
"""simple docstring"""
import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_a      :			Dict					=							logging.get_logger(__name__)






class      _UpperCAmelCase				(					_snake_case):
				__lowercase							: Tuple  =					"""AutoTokenizer"""
				__lowercase							: Dict  =					["""tokenizer"""]

				__lowercase							: Optional[Any]  =					{
				    """semantic_prompt""": 1,
				    """coarse_prompt""": 2,
				    """fine_prompt""": 2,
				}
				def __init__(    self						,    snake_case_						,    snake_case_=None     ):
						super().__init__(snake_case_     )

						_snake_case     :						List[str]						=   speaker_embeddings
				@classmethod
				def 					lowerCamelCase__	(    cls						,    snake_case_						,    snake_case_="speaker_embeddings_path.json"						,    **snake_case_     ):

						if speaker_embeddings_dict_path is not None:
								_snake_case     :						List[Any]						=   get_file_from_repo(
								    snake_case_						,    snake_case_						,    subfolder=kwargs.pop("subfolder"						,    snake_case_     )						,    cache_dir=kwargs.pop("cache_dir"						,    snake_case_     )						,    force_download=kwargs.pop("force_download"						,    snake_case_     )						,    proxies=kwargs.pop("proxies"						,    snake_case_     )						,    resume_download=kwargs.pop("resume_download"						,    snake_case_     )						,    local_files_only=kwargs.pop("local_files_only"						,    snake_case_     )						,    use_auth_token=kwargs.pop("use_auth_token"						,    snake_case_     )						,    revision=kwargs.pop("revision"						,    snake_case_     )						,    )
								if speaker_embeddings_path is None:
										logger.warning(
										    F'`{os.path.join(snake_case_						,    snake_case_     )}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'     )
										_snake_case     :						Dict						=   None
								else:
										with open(snake_case_     ) as speaker_embeddings_json:
												_snake_case     :						int						=   json.load(snake_case_     )
						else:
								_snake_case     :						int						=   None

						_snake_case     :						Dict						=   AutoTokenizer.from_pretrained(snake_case_						,    **snake_case_     )

						return cls(tokenizer=snake_case_						,    speaker_embeddings=snake_case_     )
				def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_="speaker_embeddings_path.json"						,    snake_case_="speaker_embeddings"						,    snake_case_ = False						,    **snake_case_						,    ):
						if self.speaker_embeddings is not None:
								os.makedirs(os.path.join(snake_case_						,    snake_case_						,    "v2"     )						,    exist_ok=snake_case_     )

								_snake_case     :						int						=   {}

								_snake_case     :						int						=   save_directory

								for prompt_key in self.speaker_embeddings:
										if prompt_key != "repo_or_path":
												_snake_case     :						Union[str, Any]						=   self._load_voice_preset(snake_case_     )

												_snake_case     :						List[str]						=   {}
												for key in self.speaker_embeddings[prompt_key]:
														np.save(
														    os.path.join(
														        embeddings_dict["repo_or_path"]						,    snake_case_						,    F'{prompt_key}_{key}'     )						,    voice_preset[key]						,    allow_pickle=snake_case_						,    )
														_snake_case     :						Optional[int]						=   os.path.join(snake_case_						,    F'{prompt_key}_{key}.npy'     )

												_snake_case     :						Union[str, Any]						=   tmp_dict

								with open(os.path.join(snake_case_						,    snake_case_     )						,    "w"     ) as fp:
										json.dump(snake_case_						,    snake_case_     )

						super().save_pretrained(snake_case_						,    snake_case_						,    **snake_case_     )
				def 					lowerCamelCase__	(    self						,    snake_case_ = None						,    **snake_case_     ):
						_snake_case     :						Dict						=   self.speaker_embeddings[voice_preset]

						_snake_case     :						Optional[Any]						=   {}
						for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
								if key not in voice_preset_paths:
										raise ValueError(
										    F'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'     )

								_snake_case     :						str						=   get_file_from_repo(
								    self.speaker_embeddings.get("repo_or_path"						,    "/"     )						,    voice_preset_paths[key]						,    subfolder=kwargs.pop("subfolder"						,    snake_case_     )						,    cache_dir=kwargs.pop("cache_dir"						,    snake_case_     )						,    force_download=kwargs.pop("force_download"						,    snake_case_     )						,    proxies=kwargs.pop("proxies"						,    snake_case_     )						,    resume_download=kwargs.pop("resume_download"						,    snake_case_     )						,    local_files_only=kwargs.pop("local_files_only"						,    snake_case_     )						,    use_auth_token=kwargs.pop("use_auth_token"						,    snake_case_     )						,    revision=kwargs.pop("revision"						,    snake_case_     )						,    )
								if path is None:
										raise ValueError(
										    F'`{os.path.join(self.speaker_embeddings.get("repo_or_path"						,    "/"     )						,    voice_preset_paths[key]     )}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'     )

								_snake_case     :						str						=   np.load(snake_case_     )

						return voice_preset_dict
				def 					lowerCamelCase__	(    self						,    snake_case_ = None     ):
						for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
								if key not in voice_preset:
										raise ValueError(F'Voice preset unrecognized, missing {key} as a key.'     )

								if not isinstance(voice_preset[key]						,    np.ndarray     ):
										raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key]     )}D ndarray.'     )

								if len(voice_preset[key].shape     ) != self.preset_shape[key]:
										raise ValueError(F'{key} voice preset must be a {str(self.preset_shape[key]     )}D ndarray.'     )


				def __call__(    self						,    snake_case_=None						,    snake_case_=None						,    snake_case_="pt"						,    snake_case_=2_56						,    snake_case_=False						,    snake_case_=True						,    snake_case_=False						,    **snake_case_						,    ):
						if voice_preset is not None and not isinstance(snake_case_						,    snake_case_     ):
								if (
								    isinstance(snake_case_						,    snake_case_     )
								    and self.speaker_embeddings is not None
								    and voice_preset in self.speaker_embeddings
								):
										_snake_case     :						Any						=   self._load_voice_preset(snake_case_     )

								else:
										if isinstance(snake_case_						,    snake_case_     ) and not voice_preset.endswith(".npz"     ):
												_snake_case     :						Any						=   voice_preset + ".npz"

										_snake_case     :						List[Any]						=   np.load(snake_case_     )

						if voice_preset is not None:
								self._validate_voice_preset_dict(snake_case_						,    **snake_case_     )
								_snake_case     :						List[Any]						=   BatchFeature(data=snake_case_						,    tensor_type=snake_case_     )

						_snake_case     :						Tuple						=   self.tokenizer(
						    snake_case_						,    return_tensors=snake_case_						,    padding="max_length"						,    max_length=snake_case_						,    return_attention_mask=snake_case_						,    return_token_type_ids=snake_case_						,    add_special_tokens=snake_case_						,    **snake_case_						,    )

						if voice_preset is not None:
								_snake_case     :						Union[str, Any]						=   voice_preset

						return encoded_text



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_a      :			Optional[int]					=							{
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			List[Any]					=							["""Pix2StructImageProcessor"""]


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a      :			Dict					=							[
        """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """Pix2StructPreTrainedModel""",
        """Pix2StructForConditionalGeneration""",
        """Pix2StructVisionModel""",
        """Pix2StructTextModel""",
    ]

if TYPE_CHECKING:
    from .configuration_pixastruct import (
        PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        PixaStructConfig,
        PixaStructTextConfig,
        PixaStructVisionConfig,
    )
    from .processing_pixastruct import PixaStructProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_pixastruct import PixaStructImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_pixastruct import (
            PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
            PixaStructForConditionalGeneration,
            PixaStructPreTrainedModel,
            PixaStructTextModel,
            PixaStructVisionModel,
        )

else:
    import sys

    _a      :			Optional[int]					=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

