



"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import OwlViTImageProcessor, OwlViTProcessor

@require_vision
class        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					def 		snake_case       (       self							:  Any     )->					Optional[Any]:
								lowerCamelCase__		:		Optional[int]			=tempfile.mkdtemp()

								# fmt: off
								lowerCamelCase__		:		Dict			=['''''', '''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
								# fmt: on
								lowerCamelCase__		:		List[Any]			=dict(zip(lowerCamelCase,  range(len(lowerCamelCase     )     )     )     )
								lowerCamelCase__		:		Union[str, Any]			=['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
								lowerCamelCase__		:		Optional[int]			={'''unk_token''': '''<unk>'''}

								lowerCamelCase__		:		Optional[int]			=os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''vocab_file''']     )
								lowerCamelCase__		:		Optional[int]			=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     )     )

								lowerCamelCase__		:		str			={
								    '''do_resize''': True,
								    '''size''': 20,
								    '''do_center_crop''': True,
								    '''crop_size''': 18,
								    '''do_normalize''': True,
								    '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
								    '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
								}
								lowerCamelCase__		:		str			=os.path.join(self.tmpdirname,  lowerCamelCase     )
								with open(self.image_processor_file,  '''w''',  encoding='''utf-8'''     ) as fp:
											json.dump(lowerCamelCase,  lowerCamelCase     )



					def 		snake_case       (       self							:  List[str],  **lowerCamelCase							:  Union[str, Any]     )->					List[Any]:
								return CLIPTokenizer.from_pretrained(self.tmpdirname,  pad_token='''!''',  **lowerCamelCase     )



					def 		snake_case       (       self							:  str,  **lowerCamelCase							:  Union[str, Any]     )->					List[Any]:
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname,  pad_token='''!''',  **lowerCamelCase     )



					def 		snake_case       (       self							:  Optional[int],  **lowerCamelCase							:  int     )->					str:
								return OwlViTImageProcessor.from_pretrained(self.tmpdirname,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					str:
								shutil.rmtree(self.tmpdirname     )



					def 		snake_case       (       self							:  str     )->					Any:
								lowerCamelCase__		:		List[str]			=[np.random.randint(255,  size=(3, 30, 400),  dtype=np.uinta     )]

								lowerCamelCase__		:		List[str]			=[Image.fromarray(np.moveaxis(lowerCamelCase,  0,  -1     )     ) for x in image_inputs]

								return image_inputs



					def 		snake_case       (       self							:  List[Any]     )->					Dict:
								lowerCamelCase__		:		Dict			=self.get_tokenizer()
								lowerCamelCase__		:		Union[str, Any]			=self.get_rust_tokenizer()
								lowerCamelCase__		:		Optional[int]			=self.get_image_processor()

								lowerCamelCase__		:		List[Any]			=OwlViTProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )
								processor_slow.save_pretrained(self.tmpdirname     )
								lowerCamelCase__		:		Union[str, Any]			=OwlViTProcessor.from_pretrained(self.tmpdirname,  use_fast=lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=OwlViTProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )
								processor_fast.save_pretrained(self.tmpdirname     )
								lowerCamelCase__		:		Union[str, Any]			=OwlViTProcessor.from_pretrained(self.tmpdirname     )

								self.assertEqual(processor_slow.tokenizer.get_vocab(),  tokenizer_slow.get_vocab()     )
								self.assertEqual(processor_fast.tokenizer.get_vocab(),  tokenizer_fast.get_vocab()     )
								self.assertEqual(tokenizer_slow.get_vocab(),  tokenizer_fast.get_vocab()     )
								self.assertIsInstance(processor_slow.tokenizer,  lowerCamelCase     )
								self.assertIsInstance(processor_fast.tokenizer,  lowerCamelCase     )

								self.assertEqual(processor_slow.image_processor.to_json_string(),  image_processor.to_json_string()     )
								self.assertEqual(processor_fast.image_processor.to_json_string(),  image_processor.to_json_string()     )
								self.assertIsInstance(processor_slow.image_processor,  lowerCamelCase     )
								self.assertIsInstance(processor_fast.image_processor,  lowerCamelCase     )



					def 		snake_case       (       self							:  List[Any]     )->					Tuple:
								lowerCamelCase__		:		List[Any]			=OwlViTProcessor(tokenizer=self.get_tokenizer(),  image_processor=self.get_image_processor()     )
								processor.save_pretrained(self.tmpdirname     )

								lowerCamelCase__		:		List[str]			=self.get_tokenizer(bos_token='''(BOS)''',  eos_token='''(EOS)'''     )
								lowerCamelCase__		:		int			=self.get_image_processor(do_normalize=lowerCamelCase     )

								lowerCamelCase__		:		Optional[int]			=OwlViTProcessor.from_pretrained(
								    self.tmpdirname,  bos_token='''(BOS)''',  eos_token='''(EOS)''',  do_normalize=lowerCamelCase     )

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

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



					def 		snake_case       (       self							:  List[Any]     )->					Dict:
								lowerCamelCase__		:		List[str]			=self.get_image_processor()
								lowerCamelCase__		:		Optional[Any]			=self.get_tokenizer()

								lowerCamelCase__		:		List[str]			=OwlViTProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )

								lowerCamelCase__		:		Any			=self.prepare_image_inputs()

								lowerCamelCase__		:		Optional[int]			=image_processor(lowerCamelCase,  return_tensors='''np'''     )
								lowerCamelCase__		:		List[Any]			=processor(images=lowerCamelCase,  return_tensors='''np'''     )

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



					def 		snake_case       (       self							:  Optional[int]     )->					List[Any]:
								lowerCamelCase__		:		List[str]			=self.get_image_processor()
								lowerCamelCase__		:		Tuple			=self.get_tokenizer()

								lowerCamelCase__		:		List[str]			=OwlViTProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )

								lowerCamelCase__		:		Any			='''lower newer'''

								lowerCamelCase__		:		List[str]			=processor(text=lowerCamelCase,  return_tensors='''np'''     )

								lowerCamelCase__		:		int			=tokenizer(lowerCamelCase,  return_tensors='''np'''     )

								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key][0].tolist(),  encoded_processor[key][0].tolist()     )



					def 		snake_case       (       self							:  List[Any]     )->					Dict:
								lowerCamelCase__		:		List[str]			=self.get_image_processor()
								lowerCamelCase__		:		List[Any]			=self.get_tokenizer()

								lowerCamelCase__		:		List[str]			=OwlViTProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )

								lowerCamelCase__		:		Optional[int]			='''lower newer'''
								lowerCamelCase__		:		Tuple			=self.prepare_image_inputs()

								lowerCamelCase__		:		Any			=processor(text=lowerCamelCase,  images=lowerCamelCase     )

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

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



					def 		snake_case       (       self							:  Tuple     )->					Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]			='''google/owlvit-base-patch32'''
								lowerCamelCase__		:		Optional[int]			=OwlViTProcessor.from_pretrained(lowerCamelCase     )

								lowerCamelCase__		:		Optional[int]			=['''cat''', '''nasa badge''']
								lowerCamelCase__		:		Union[str, Any]			=processor(text=lowerCamelCase     )

								lowerCamelCase__		:		List[str]			=16
								self.assertListEqual(list(inputs.keys()     ),  ['''input_ids''', '''attention_mask''']     )
								self.assertEqual(inputs['''input_ids'''].shape,  (2, seq_length)     )

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



					def 		snake_case       (       self							:  Any     )->					str:
								lowerCamelCase__		:		str			='''google/owlvit-base-patch32'''
								lowerCamelCase__		:		List[str]			=OwlViTProcessor.from_pretrained(lowerCamelCase     )

								lowerCamelCase__		:		Optional[int]			=[['''cat''', '''nasa badge'''], ['''person''']]
								lowerCamelCase__		:		List[str]			=processor(text=lowerCamelCase     )

								lowerCamelCase__		:		Tuple			=16
								lowerCamelCase__		:		Optional[int]			=len(lowerCamelCase     )
								lowerCamelCase__		:		Tuple			=max([len(lowerCamelCase     ) for texts in input_texts]     )

								self.assertListEqual(list(inputs.keys()     ),  ['''input_ids''', '''attention_mask''']     )
								self.assertEqual(inputs['''input_ids'''].shape,  (batch_size * num_max_text_queries, seq_length)     )

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



					def 		snake_case       (       self							:  Optional[Any]     )->					Any:
								lowerCamelCase__		:		Any			='''google/owlvit-base-patch32'''
								lowerCamelCase__		:		List[Any]			=OwlViTProcessor.from_pretrained(lowerCamelCase     )

								lowerCamelCase__		:		Optional[int]			=['''cat''', '''nasa badge''']
								lowerCamelCase__		:		Tuple			=processor(text=lowerCamelCase     )

								lowerCamelCase__		:		List[str]			=16
								lowerCamelCase__		:		List[str]			=inputs['''input_ids''']
								lowerCamelCase__		:		str			=[
								    [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								    [4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								]

								self.assertListEqual(list(inputs.keys()     ),  ['''input_ids''', '''attention_mask''']     )
								self.assertEqual(inputs['''input_ids'''].shape,  (2, seq_length)     )
								self.assertListEqual(list(input_ids[0]     ),  predicted_ids[0]     )
								self.assertListEqual(list(input_ids[1]     ),  predicted_ids[1]     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					Union[str, Any]:
								lowerCamelCase__		:		Union[str, Any]			=self.get_image_processor()
								lowerCamelCase__		:		Any			=self.get_tokenizer()

								lowerCamelCase__		:		List[Any]			=OwlViTProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )

								lowerCamelCase__		:		int			=self.prepare_image_inputs()
								lowerCamelCase__		:		Optional[Any]			=self.prepare_image_inputs()

								lowerCamelCase__		:		Union[str, Any]			=processor(images=lowerCamelCase,  query_images=lowerCamelCase     )

								self.assertListEqual(list(inputs.keys()     ),  ['''query_pixel_values''', '''pixel_values''']     )

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




					def 		snake_case       (       self							:  Dict     )->					Any:
								lowerCamelCase__		:		Optional[int]			=self.get_image_processor()
								lowerCamelCase__		:		Optional[Any]			=self.get_tokenizer()

								lowerCamelCase__		:		str			=OwlViTProcessor(tokenizer=lowerCamelCase,  image_processor=lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								lowerCamelCase__		:		Tuple			=processor.batch_decode(lowerCamelCase     )
								lowerCamelCase__		:		Optional[int]			=tokenizer.batch_decode(lowerCamelCase     )

								self.assertListEqual(lowerCamelCase,  lowerCamelCase     )









"""simple docstring"""






from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image


_lowercase : Union[str, Any]      						=						["text", "image", "audio"]






def      snake_case__   (		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			=[]

			for input_type in input_types:
						if input_type == "text":
									inputs.append('''Text input'''	)
						elif input_type == "image":
									inputs.append(
									    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''	)	) / '''000000039769.png'''	).resize((512, 512)	)	)
						elif input_type == "audio":
									inputs.append(torch.ones(3000	)	)
						elif isinstance(__lowerCamelCase							,		__lowerCamelCase	):
									inputs.append(create_inputs(__lowerCamelCase	)	)
						else:
									raise ValueError(f'''Invalid type requested: {input_type}'''	)

			return inputs






def      snake_case__   (		__lowerCamelCase				:    List	):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=[]

			for output in outputs:
						if isinstance(__lowerCamelCase							,		(str, AgentText)	):
									output_types.append('''text'''	)
						elif isinstance(__lowerCamelCase							,		(Image.Image, AgentImage)	):
									output_types.append('''image'''	)
						elif isinstance(__lowerCamelCase							,		(torch.Tensor, AgentAudio)	):
									output_types.append('''audio'''	)
						else:
									raise ValueError(f'''Invalid output: {output}'''	)

			return output_types

@is_tool_test
class        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def 		snake_case       (       self							:  Any     )->					Optional[Any]:
								self.assertTrue(hasattr(self.tool,  '''inputs'''     )     )
								self.assertTrue(hasattr(self.tool,  '''outputs'''     )     )

								lowerCamelCase__		:		Tuple			=self.tool.inputs
								for _input in inputs:
											if isinstance(_input,  lowerCamelCase     ):
														for __input in _input:
																	self.assertTrue(__input in authorized_types     )
											else:
														self.assertTrue(_input in authorized_types     )

								lowerCamelCase__		:		Optional[Any]			=self.tool.outputs
								for _output in outputs:
											self.assertTrue(_output in authorized_types     )



					def 		snake_case       (       self							:  Optional[int]     )->					Union[str, Any]:
								lowerCamelCase__		:		Optional[int]			=create_inputs(self.tool.inputs     )
								lowerCamelCase__		:		List[Any]			=self.tool(*lowerCamelCase     )

								# There is a single output
								if len(self.tool.outputs     ) == 1:
											lowerCamelCase__		:		Optional[int]			=[outputs]

								self.assertListEqual(output_types(lowerCamelCase     ),  self.tool.outputs     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[str]:
								self.assertTrue(hasattr(self.tool,  '''description'''     )     )
								self.assertTrue(hasattr(self.tool,  '''default_checkpoint'''     )     )
								self.assertTrue(self.tool.description.startswith('''This is a tool that'''     )     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					str:
								lowerCamelCase__		:		List[str]			=create_inputs(self.tool.inputs     )
								lowerCamelCase__		:		Optional[Any]			=self.tool(*lowerCamelCase     )

								if not isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		Any			=[outputs]

								self.assertEqual(len(lowerCamelCase     ),  len(self.tool.outputs     )     )

								for output, output_type in zip(lowerCamelCase,  self.tool.outputs     ):
											lowerCamelCase__		:		List[Any]			=AGENT_TYPE_MAPPING[output_type]
											self.assertTrue(isinstance(lowerCamelCase,  lowerCamelCase     )     )




					def 		snake_case       (       self							:  Optional[Any]     )->					List[Any]:
								lowerCamelCase__		:		Optional[Any]			=create_inputs(self.tool.inputs     )

								lowerCamelCase__		:		List[str]			=[]

								for _input, input_type in zip(lowerCamelCase,  self.tool.inputs     ):
											if isinstance(lowerCamelCase,  lowerCamelCase     ):
														_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input     ) for _input_type in input_type]     )
											else:
														_inputs.append(AGENT_TYPE_MAPPING[input_type](_input     )     )

        # Should not raise an error
								lowerCamelCase__		:		Any			=self.tool(*lowerCamelCase     )

								if not isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		Optional[int]			=[outputs]

								self.assertEqual(len(lowerCamelCase     ),  len(self.tool.outputs     )     )




