


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast





@require_vision
class        lowercase_				( unittest.TestCase):






							"""simple docstring"""







							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		tempfile.mkdtemp()

													a_					=		CLIPImageProcessor()
													a_					=		BertTokenizer.from_pretrained(
													    """hf-internal-testing/tiny-random-BertModel"""			,    model_input_names=["""input_ids""", """attention_mask"""]		)

													a_					=		GitProcessor(_UpperCAmelCase			,    _UpperCAmelCase		)

													processor.save_pretrained(self.tmpdirname		)


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		).tokenizer


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		).image_processor


							def 						lowercase__     (   self		):







													"""simple docstring"""


													shutil.rmtree(self.tmpdirname		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		[np.random.randint(255			,    size=(3, 30, 400)			,    dtype=np.uinta		)]

													a_					=		[Image.fromarray(np.moveaxis(_UpperCAmelCase			,    0			,    -1		)		) for x in image_inputs]

													return image_inputs


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		GitProcessor(tokenizer=self.get_tokenizer()			,    image_processor=self.get_image_processor()		)
													processor.save_pretrained(self.tmpdirname		)

													a_					=		self.get_tokenizer(bos_token="""(BOS)"""			,    eos_token="""(EOS)"""		)
													a_					=		self.get_image_processor(do_normalize=_UpperCAmelCase			,    padding_value=1.0		)

													a_					=		GitProcessor.from_pretrained(
													    self.tmpdirname			,    bos_token="""(BOS)"""			,    eos_token="""(EOS)"""			,    do_normalize=_UpperCAmelCase			,    padding_value=1.0		)

													self.assertEqual(processor.tokenizer.get_vocab()			,    tokenizer_add_kwargs.get_vocab()		)
													self.assertIsInstance(processor.tokenizer			,    _UpperCAmelCase		)

													self.assertEqual(processor.image_processor.to_json_string()			,    image_processor_add_kwargs.to_json_string()		)
													self.assertIsInstance(processor.image_processor			,    _UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.get_image_processor()
													a_					=		self.get_tokenizer()

													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)

													a_					=		self.prepare_image_inputs()

													a_					=		image_processor(_UpperCAmelCase			,    return_tensors="""np"""		)
													a_					=		processor(images=_UpperCAmelCase			,    return_tensors="""np"""		)

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()			,    input_processor[key].sum()			,    delta=1e-2		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.get_image_processor()
													a_					=		self.get_tokenizer()

													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)

													a_					=		"""lower newer"""

													a_					=		processor(text=_UpperCAmelCase		)

													a_					=		tokenizer(_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]			,    encoded_processor[key]		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.get_image_processor()
													a_					=		self.get_tokenizer()

													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)

													a_					=		"""lower newer"""
													a_					=		self.prepare_image_inputs()

													a_					=		processor(text=_UpperCAmelCase			,    images=_UpperCAmelCase		)

													self.assertListEqual(list(inputs.keys()		)			,    ["""input_ids""", """attention_mask""", """pixel_values"""]		)

													# test if it raises when no input is passed
													with pytest.raises(_UpperCAmelCase		):
																			processor()


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.get_image_processor()
													a_					=		self.get_tokenizer()

													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)

													a_					=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													a_					=		processor.batch_decode(_UpperCAmelCase		)
													a_					=		tokenizer.batch_decode(_UpperCAmelCase		)

													self.assertListEqual(_UpperCAmelCase			,    _UpperCAmelCase		)






							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		self.get_image_processor()
													a_					=		self.get_tokenizer()

													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)

													a_					=		"""lower newer"""
													a_					=		self.prepare_image_inputs()

													a_					=		processor(text=_UpperCAmelCase			,    images=_UpperCAmelCase		)

													# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
													self.assertListEqual(list(inputs.keys()		)			,    ["""input_ids""", """attention_mask""", """pixel_values"""]		)



from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
	import torch





class        lowercase_				( UpperCamelCase__):






							"""simple docstring"""







							snake_case_        =		['''image_processor''', '''tokenizer''']
							snake_case_        =		'''OneFormerImageProcessor'''
							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):







													"""simple docstring"""


													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`."""		)

													a_					=		max_seq_length
													a_					=		task_seq_length

													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)


							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):







													"""simple docstring"""


													if text_list is None:
																			raise ValueError("""tokens cannot be None."""		)

													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)

													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]

													a_					=		[]
													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
																			token_inputs.append(token.unsqueeze(0		)		)

													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
													return token_inputs


							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""		)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""		)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		[task_inputs]

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
																			a_					=		[]
																			for task in task_inputs:
																									a_					=		f"the task is {task}"
																									task_token_inputs.append(_UpperCAmelCase		)
																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)

													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
																			a_					=		encoded_inputs.text_inputs

																			a_					=		[]
																			for texts in texts_list:
																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
																									text_inputs.append(text_input_list.unsqueeze(0		)		)

																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)

													return encoded_inputs


							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):







													"""simple docstring"""


													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""		)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""		)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)

													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
																			a_					=		[task_inputs]

													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
																			a_					=		[]
																			for task in task_inputs:
																									a_					=		f"the task is {task}"
																									task_token_inputs.append(_UpperCAmelCase		)
																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)

													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
																			a_					=		encoded_inputs.text_inputs

																			a_					=		[]
																			for texts in texts_list:
																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
																									text_inputs.append(text_input_list.unsqueeze(0		)		)

																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)

													return encoded_inputs


							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)


							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)






							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):







													"""simple docstring"""


													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)