


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     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any      =  ["image_processor", "tokenizer"]
    UpperCAmelCase__						:		Dict      =  "OwlViTImageProcessor"
    UpperCAmelCase__						:		Union[str, Any]      =  ("CLIPTokenizer", "CLIPTokenizerFast")






    def __init__(			self					,   A_=None					,   A_=None					,   **A_    )  ->       Tuple:
         __UpperCamelCase    							=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_					,   )
              __UpperCamelCase    							=kwargs.pop('feature_extractor'    )

         __UpperCamelCase    							=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					,   A_=None					,   A_=None					,   A_=None					,   A_="max_length"					,   A_="np"					,   **A_    )  ->       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_    )):
                   __UpperCamelCase    							=[self.tokenizer(A_					,   padding=A_					,   return_tensors=A_					,   **A_    )]

              elif isinstance(A_					,   A_    ) and isinstance(text[0]					,   A_    ):
                   __UpperCamelCase    							=[]

                   # Maximum number of queries across batch
                   __UpperCamelCase    							=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:
                             __UpperCamelCase    							=t + [' '] * (max_num_queries - len(A_    ))

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

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

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

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

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

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

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

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

              __UpperCamelCase    							=BatchEncoding()
              __UpperCamelCase    							=input_ids
              __UpperCamelCase    							=attention_mask

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

         if images is not None:
              __UpperCamelCase    							=self.image_processor(A_					,   return_tensors=A_					,   **A_    )

         if text is not None and images is not None:
              __UpperCamelCase    							=image_features.pixel_values
              return encoding
         elif query_images is not None and images is not None:
              __UpperCamelCase    							=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 _a					(			self					,   *A_					,   **A_    )  ->       Union[str, Any]:
         return self.image_processor.post_process(*A_					,   **A_    )






    def _a					(			self					,   *A_					,   **A_    )  ->       Optional[Any]:
         return self.image_processor.post_process_object_detection(*A_					,   **A_    )






    def _a					(			self					,   *A_					,   **A_    )  ->       Optional[int]:
         return self.image_processor.post_process_image_guided_detection(*A_					,   **A_    )






    def _a					(			self					,   *A_					,   **A_    )  ->       str:
         return self.tokenizer.batch_decode(*A_					,   **A_    )






    def _a					(			self					,   *A_					,   **A_    )  ->       Any:
         return self.tokenizer.decode(*A_					,   **A_    )






    @property
    def _a					(			self    )  ->       Dict:
         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 _a					(			self    )  ->       Tuple:
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,   A_					,   )
         return self.image_processor









from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def _a					(			self					,   A_    )  ->       float:
         return 0.0

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					np.ndarray			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     __UpperCamelCase    							=max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

     __UpperCamelCase    							=[0] * (samplerate - size)  # zero-padding
     outputs += filler
     __UpperCamelCase    							=np.abs(np.fft.fft(SCREAMING_SNAKE_CASE__				)				)
     __UpperCamelCase    							=20 * np.logaa(SCREAMING_SNAKE_CASE__				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24			,     samplerate / 2 - 1				)
     plt.xlabel('Frequency (Hz)'				)
     plt.xscale('log'				)

     # Display within reasonable bounds
     __UpperCamelCase    							=get_bounds(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     plt.ylim(max([-80, bounds[0]]				)			,     min([80, bounds[1]]				)				)
     plt.ylabel('Gain (dB)'				)

     plt.plot(SCREAMING_SNAKE_CASE__				)
     plt.show()





def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					FilterType			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=5_12
     __UpperCamelCase    							=[1] + [0] * (size - 1)
     __UpperCamelCase    							=[filter_type.process(SCREAMING_SNAKE_CASE__				) for item in inputs]

     __UpperCamelCase    							=[0] * (samplerate - size)  # zero-padding
     outputs += filler
     __UpperCamelCase    							=np.angle(np.fft.fft(SCREAMING_SNAKE_CASE__				)				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24			,     samplerate / 2 - 1				)
     plt.xlabel('Frequency (Hz)'				)
     plt.xscale('log'				)

     plt.ylim(-2 * pi			,     2 * pi				)
     plt.ylabel('Phase shift (Radians)'				)
     plt.plot(np.unwrap(SCREAMING_SNAKE_CASE__			,     -2 * pi				)				)
     plt.show()





