


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     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    ["image_processor", "tokenizer"]
       A__      		=    "OwlViTImageProcessor"
       A__      		=    ("CLIPTokenizer", "CLIPTokenizerFast")





       def __init__(     self							:    str      ,   __lowerCamelCase							:    Any=None      ,   __lowerCamelCase							:    Tuple=None      ,   **__lowerCamelCase							:    Tuple ):


        '''simple docstring'''



        lowerCamelCase__	:       Any					=						None
        if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."      ,   __UpperCamelCase      ,   )
         lowerCamelCase__	:       Optional[Any]					=						kwargs.pop("feature_extractor" )

        lowerCamelCase__	:       str					=						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__(__UpperCamelCase      ,   __UpperCamelCase )





       def __call__(     self							:    Tuple      ,   __lowerCamelCase							:    Any=None      ,   __lowerCamelCase							:    Optional[int]=None      ,   __lowerCamelCase							:    Dict=None      ,   __lowerCamelCase							:    Optional[int]="max_length"      ,   __lowerCamelCase							:    List[Any]="np"      ,   **__lowerCamelCase							:    Union[str, Any] ):


        '''simple docstring'''



        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(__UpperCamelCase      ,   __UpperCamelCase ) or (isinstance(__UpperCamelCase      ,   __UpperCamelCase ) and not isinstance(text[0]      ,   __UpperCamelCase )):
          lowerCamelCase__	:       Optional[Any]					=						[self.tokenizer(__UpperCamelCase      ,   padding=__UpperCamelCase      ,   return_tensors=__UpperCamelCase      ,   **__UpperCamelCase )]

         elif isinstance(__UpperCamelCase      ,   __UpperCamelCase ) and isinstance(text[0]      ,   __UpperCamelCase ):
          lowerCamelCase__	:       Union[str, Any]					=						[]

          # Maximum number of queries across batch
          lowerCamelCase__	:       Optional[int]					=						max([len(__UpperCamelCase ) for t in text] )

          # Pad all batch samples to max number of text queries
          for t in text:
           if len(__UpperCamelCase ) != max_num_queries:
            lowerCamelCase__	:       int					=						t + [" "] * (max_num_queries - len(__UpperCamelCase ))

           lowerCamelCase__	:       Union[str, Any]					=						self.tokenizer(__UpperCamelCase      ,   padding=__UpperCamelCase      ,   return_tensors=__UpperCamelCase      ,   **__UpperCamelCase )
           encodings.append(__UpperCamelCase )
         else:
          raise TypeError("Input text should be a string, a list of strings or a nested list of strings" )

         if return_tensors == "np":
          lowerCamelCase__	:       Optional[int]					=						np.concatenate([encoding["input_ids"] for encoding in encodings]      ,   axis=0 )
          lowerCamelCase__	:       Optional[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

          lowerCamelCase__	:       Union[str, Any]					=						jnp.concatenate([encoding["input_ids"] for encoding in encodings]      ,   axis=0 )
          lowerCamelCase__	:       List[Any]					=						jnp.concatenate([encoding["attention_mask"] for encoding in encodings]      ,   axis=0 )

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

          lowerCamelCase__	:       Optional[Any]					=						torch.cat([encoding["input_ids"] for encoding in encodings]      ,   dim=0 )
          lowerCamelCase__	:       List[str]					=						torch.cat([encoding["attention_mask"] for encoding in encodings]      ,   dim=0 )

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

          lowerCamelCase__	:       List[Any]					=						tf.stack([encoding["input_ids"] for encoding in encodings]      ,   axis=0 )
          lowerCamelCase__	:       Tuple					=						tf.stack([encoding["attention_mask"] for encoding in encodings]      ,   axis=0 )

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

         lowerCamelCase__	:       List[str]					=						BatchEncoding()
         lowerCamelCase__	:       Dict					=						input_ids
         lowerCamelCase__	:       Optional[Any]					=						attention_mask

        if query_images is not None:
         lowerCamelCase__	:       Union[str, Any]					=						BatchEncoding()
         lowerCamelCase__	:       Dict					=						self.image_processor(
             __UpperCamelCase      ,   return_tensors=__UpperCamelCase      ,   **__UpperCamelCase ).pixel_values
         lowerCamelCase__	:       Union[str, Any]					=						query_pixel_values

        if images is not None:
         lowerCamelCase__	:       Optional[int]					=						self.image_processor(__UpperCamelCase      ,   return_tensors=__UpperCamelCase      ,   **__UpperCamelCase )

        if text is not None and images is not None:
         lowerCamelCase__	:       Optional[int]					=						image_features.pixel_values
         return encoding
        elif query_images is not None and images is not None:
         lowerCamelCase__	:       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(**__UpperCamelCase )      ,   tensor_type=__UpperCamelCase )





       def 		lowerCAmelCase  (     self							:    List[str]      ,   *__lowerCamelCase							:    Union[str, Any]      ,   **__lowerCamelCase							:    Optional[Any] ):


        '''simple docstring'''



        return self.image_processor.post_process(*__UpperCamelCase      ,   **__UpperCamelCase )





       def 		lowerCAmelCase  (     self							:    List[str]      ,   *__lowerCamelCase							:    List[str]      ,   **__lowerCamelCase							:    Optional[int] ):


        '''simple docstring'''



        return self.image_processor.post_process_object_detection(*__UpperCamelCase      ,   **__UpperCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   *__lowerCamelCase							:    List[Any]      ,   **__lowerCamelCase							:    Dict ):


        '''simple docstring'''



        return self.image_processor.post_process_image_guided_detection(*__UpperCamelCase      ,   **__UpperCamelCase )





       def 		lowerCAmelCase  (     self							:    str      ,   *__lowerCamelCase							:    int      ,   **__lowerCamelCase							:    Optional[int] ):


        '''simple docstring'''



        return self.tokenizer.batch_decode(*__UpperCamelCase      ,   **__UpperCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   *__lowerCamelCase							:    int      ,   **__lowerCamelCase							:    Optional[int] ):


        '''simple docstring'''



        return self.tokenizer.decode(*__UpperCamelCase      ,   **__UpperCamelCase )





       @property
       def 		lowerCAmelCase  (     self							:    Any ):


        '''simple docstring'''



        warnings.warn(
            "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,   __UpperCamelCase      ,   )
        return self.image_processor_class





       @property
       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


        '''simple docstring'''



        warnings.warn(
            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,   __UpperCamelCase      ,   )
        return self.image_processor






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A   :					Optional[int]	=      {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[Any]	=      ["SpeechT5Tokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[str]	=      [
        "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SpeechT5ForSpeechToText",
        "SpeechT5ForSpeechToSpeech",
        "SpeechT5ForTextToSpeech",
        "SpeechT5Model",
        "SpeechT5PreTrainedModel",
        "SpeechT5HifiGan",
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

    A   :					Any	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


