       ,      add_prefix_space=lowerCamelCase						)
							lowerCamelCase_	:     Union[str, Any] 						=      rust_tokenizer.encode(lowerCamelCase						)
							self.assertListEqual(lowerCamelCase       ,      lowerCamelCase						)

							# Testing the unknown token
							lowerCamelCase_	:     Optional[int] 						=      tokens + [rust_tokenizer.unk_token]
							lowerCamelCase_	:     int 						=      [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCamelCase						)       ,      lowerCamelCase						)



					def   __a     (		self     :     Tuple       ,      *lowerCamelCase     :     Dict       ,      **lowerCamelCase     :     Tuple						):
							# It's very difficult to mix/test pretokenization with byte-level
							# And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
							pass



					def   __a     (		self     :     Optional[int]       ,      lowerCamelCase     :     Any=15						):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
											lowerCamelCase_	:     Optional[int] 						=      self.rust_tokenizer_class.from_pretrained(lowerCamelCase       ,      **lowerCamelCase						)

											# Simple input
											lowerCamelCase_	:     Any 						=      'This is a simple input'
											lowerCamelCase_	:     Optional[int] 						=      ['This is a simple input 1', 'This is a simple input 2']
											lowerCamelCase_	:     Optional[Any] 						=      ('This is a simple input', 'This is a pair')
											lowerCamelCase_	:     str 						=      [
											    ('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   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Optional[int] 						=      CodeGenTokenizer.from_pretrained(self.tmpdirname       ,      pad_token='<pad>'						)

							# Simple input
							lowerCamelCase_	:     Tuple 						=      'This is a simple input'
							lowerCamelCase_	:     Tuple 						=      ['This is a simple input looooooooong', 'This is a simple input']
							lowerCamelCase_	:     Any 						=      ('This is a simple input', 'This is a pair')
							lowerCamelCase_	:     Tuple 						=      [
							    ('This is a simple input loooooong', 'This is a simple input'),
							    ('This is a simple pair loooooong', 'This is a simple pair'),
							]

							lowerCamelCase_	:     str 						=      tokenizer.pad_token_id

							lowerCamelCase_	:     int 						=      tokenizer(lowerCamelCase       ,      padding='max_length'       ,      max_length=30       ,      return_tensors='np'						)
							lowerCamelCase_	:     List[Any] 						=      tokenizer(lowerCamelCase       ,      padding=lowerCamelCase       ,      truncate=lowerCamelCase       ,      return_tensors='np'						)
							lowerCamelCase_	:     Tuple 						=      tokenizer(*lowerCamelCase       ,      padding='max_length'       ,      max_length=60       ,      return_tensors='np'						)
							lowerCamelCase_	:     Union[str, Any] 						=      tokenizer(lowerCamelCase       ,      padding=lowerCamelCase       ,      truncate=lowerCamelCase       ,      return_tensors='np'						)

							# s
							# test single string max_length padding
							self.assertEqual(out_s['input_ids'].shape[-1]       ,      30						)
							self.assertTrue(pad_token_id in out_s['input_ids']						)
							self.assertTrue(0 in out_s['attention_mask']						)

							# s2
							# test automatic padding
							self.assertEqual(out_sa['input_ids'].shape[-1]       ,      33						)
							# long slice doesn't have padding
							self.assertFalse(pad_token_id in out_sa['input_ids'][0]						)
							self.assertFalse(0 in out_sa['attention_mask'][0]						)
							# short slice does have padding
							self.assertTrue(pad_token_id in out_sa['input_ids'][1]						)
							self.assertTrue(0 in out_sa['attention_mask'][1]						)

							# p
							# test single pair max_length padding
							self.assertEqual(out_p['input_ids'].shape[-1]       ,      60						)
							self.assertTrue(pad_token_id in out_p['input_ids']						)
							self.assertTrue(0 in out_p['attention_mask']						)

							# p2
							# test automatic padding pair
							self.assertEqual(out_pa['input_ids'].shape[-1]       ,      52						)
							# long slice pair doesn't have padding
							self.assertFalse(pad_token_id in out_pa['input_ids'][0]						)
							self.assertFalse(0 in out_pa['attention_mask'][0]						)
							# short slice pair does have padding
							self.assertTrue(pad_token_id in out_pa['input_ids'][1]						)
							self.assertTrue(0 in out_pa['attention_mask'][1]						)



					def   __a     (		self     :     str						):
							lowerCamelCase_	:     List[str] 						=      '$$$'
							lowerCamelCase_	:     Dict 						=      CodeGenTokenizer.from_pretrained(self.tmpdirname       ,      bos_token=lowerCamelCase       ,      add_bos_token=lowerCamelCase						)

							lowerCamelCase_	:     Optional[Any] 						=      'This is a simple input'
							lowerCamelCase_	:     Optional[Any] 						=      ['This is a simple input 1', 'This is a simple input 2']

							lowerCamelCase_	:     List[str] 						=      tokenizer.bos_token_id

							lowerCamelCase_	:     Optional[Any] 						=      tokenizer(lowerCamelCase						)
							lowerCamelCase_	:     Optional[int] 						=      tokenizer(lowerCamelCase						)

							self.assertEqual(out_s.input_ids[0]       ,      lowerCamelCase						)
							self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids						)						)

							lowerCamelCase_	:     str 						=      tokenizer.decode(out_s.input_ids						)
							lowerCamelCase_	:     Any 						=      tokenizer.batch_decode(out_sa.input_ids						)

							self.assertEqual(decode_s.split()[0]       ,      lowerCamelCase						)
							self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa						)						)



					@slow
					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     Optional[int] 						=      CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono'						)

							lowerCamelCase_	:     Tuple 						=      '\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#'
							lowerCamelCase_	:     Any 						=      '\nif len_a > len_b:      result = a\nelse:      result = b'

							lowerCamelCase_	:     int 						=      tokenizer.encode(lowerCamelCase						)
							lowerCamelCase_	:     Optional[int] 						=      ['^#', re.escape('<|endoftext|>'						), '^\'\'\'', '^"""', '\n\n\n']
							lowerCamelCase_	:     str 						=      tokenizer.decode(lowerCamelCase       ,      truncate_before_pattern=lowerCamelCase						)
							self.assertEqual(lowerCamelCase       ,      lowerCamelCase						)





					def   __a     (		self     :     Optional[Any]						):
							pass
