import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class     __lowerCAmelCase					(    __snake_case		):
			UpperCamelCase           =  ['''image_processor''', '''tokenizer''']
			UpperCamelCase           =  '''OwlViTImageProcessor'''
			UpperCamelCase           =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')



			def __init__(		self     :						str				,	A     :						int=None				,	A     :						str=None				,	**A     :						int)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
									    ' instead.'				,	_lowercase				,	)
									_UpperCAmelCase	 =     kwargs.pop('feature_extractor')

						_UpperCAmelCase	 =     image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError('You need to specify an `image_processor`.')
						if tokenizer is None:
									raise ValueError('You need to specify a `tokenizer`.')

						super().__init__(_lowercase				,	_lowercase)



			def __call__(		self     :						List[str]				,	A     :						str=None				,	A     :						Tuple=None				,	A     :						Any=None				,	A     :						str="max_length"				,	A     :						Dict="np"				,	**A     :						List[Any])     ->					Dict:



						"""simple docstring"""






						if text is None and query_images is None and images is None:
									raise ValueError(
									    'You have to specify at least one text or query image or image. All three cannot be none.')

						if text is not None:
									if isinstance(_lowercase				,	_lowercase) or (isinstance(_lowercase				,	_lowercase) and not isinstance(text[0]				,	_lowercase)):
												_UpperCAmelCase	 =     [self.tokenizer(_lowercase				,	padding=_lowercase				,	return_tensors=_lowercase				,	**_lowercase)]

									elif isinstance(_lowercase				,	_lowercase) and isinstance(text[0]				,	_lowercase):
												_UpperCAmelCase	 =     []

												# Maximum number of queries across batch
												_UpperCAmelCase	 =     max([len(_lowercase) for t in text])

												# Pad all batch samples to max number of text queries
												for t in text:
															if len(_lowercase) != max_num_queries:
																		_UpperCAmelCase	 =     t + [""" """] * (max_num_queries - len(_lowercase))

															_UpperCAmelCase	 =     self.tokenizer(_lowercase				,	padding=_lowercase				,	return_tensors=_lowercase				,	**_lowercase)
															encodings.append(_lowercase)
									else:
												raise TypeError('Input text should be a string, a list of strings or a nested list of strings')

									if return_tensors == "np":
												_UpperCAmelCase	 =     np.concatenate([encoding['input_ids'] for encoding in encodings]				,	axis=0)
												_UpperCAmelCase	 =     np.concatenate([encoding['attention_mask'] for encoding in encodings]				,	axis=0)

									elif return_tensors == "jax" and is_flax_available():
												import jax.numpy as jnp

												_UpperCAmelCase	 =     jnp.concatenate([encoding['input_ids'] for encoding in encodings]				,	axis=0)
												_UpperCAmelCase	 =     jnp.concatenate([encoding['attention_mask'] for encoding in encodings]				,	axis=0)

									elif return_tensors == "pt" and is_torch_available():
												import torch

												_UpperCAmelCase	 =     torch.cat([encoding['input_ids'] for encoding in encodings]				,	dim=0)
												_UpperCAmelCase	 =     torch.cat([encoding['attention_mask'] for encoding in encodings]				,	dim=0)

									elif return_tensors == "tf" and is_tf_available():
												import tensorflow as tf

												_UpperCAmelCase	 =     tf.stack([encoding['input_ids'] for encoding in encodings]				,	axis=0)
												_UpperCAmelCase	 =     tf.stack([encoding['attention_mask'] for encoding in encodings]				,	axis=0)

									else:
												raise ValueError('Target return tensor type could not be returned')

									_UpperCAmelCase	 =     BatchEncoding()
									_UpperCAmelCase	 =     input_ids
									_UpperCAmelCase	 =     attention_mask

						if query_images is not None:
									_UpperCAmelCase	 =     BatchEncoding()
									_UpperCAmelCase	 =     self.image_processor(
									    _lowercase				,	return_tensors=_lowercase				,	**_lowercase).pixel_values
									_UpperCAmelCase	 =     query_pixel_values

						if images is not None:
									_UpperCAmelCase	 =     self.image_processor(_lowercase				,	return_tensors=_lowercase				,	**_lowercase)

						if text is not None and images is not None:
									_UpperCAmelCase	 =     image_features.pixel_values
									return encoding
						elif query_images is not None and images is not None:
									_UpperCAmelCase	 =     image_features.pixel_values
									return encoding
						elif text is not None or query_images is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**_lowercase)				,	tensor_type=_lowercase)



			def 					_lowerCamelCase (		self     :						Union[str, Any]				,	*A     :						Union[str, Any]				,	**A     :						Tuple)     ->					Optional[int]:



						"""simple docstring"""






						return self.image_processor.post_process(*_lowercase				,	**_lowercase)



			def 					_lowerCamelCase (		self     :						Dict				,	*A     :						Optional[int]				,	**A     :						Optional[int])     ->					str:



						"""simple docstring"""






						return self.image_processor.post_process_object_detection(*_lowercase				,	**_lowercase)



			def 					_lowerCamelCase (		self     :						Any				,	*A     :						Optional[Any]				,	**A     :						Any)     ->					Dict:



						"""simple docstring"""






						return self.image_processor.post_process_image_guided_detection(*_lowercase				,	**_lowercase)



			def 					_lowerCamelCase (		self     :						Any				,	*A     :						str				,	**A     :						Optional[Any])     ->					Tuple:



						"""simple docstring"""






						return self.tokenizer.batch_decode(*_lowercase				,	**_lowercase)



			def 					_lowerCamelCase (		self     :						int				,	*A     :						Any				,	**A     :						Dict)     ->					int:



						"""simple docstring"""






						return self.tokenizer.decode(*_lowercase				,	**_lowercase)



			@property
			def 					_lowerCamelCase (		self     :						str)     ->					int:



						"""simple docstring"""






						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,	_lowercase				,	)
						return self.image_processor_class



			@property
			def 					_lowerCamelCase (		self     :						List[Any])     ->					Tuple:



						"""simple docstring"""






						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,	_lowercase				,	)
						return self.image_processor





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  MgpstrTokenizer
			UpperCamelCase           =  False
			UpperCamelCase           =  {}
			UpperCamelCase           =  False



			def 					_lowerCamelCase (		self     :						int)     ->					List[Any]:



						"""simple docstring"""






						super().setUp()

						# fmt: off
						_UpperCAmelCase	 =     ['[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
						_UpperCAmelCase	 =     dict(zip(A				,	range(len(A))))

						_UpperCAmelCase	 =     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')



			def 					_lowerCamelCase (		self     :						Dict				,	**A     :						List[Any])     ->					Optional[Any]:



						"""simple docstring"""






						return MgpstrTokenizer.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						Optional[int])     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     'tester'
						_UpperCAmelCase	 =     'tester'
						return input_text, output_text



			@unittest.skip('MGP-STR always lower cases letters.')
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					int:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_tokenizers(do_lower_case=A)
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												_UpperCAmelCase	 =     '[SPECIAL_TOKEN]'

												tokenizer.add_special_tokens({'cls_token': special_token})
												_UpperCAmelCase	 =     tokenizer.encode([special_token]				,	add_special_tokens=A)
												self.assertEqual(len(A)				,	1)

												_UpperCAmelCase	 =     tokenizer.decode(A				,	skip_special_tokens=A)
												self.assertTrue(special_token not in decoded)



			def 					_lowerCamelCase (		self     :						Any)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.get_tokenizers()
						for tokenizer in tokenizers:
									with self.subTest(F"{tokenizer.__class__.__name__}"):
												_UpperCAmelCase ,  _UpperCAmelCase	 =     self.get_input_output_texts(A)

												_UpperCAmelCase	 =     tokenizer.tokenize(A)
												_UpperCAmelCase	 =     tokenizer.convert_tokens_to_ids(A)
												_UpperCAmelCase	 =     tokenizer.encode(A				,	add_special_tokens=A)
												self.assertListEqual(A				,	A)

												_UpperCAmelCase	 =     tokenizer.convert_ids_to_tokens(A)
												self.assertNotEqual(len(A)				,	0)
												_UpperCAmelCase	 =     tokenizer.decode(A)
												self.assertIsInstance(A				,	A)

												self.assertEqual(text_a.replace(' '				,	'')				,	A)



			@unittest.skip('MGP-STR tokenizer only handles one sequence.')
			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						pass



			@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer')
			def 					_lowerCamelCase (		self     :						Dict)     ->					Tuple:



						"""simple docstring"""






						pass
