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						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Any		=["""image_processor""", """tokenizer"""]
     _SCREAMING_SNAKE_CASE     :     Tuple		="""OwlViTImageProcessor"""
     _SCREAMING_SNAKE_CASE     :     List[str]		=("""CLIPTokenizer""", """CLIPTokenizerFast""")



     def __init__(					self				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      **lowerCAmelCase__       ):
            _A=	None
            if "feature_extractor" in kwargs:
                   warnings.warn(
                       'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                       ' instead.'				,      lowerCAmelCase__				,      )
                   _A=	kwargs.pop('feature_extractor'       )

            _A=	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__(lowerCAmelCase__				,      lowerCAmelCase__       )



     def __call__(					self				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__="max_length"				,      lowerCAmelCase__="np"				,      **lowerCAmelCase__       ):

            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(lowerCAmelCase__				,      lowerCAmelCase__       ) or (isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ) and not isinstance(text[0]				,      lowerCAmelCase__       )):
                          _A=	[self.tokenizer(lowerCAmelCase__				,      padding=lowerCAmelCase__				,      return_tensors=lowerCAmelCase__				,      **lowerCAmelCase__       )]

                   elif isinstance(lowerCAmelCase__				,      lowerCAmelCase__       ) and isinstance(text[0]				,      lowerCAmelCase__       ):
                          _A=	[]

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

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

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

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

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

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

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

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

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

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

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

                   _A=	BatchEncoding()
                   _A=	input_ids
                   _A=	attention_mask

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

            if images is not None:
                   _A=	self.image_processor(lowerCAmelCase__				,      return_tensors=lowerCAmelCase__				,      **lowerCAmelCase__       )

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



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return self.image_processor.post_process(*lowerCAmelCase__				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return self.image_processor.post_process_object_detection(*lowerCAmelCase__				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return self.image_processor.post_process_image_guided_detection(*lowerCAmelCase__				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return self.tokenizer.batch_decode(*lowerCAmelCase__				,      **lowerCAmelCase__       )



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return self.tokenizer.decode(*lowerCAmelCase__				,      **lowerCAmelCase__       )



     @property
     def 			a__  (					self       ):
            warnings.warn(
                '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,      lowerCAmelCase__				,      )
            return self.image_processor_class





     @property
     def 			a__  (					self       ):
            warnings.warn(
                '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,      lowerCAmelCase__				,      )
            return self.image_processor
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.nn.Linear(10				,      10       )
            _A=	torch.optim.SGD(model.parameters()				,      0.1       )
            _A=	Accelerator()
            _A=	accelerator.prepare(lowerCAmelCase__       )
            try:
                   pickle.loads(pickle.dumps(lowerCAmelCase__       )       )
            except Exception as e:
                   self.fail(f"Accelerated optimizer pickling failed with {e}"       )
            AcceleratorState._reset_state()