'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__UpperCAmelCase     							=logging.get_logger(__name__)




class     a__      (   UpperCAmelCase__ ):






     def __init__( self		:		List[Any]  ,				a		:		int  ,				a		:		int  ,				a		:		float  ,				**a		:		str    ):





         """simple docstring"""



         __lowerCamelCase		=  feature_size
         __lowerCamelCase		=  sampling_rate
         __lowerCamelCase		=  padding_value

         __lowerCamelCase		=  kwargs.pop('''padding_side'''  ,				'''right'''    )
         __lowerCamelCase		=  kwargs.pop('''return_attention_mask'''  ,				a    )

         super().__init__(**a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		str  ,				a		:		Union[
             BatchFeature,
             List[BatchFeature],
             Dict[str, BatchFeature],
             Dict[str, List[BatchFeature]],
             List[Dict[str, BatchFeature]],
         ]  ,				a		:		Union[bool, str, PaddingStrategy] = True  ,				a		:		Optional[int] = None  ,				a		:		bool = False  ,				a		:		Optional[int] = None  ,				a		:		Optional[bool] = None  ,				a		:		Optional[Union[str, TensorType]] = None  ,				):





         """simple docstring"""



         if isinstance(a  ,				(list, tuple)    ) and isinstance(processed_features[0]  ,				(dict, BatchFeature)    ):
             __lowerCamelCase		=  {
                 key: [example[key] for example in processed_features] for key in processed_features[0].keys()
             }

         # The model's main input name, usually `input_values`, has be passed for padding
         if self.model_input_names[0] not in processed_features:
             raise ValueError(
                 '''You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'''
                 f""" to this method that includes {self.model_input_names[0]}, but you provided"""
                 f""" {list(processed_features.keys()    )}"""    )

         __lowerCamelCase		=  processed_features[self.model_input_names[0]]
         __lowerCamelCase		=  (
             return_attention_mask if return_attention_mask is not None else self.return_attention_mask
         )

         if len(a    ) == 0:
             if return_attention_mask:
                 __lowerCamelCase		=  []
             return processed_features

         # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
         # and rebuild them afterwards if no return_tensors is specified
         # Note that we lose the specific device the tensor may be on for PyTorch

         __lowerCamelCase		=  required_input[0]
         if isinstance(a  ,				(list, tuple)    ):
             # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
             __lowerCamelCase		=  0
             while len(required_input[index]    ) == 0:
                 index += 1
             if index < len(a    ):
                 __lowerCamelCase		=  required_input[index][0]

         if return_tensors is None:
             if is_tf_tensor(a    ):
                 __lowerCamelCase		=  '''tf'''
             elif is_torch_tensor(a    ):
                 __lowerCamelCase		=  '''pt'''
             elif isinstance(a  ,				(int, float, list, tuple, np.ndarray)    ):
                 __lowerCamelCase		=  '''np'''
             else:
                 raise ValueError(
                     f"""type of {first_element} unknown: {type(a    )}. """
                     '''Should be one of a python, numpy, pytorch or tensorflow object.'''    )

         for key, value in processed_features.items():
             if isinstance(value[0]  ,				(int, float)    ):
                 __lowerCamelCase		=  to_numpy(a    )
             else:
                 __lowerCamelCase		=  [to_numpy(a    ) for v in value]

        # Convert padding_strategy in PaddingStrategy
         __lowerCamelCase		=  self._get_padding_strategies(padding=a  ,				max_length=a    )

         __lowerCamelCase		=  processed_features[self.model_input_names[0]]

         __lowerCamelCase		=  len(a    )
         if not all(len(a    ) == batch_size for v in processed_features.values()    ):
             raise ValueError('''Some items in the output dictionary have a different batch size than others.'''    )

         __lowerCamelCase		=  []
         for i in range(a    ):
             __lowerCamelCase		=  {k: v[i] for k, v in processed_features.items()}
             # truncation
             __lowerCamelCase		=  self._truncate(
                 a  ,				max_length=a  ,				pad_to_multiple_of=a  ,				truncation=a  ,				)
             truncated_inputs.append(a    )

         if padding_strategy == PaddingStrategy.LONGEST:
             # make sure that `max_length` cannot be longer than the longest truncated length
             __lowerCamelCase		=  max(len(input_slice[self.model_input_names[0]]    ) for input_slice in truncated_inputs    )
             __lowerCamelCase		=  PaddingStrategy.MAX_LENGTH

         __lowerCamelCase		=  {}
         for i in range(a    ):
             # padding
             __lowerCamelCase		=  self._pad(
                 truncated_inputs[i]  ,				max_length=a  ,				padding_strategy=a  ,				pad_to_multiple_of=a  ,				return_attention_mask=a  ,				)

             for key, value in outputs.items():
                 if key not in batch_outputs:
                     __lowerCamelCase		=  []
                 if value.dtype is np.dtype(np.floataa    ):
                     __lowerCamelCase		=  value.astype(np.floataa    )
                 batch_outputs[key].append(a    )

         return BatchFeature(a  ,				tensor_type=a    )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple  ,				a		:		Union[Dict[str, np.ndarray], BatchFeature]  ,				a		:		Optional[int] = None  ,				a		:		PaddingStrategy = PaddingStrategy.DO_NOT_PAD  ,				a		:		Optional[int] = None  ,				a		:		Optional[bool] = None  ,				):





         """simple docstring"""



         __lowerCamelCase		=  processed_features[self.model_input_names[0]]

         if padding_strategy == PaddingStrategy.LONGEST:
             __lowerCamelCase		=  len(a    )

         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
             __lowerCamelCase		=  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         __lowerCamelCase		=  padding_strategy != PaddingStrategy.DO_NOT_PAD and len(a    ) < max_length

         if return_attention_mask and "attention_mask" not in processed_features:
             __lowerCamelCase		=  np.ones(len(a    )  ,				dtype=np.intaa    )

         if needs_to_be_padded:
             __lowerCamelCase		=  max_length - len(a    )
             if self.padding_side == "right":
                 if return_attention_mask:
                     __lowerCamelCase		=  np.pad(
                         processed_features['''attention_mask''']  ,				(0, difference)    )
                 __lowerCamelCase		=  ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                 __lowerCamelCase		=  np.pad(
                     a  ,				a  ,				'''constant'''  ,				constant_values=self.padding_value    )
             elif self.padding_side == "left":
                 if return_attention_mask:
                     __lowerCamelCase		=  np.pad(
                         processed_features['''attention_mask''']  ,				(difference, 0)    )
                 __lowerCamelCase		=  ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                 __lowerCamelCase		=  np.pad(
                     a  ,				a  ,				'''constant'''  ,				constant_values=self.padding_value    )
             else:
                 raise ValueError('''Invalid padding strategy:''' + str(self.padding_side    )    )

         return processed_features






     def    SCREAMING_SNAKE_CASE__   ( self		:		int  ,				a		:		Union[Dict[str, np.ndarray], BatchFeature]  ,				a		:		Optional[int] = None  ,				a		:		Optional[int] = None  ,				a		:		Optional[bool] = None  ,				):





         """simple docstring"""



         if not truncation:
             return processed_features
         elif truncation and max_length is None:
             raise ValueError('''When setting ``truncation=True``, make sure that ``max_length`` is defined.'''    )

         __lowerCamelCase		=  processed_features[self.model_input_names[0]]

         # find `max_length` that fits `pad_to_multiple_of`
         if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
             __lowerCamelCase		=  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         __lowerCamelCase		=  len(a    ) > max_length

         if needs_to_be_truncated:
             __lowerCamelCase		=  processed_features[self.model_input_names[0]][:max_length]
             if "attention_mask" in processed_features:
                 __lowerCamelCase		=  processed_features['''attention_mask'''][:max_length]

         return processed_features






     def    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		Any=False  ,				a		:		Any=None    ):





         """simple docstring"""



         if padding is not False:
             if padding is True:
                 __lowerCamelCase		=  PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
             elif not isinstance(a  ,				a    ):
                 __lowerCamelCase		=  PaddingStrategy(a    )
             elif isinstance(a  ,				a    ):
                 __lowerCamelCase		=  padding
         else:
             __lowerCamelCase		=  PaddingStrategy.DO_NOT_PAD

         # Set max length if needed
         if max_length is None:
             if padding_strategy == PaddingStrategy.MAX_LENGTH:
                 raise ValueError(
                     f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""    )

        # Test if we have a padding value
         if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
             raise ValueError(
                 '''Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'''
                 ''' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'''    )

         return padding_strategy







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 CLIPSegProcessor, ViTImageProcessor







@require_vision
class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Any										=tempfile.mkdtemp()

        # fmt: off
        a__	:					List[Any]										=["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
        a__	:					str										=dict(zip(lowerCAmelCase__		,      range(len(lowerCAmelCase__							)							)							)							)
        a__	:					List[Any]										=["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        a__	:					Optional[int]										={"unk_token": "<unk>"}

        a__	:					Optional[Any]										=os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]							)
        a__	:					Tuple										=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__							)							)

        a__	:					Optional[Any]										={
            "do_resize": True,
            "size": 2_0,
            "do_center_crop": True,
            "crop_size": 1_8,
            "do_normalize": True,
            "image_mean": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
            "image_std": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
        }
        a__	:					Dict										=os.path.join(self.tmpdirname		,      lowerCAmelCase__							)
        with open(self.image_processor_file		,      "w"		,      encoding="utf-8"							) as fp:
          json.dump(lowerCAmelCase__		,      lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




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







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



        '''simple docstring'''




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







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        shutil.rmtree(self.tmpdirname							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=[np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

        a__	:					List[Any]										=[Image.fromarray(np.moveaxis(lowerCAmelCase__		,      0		,      -1							)							) for x in image_inputs]

        return image_inputs







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_tokenizer()
        a__	:					int										=self.get_rust_tokenizer()
        a__	:					List[str]										=self.get_image_processor()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_slow.save_pretrained(self.tmpdirname							)
        a__	:					Optional[Any]										=CLIPSegProcessor.from_pretrained(self.tmpdirname		,      use_fast=lowerCAmelCase__							)

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_fast.save_pretrained(self.tmpdirname							)
        a__	:					Dict										=CLIPSegProcessor.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    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					List[str]										=CLIPSegProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
        processor.save_pretrained(self.tmpdirname							)

        a__	:					str										=self.get_tokenizer(bos_token="(BOS)"		,      eos_token="(EOS)"							)
        a__	:					int										=self.get_image_processor(do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        a__	:					Optional[Any]										=CLIPSegProcessor.from_pretrained(
            self.tmpdirname		,      bos_token="(BOS)"		,      eos_token="(EOS)"		,      do_normalize=lowerCAmelCase__		,      padding_value=1.0							)

        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    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					str										=self.get_image_processor()
        a__	:					Optional[int]										=self.get_tokenizer()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					str										=self.prepare_image_inputs()

        a__	:					Any										=image_processor(lowerCAmelCase__		,      return_tensors="np"							)
        a__	:					Optional[int]										=processor(images=lowerCAmelCase__		,      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							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					List[Any]										=self.get_tokenizer()

        a__	:					Optional[int]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Union[str, Any]										="lower newer"

        a__	:					List[str]										=processor(text=lowerCAmelCase__							)

        a__	:					str										=tokenizer(lowerCAmelCase__							)

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







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Any										=self.get_image_processor()
        a__	:					Dict										=self.get_tokenizer()

        a__	:					Union[str, Any]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										="lower newer"
        a__	:					int										=self.prepare_image_inputs()

        a__	:					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    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_image_processor()
        a__	:					Optional[Any]										=self.get_tokenizer()

        a__	:					str										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					int										=self.prepare_image_inputs()
        a__	:					Union[str, Any]										=self.prepare_image_inputs()

        a__	:					Tuple										=processor(images=lowerCAmelCase__		,      visual_prompt=lowerCAmelCase__							)

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

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







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					Any										=self.get_tokenizer()

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        a__	:					Optional[Any]										=processor.batch_decode(lowerCAmelCase__							)
        a__	:					Dict										=tokenizer.batch_decode(lowerCAmelCase__							)

        self.assertListEqual(lowerCAmelCase__		,      lowerCAmelCase__							)






