






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    A_	(__lowerCAmelCase							):
							"""simple docstring"""




							a__   		=	['''image_processor''', '''tokenizer''']
							a__   		=	'''OwlViTImageProcessor'''
							a__   		=	('''CLIPTokenizer''', '''CLIPTokenizerFast''')

							def __init__(		self			:str       ,							lowerCAmelCase__			:Dict=None       ,							lowerCAmelCase__			:Any=None       ,							**lowerCAmelCase__			:List[Any]				)				->  Any:


										'''simple docstring'''




										snake_case_				:					Dict   =       None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
													    " instead."       ,							_UpperCamelCase       ,							)
													snake_case_				:					Optional[int]   =       kwargs.pop("feature_extractor"				)

										snake_case_				:					List[Any]   =       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__(_UpperCamelCase       ,							_UpperCamelCase				)

							def __call__(		self			:int       ,							lowerCAmelCase__			:Union[str, Any]=None       ,							lowerCAmelCase__			:Optional[int]=None       ,							lowerCAmelCase__			:Dict=None       ,							lowerCAmelCase__			:Dict="max_length"       ,							lowerCAmelCase__			:List[str]="np"       ,							**lowerCAmelCase__			:Optional[Any]				)				->  List[Any]:


										'''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(_UpperCamelCase       ,							_UpperCamelCase				) or (isinstance(_UpperCamelCase       ,							_UpperCamelCase				) and not isinstance(text[0]       ,							_UpperCamelCase				)):
																snake_case_				:					int   =       [self.tokenizer(_UpperCamelCase       ,							padding=_UpperCamelCase       ,							return_tensors=_UpperCamelCase       ,							**_UpperCamelCase				)]

													elif isinstance(_UpperCamelCase       ,							_UpperCamelCase				) and isinstance(text[0]       ,							_UpperCamelCase				):
																snake_case_				:					Union[str, Any]   =       []

																# Maximum number of queries across batch
																snake_case_				:					List[str]   =       max([len(_UpperCamelCase				) for t in text]				)

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

																			snake_case_				:					int   =       self.tokenizer(_UpperCamelCase       ,							padding=_UpperCamelCase       ,							return_tensors=_UpperCamelCase       ,							**_UpperCamelCase				)
																			encodings.append(_UpperCamelCase				)
													else:
																raise TypeError("Input text should be a string, a list of strings or a nested list of strings"				)

													if return_tensors == "np":
																snake_case_				:					List[str]   =       np.concatenate([encoding["input_ids"] for encoding in encodings]       ,							axis=0				)
																snake_case_				:					Union[str, Any]   =       np.concatenate([encoding["attention_mask"] for encoding in encodings]       ,							axis=0				)

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

																snake_case_				:					str   =       jnp.concatenate([encoding["input_ids"] for encoding in encodings]       ,							axis=0				)
																snake_case_				:					Optional[Any]   =       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]       ,							axis=0				)

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

																snake_case_				:					str   =       torch.cat([encoding["input_ids"] for encoding in encodings]       ,							dim=0				)
																snake_case_				:					List[Any]   =       torch.cat([encoding["attention_mask"] for encoding in encodings]       ,							dim=0				)

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

																snake_case_				:					List[str]   =       tf.stack([encoding["input_ids"] for encoding in encodings]       ,							axis=0				)
																snake_case_				:					List[str]   =       tf.stack([encoding["attention_mask"] for encoding in encodings]       ,							axis=0				)

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

													snake_case_				:					Any   =       BatchEncoding()
													snake_case_				:					str   =       input_ids
													snake_case_				:					Union[str, Any]   =       attention_mask

										if query_images is not None:
													snake_case_				:					Optional[Any]   =       BatchEncoding()
													snake_case_				:					int   =       self.image_processor(
													    _UpperCamelCase       ,							return_tensors=_UpperCamelCase       ,							**_UpperCamelCase				).pixel_values
													snake_case_				:					Tuple   =       query_pixel_values

										if images is not None:
													snake_case_				:					Any   =       self.image_processor(_UpperCamelCase       ,							return_tensors=_UpperCamelCase       ,							**_UpperCamelCase				)

										if text is not None and images is not None:
													snake_case_				:					List[str]   =       image_features.pixel_values
													return encoding
										elif query_images is not None and images is not None:
													snake_case_				:					List[str]   =       image_features.pixel_values
													return encoding
										elif text is not None or query_images is not None:
													return encoding
										else:
													return BatchEncoding(data=dict(**_UpperCamelCase				)       ,							tensor_type=_UpperCamelCase				)

							def 							_A	(		self			:Union[str, Any]       ,							*lowerCAmelCase__			:int       ,							**lowerCAmelCase__			:int				)				->  Tuple:


										'''simple docstring'''




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

							def 							_A	(		self			:Tuple       ,							*lowerCAmelCase__			:Optional[int]       ,							**lowerCAmelCase__			:List[Any]				)				->  Union[str, Any]:


										'''simple docstring'''




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

							def 							_A	(		self			:int       ,							*lowerCAmelCase__			:Dict       ,							**lowerCAmelCase__			:str				)				->  int:


										'''simple docstring'''




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

							def 							_A	(		self			:Optional[int]       ,							*lowerCAmelCase__			:Any       ,							**lowerCAmelCase__			:str				)				->  List[Any]:


										'''simple docstring'''




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

							def 							_A	(		self			:Any       ,							*lowerCAmelCase__			:Tuple       ,							**lowerCAmelCase__			:List[Any]				)				->  Any:


										'''simple docstring'''




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

							@property
							def 							_A	(		self			:Optional[Any]				)				->  List[Any]:


										'''simple docstring'''




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






							@property
							def 							_A	(		self			:Dict				)				->  List[Any]:


										'''simple docstring'''




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












'''simple docstring'''




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    A_	(a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	MgpstrTokenizer
							a__   		=	False
							a__   		=	{}
							a__   		=	False

							def 							_A	(		self			:List[str]				)				->  List[str]:


										'''simple docstring'''




										super().setUp()

										# fmt: off
										snake_case_				:					Dict   =       ["[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
										snake_case_				:					List[str]   =       dict(zip(lowerCAmelCase__       ,							range(len(lowerCAmelCase__				)				)				)				)

										snake_case_				:					Tuple   =       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(lowerCAmelCase__				) + "\n"				)

							def 							_A	(		self			:Optional[Any]       ,							**lowerCAmelCase__			:Optional[Any]				)				->  Dict:


										'''simple docstring'''




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

							def 							_A	(		self			:Dict       ,							lowerCAmelCase__			:Any				)				->  str:


										'''simple docstring'''




										snake_case_				:					Dict   =       "tester"
										snake_case_				:					Tuple   =       "tester"
										return input_text, output_text

							@unittest.skip("MGP-STR always lower cases letters."				)
							def 							_A	(		self			:Dict				)				->  str:


										'''simple docstring'''




										pass

							def 							_A	(		self			:Tuple				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       self.get_tokenizers(do_lower_case=lowerCAmelCase__				)
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
																snake_case_				:					Tuple   =       "[SPECIAL_TOKEN]"

																tokenizer.add_special_tokens({"cls_token": special_token}				)
																snake_case_				:					str   =       tokenizer.encode([special_token]       ,							add_special_tokens=lowerCAmelCase__				)
																self.assertEqual(len(lowerCAmelCase__				)       ,							1				)

																snake_case_				:					Tuple   =       tokenizer.decode(lowerCAmelCase__       ,							skip_special_tokens=lowerCAmelCase__				)
																self.assertTrue(special_token not in decoded				)

							def 							_A	(		self			:int				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
																snake_case_,	snake_case_				:					str   =       self.get_input_output_texts(lowerCAmelCase__				)

																snake_case_				:					Union[str, Any]   =       tokenizer.tokenize(lowerCAmelCase__				)
																snake_case_				:					List[Any]   =       tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
																snake_case_				:					Dict   =       tokenizer.encode(lowerCAmelCase__       ,							add_special_tokens=lowerCAmelCase__				)
																self.assertListEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)

																snake_case_				:					List[str]   =       tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
																self.assertNotEqual(len(lowerCAmelCase__				)       ,							0				)
																snake_case_				:					List[str]   =       tokenizer.decode(lowerCAmelCase__				)
																self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)

																self.assertEqual(text_a.replace(" "       ,							""				)       ,							lowerCAmelCase__				)

							@unittest.skip("MGP-STR tokenizer only handles one sequence."				)
							def 							_A	(		self			:Union[str, Any]				)				->  Any:


										'''simple docstring'''




										pass






							@unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"				)
							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										pass




