




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    (		_lowerCamelCase						):






   '''simple docstring'''





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

   def __init__( self			:   List[str]       ,						lowerCamelCase_			:   int=None       ,						lowerCamelCase_			:   List[str]=None       ,						**lowerCamelCase_			:   Dict  )						->   List[Any]:
        __magic_name__							:	int    							=							None
        if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''       ,						__a       ,						)
             __magic_name__							:	Optional[Any]    							=							kwargs.pop('''feature_extractor'''  )

        __magic_name__							:	Union[str, 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__(__a       ,						__a  )

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

        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(__a       ,						__a  ) or (isinstance(__a       ,						__a  ) and not isinstance(text[0]       ,						__a  )):
                  __magic_name__							:	Tuple    							=							[self.tokenizer(__a       ,						padding=__a       ,						return_tensors=__a       ,						**__a  )]

             elif isinstance(__a       ,						__a  ) and isinstance(text[0]       ,						__a  ):
                  __magic_name__							:	List[Any]    							=							[]

                  # Maximum number of queries across batch
                  __magic_name__							:	List[Any]    							=							max([len(__a  ) for t in text]  )

                  # Pad all batch samples to max number of text queries
                  for t in text:
                       if len(__a  ) != max_num_queries:
                            __magic_name__							:	Optional[Any]    							=							t + [''' '''] * (max_num_queries - len(__a  ))

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

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

                  __magic_name__							:	Optional[int]    							=							jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]       ,						axis=0  )
                  __magic_name__							:	List[str]    							=							jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]       ,						axis=0  )

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

                  __magic_name__							:	int    							=							torch.cat([encoding['''input_ids'''] for encoding in encodings]       ,						dim=0  )
                  __magic_name__							:	Optional[int]    							=							torch.cat([encoding['''attention_mask'''] for encoding in encodings]       ,						dim=0  )

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

                  __magic_name__							:	int    							=							tf.stack([encoding['''input_ids'''] for encoding in encodings]       ,						axis=0  )
                  __magic_name__							:	List[Any]    							=							tf.stack([encoding['''attention_mask'''] for encoding in encodings]       ,						axis=0  )

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

             __magic_name__							:	str    							=							BatchEncoding()
             __magic_name__							:	str    							=							input_ids
             __magic_name__							:	Optional[Any]    							=							attention_mask

        if query_images is not None:
             __magic_name__							:	int    							=							BatchEncoding()
             __magic_name__							:	Optional[Any]    							=							self.image_processor(
                 __a       ,						return_tensors=__a       ,						**__a  ).pixel_values
             __magic_name__							:	Union[str, Any]    							=							query_pixel_values

        if images is not None:
             __magic_name__							:	Dict    							=							self.image_processor(__a       ,						return_tensors=__a       ,						**__a  )

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

   def 							UpperCAmelCase__       ( self			:   str       ,						*lowerCamelCase_			:   Dict       ,						**lowerCamelCase_			:   List[str]  )						->   str:
        return self.image_processor.post_process(*__a       ,						**__a  )

   def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						*lowerCamelCase_			:   Tuple       ,						**lowerCamelCase_			:   str  )						->   Dict:
        return self.image_processor.post_process_object_detection(*__a       ,						**__a  )

   def 							UpperCAmelCase__       ( self			:   str       ,						*lowerCamelCase_			:   Dict       ,						**lowerCamelCase_			:   Optional[int]  )						->   Optional[Any]:
        return self.image_processor.post_process_image_guided_detection(*__a       ,						**__a  )

   def 							UpperCAmelCase__       ( self			:   Tuple       ,						*lowerCamelCase_			:   Dict       ,						**lowerCamelCase_			:   Any  )						->   Optional[int]:
        return self.tokenizer.batch_decode(*__a       ,						**__a  )

   def 							UpperCAmelCase__       ( self			:   Any       ,						*lowerCamelCase_			:   Union[str, Any]       ,						**lowerCamelCase_			:   Optional[int]  )						->   Optional[Any]:
        return self.tokenizer.decode(*__a       ,						**__a  )

   @property
   def 							UpperCAmelCase__       ( self			:   int  )						->   str:
        warnings.warn(
            '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,						__a       ,						)
        return self.image_processor_class


   @property
   def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Optional[Any]:
        warnings.warn(
            '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       ,						__a       ,						)
        return self.image_processor









import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command


class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=inspect.getfile(accelerate.test_utils						)
			UpperCamelCase__								=os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['''scripts''', '''test_cli.py''']						)

			UpperCamelCase__								=['''accelerate''', '''launch''']
			UpperCamelCase__								=Path.home() / '''.cache/huggingface/accelerate'''
			UpperCamelCase__								='''default_config.yaml'''
			UpperCamelCase__								=config_folder / config_file
			UpperCamelCase__								=config_folder / '''_default_config.yaml'''

			UpperCamelCase__								=Path('''tests/test_configs'''						)

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   Optional[int]  )						->   int:
								if cls.config_path.is_file():
													cls.config_path.rename(cls.changed_path  )

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   Dict  )						->   List[str]:
								if cls.changed_path.is_file():
													cls.changed_path.rename(cls.config_path  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[Any]:
								__magic_name__							:	Dict    							=							self.base_cmd
								if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
													cmd += ["--multi_gpu"]
								execute_subprocess_async(cmd + [self.test_file_path]       ,						env=os.environ.copy()  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   Optional[int]:
								for config in sorted(self.test_config_path.glob('''**/*.yaml'''  )  ):
													with self.subTest(config_file=lowerCamelCase_  ):
																		execute_subprocess_async(
																		    self.base_cmd + ['''--config_file''', str(lowerCamelCase_  ), self.test_file_path]       ,						env=os.environ.copy()  )


			def 							UpperCAmelCase__       ( self			:   int  )						->   Union[str, Any]:
								execute_subprocess_async(['''accelerate''', '''test''']       ,						env=os.environ.copy()  )






class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								='''test-tpu'''
			UpperCamelCase__								='''us-central1-a'''
			UpperCamelCase__								='''ls'''
			UpperCamelCase__								=['''accelerate''', '''tpu-config''']
			UpperCamelCase__								='''cd /usr/share'''
			UpperCamelCase__								='''tests/test_samples/test_command_file.sh'''
			UpperCamelCase__								='''Running gcloud compute tpus tpu-vm ssh'''

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Optional[Any]:
								__magic_name__							:	str    							=							run_command(
								    self.cmd
								    + ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug''']       ,						return_stdout=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   int:
								__magic_name__							:	int    							=							run_command(
								    self.cmd
								    + [
								        '''--config_file''',
								        '''tests/test_configs/0_12_0.yaml''',
								        '''--command''',
								        self.command,
								        '''--tpu_zone''',
								        self.tpu_zone,
								        '''--tpu_name''',
								        self.tpu_name,
								        '''--debug''',
								    ]       ,						return_stdout=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Tuple:
								__magic_name__							:	Any    							=							run_command(
								    self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug''']       ,						return_stdout=lowerCamelCase_  )
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Tuple:
								__magic_name__							:	Tuple    							=							run_command(
								    self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug''']       ,						return_stdout=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Dict:
								__magic_name__							:	str    							=							run_command(
								    self.cmd
								    + [
								        '''--config_file''',
								        '''tests/test_configs/latest.yaml''',
								        '''--command''',
								        self.command,
								        '''--command''',
								        '''echo "Hello World"''',
								        '''--debug''',
								    ]       ,						return_stdout=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							run_command(
								    self.cmd
								    + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug''']       ,						return_stdout=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   int  )						->   List[Any]:
								__magic_name__							:	List[Any]    							=							run_command(
								    self.cmd
								    + [
								        '''--config_file''',
								        '''tests/test_configs/0_12_0.yaml''',
								        '''--command_file''',
								        self.command_file,
								        '''--tpu_zone''',
								        self.tpu_zone,
								        '''--tpu_name''',
								        self.tpu_name,
								        '''--debug''',
								    ]       ,						return_stdout=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   str  )						->   Dict:
								__magic_name__							:	Tuple    							=							run_command(
								    self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug''']       ,						return_stdout=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all'''       ,						lowerCamelCase_       ,						)


			def 							UpperCAmelCase__       ( self			:   Any  )						->   Tuple:
								__magic_name__							:	Tuple    							=							run_command(
								    self.cmd
								    + [
								        '''--config_file''',
								        '''tests/test_configs/latest.yaml''',
								        '''--install_accelerate''',
								        '''--accelerate_version''',
								        '''12.0.0''',
								        '''--debug''',
								    ]       ,						return_stdout=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all'''       ,						lowerCamelCase_       ,						)



