

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


_UpperCamelCase 			=		logging.get_logger(__name__)







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """AutoTokenizer"""
 _UpperCamelCase			  =  ["""tokenizer"""]

 _UpperCamelCase			  =  {
     """semantic_prompt""": 1,
     """coarse_prompt""": 2,
     """fine_prompt""": 2,
 }
 def __init__(							self      ,							A_      ,							A_=None					)					->int:



       '''simple docstring'''





       super().__init__(A_					)

       __lowerCAmelCase					:						str					=					speaker_embeddings
 @classmethod
 def 	UpperCamelCase__  (							cls      ,							A_      ,							A_="speaker_embeddings_path.json"      ,							**A_					)					->Optional[int]:



       '''simple docstring'''





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

       __lowerCAmelCase					:						str					=					AutoTokenizer.from_pretrained(A_      ,							**A_					)

       return cls(tokenizer=A_      ,							speaker_embeddings=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_="speaker_embeddings_path.json"      ,							A_="speaker_embeddings"      ,							A_ = False      ,							**A_      ,							)					->Dict:



       '''simple docstring'''





       if self.speaker_embeddings is not None:
             os.makedirs(os.path.join(A_      ,							A_      ,							'''v2'''					)      ,							exist_ok=A_					)

             __lowerCAmelCase					:						Any					=					{}

             __lowerCAmelCase					:						Tuple					=					save_directory

             for prompt_key in self.speaker_embeddings:
                   if prompt_key != "repo_or_path":
                         __lowerCAmelCase					:						Optional[Any]					=					self._load_voice_preset(A_					)

                         __lowerCAmelCase					:						List[Any]					=					{}
                         for key in self.speaker_embeddings[prompt_key]:
                               np.save(
                                   os.path.join(
                                       embeddings_dict['''repo_or_path''']      ,							A_      ,							f"""{prompt_key}_{key}"""					)      ,							voice_preset[key]      ,							allow_pickle=A_      ,							)
                               __lowerCAmelCase					:						Union[str, Any]					=					os.path.join(A_      ,							f"""{prompt_key}_{key}.npy"""					)

                         __lowerCAmelCase					:						Tuple					=					tmp_dict

             with open(os.path.join(A_      ,							A_					)      ,							'''w'''					) as fp:
                   json.dump(A_      ,							A_					)

       super().save_pretrained(A_      ,							A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_ = None      ,							**A_					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					self.speaker_embeddings[voice_preset]

       __lowerCAmelCase					:						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}]."""					)

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

             __lowerCAmelCase					:						Optional[int]					=					np.load(A_					)

       return voice_preset_dict
 def 	UpperCamelCase__  (							self      ,							A_ = None					)					->str:



       '''simple docstring'''





       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      ,							A_=None      ,							A_=None      ,							A_="pt"      ,							A_=256      ,							A_=False      ,							A_=True      ,							A_=False      ,							**A_      ,							)					->Tuple:



       '''simple docstring'''





       if voice_preset is not None and not isinstance(A_      ,							A_					):
             if (
                 isinstance(A_      ,							A_					)
                 and self.speaker_embeddings is not None
                 and voice_preset in self.speaker_embeddings
             ):
                   __lowerCAmelCase					:						Dict					=					self._load_voice_preset(A_					)

             else:
                   if isinstance(A_      ,							A_					) and not voice_preset.endswith('''.npz'''					):
                         __lowerCAmelCase					:						Dict					=					voice_preset + '''.npz'''

                   __lowerCAmelCase					:						int					=					np.load(A_					)

       if voice_preset is not None:
             self._validate_voice_preset_dict(A_      ,							**A_					)
             __lowerCAmelCase					:						Optional[Any]					=					BatchFeature(data=A_      ,							tensor_type=A_					)

       __lowerCAmelCase					:						Union[str, Any]					=					self.tokenizer(
           A_      ,							return_tensors=A_      ,							padding='''max_length'''      ,							max_length=A_      ,							return_attention_mask=A_      ,							return_token_type_ids=A_      ,							add_special_tokens=A_      ,							**A_      ,							)

       if voice_preset is not None:
             __lowerCAmelCase					:						Optional[Any]					=					voice_preset

       return encoded_text





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """table-transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }
 def __init__(							self      ,							A_=True      ,							A_=None      ,							A_=3      ,							A_=100      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=0.0      ,							A_=0.0      ,							A_=True      ,							A_="relu"      ,							A_=256      ,							A_=0.1      ,							A_=0.0      ,							A_=0.0      ,							A_=0.02      ,							A_=1.0      ,							A_=False      ,							A_="sine"      ,							A_="resnet50"      ,							A_=True      ,							A_=False      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=1      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=0.1      ,							**A_      ,							)					->Any:



       '''simple docstring'''





       if backbone_config is not None and use_timm_backbone:
             raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''					)

       if not use_timm_backbone:
             if backbone_config is None:
                   logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''					)
                   __lowerCAmelCase					:						Optional[Any]					=					CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
             elif isinstance(A_      ,							A_					):
                   __lowerCAmelCase					:						int					=					backbone_config.get('''model_type'''					)
                   __lowerCAmelCase					:						List[str]					=					CONFIG_MAPPING[backbone_model_type]
                   __lowerCAmelCase					:						Any					=					config_class.from_dict(A_					)
             # set timm attributes to None
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						List[str]					=					None, None, None

       __lowerCAmelCase					:						Tuple					=					use_timm_backbone
       __lowerCAmelCase					:						Optional[Any]					=					backbone_config
       __lowerCAmelCase					:						List[str]					=					num_channels
       __lowerCAmelCase					:						Tuple					=					num_queries
       __lowerCAmelCase					:						int					=					d_model
       __lowerCAmelCase					:						List[Any]					=					encoder_ffn_dim
       __lowerCAmelCase					:						Optional[int]					=					encoder_layers
       __lowerCAmelCase					:						List[str]					=					encoder_attention_heads
       __lowerCAmelCase					:						str					=					decoder_ffn_dim
       __lowerCAmelCase					:						Union[str, Any]					=					decoder_layers
       __lowerCAmelCase					:						Any					=					decoder_attention_heads
       __lowerCAmelCase					:						Optional[int]					=					dropout
       __lowerCAmelCase					:						Any					=					attention_dropout
       __lowerCAmelCase					:						Tuple					=					activation_dropout
       __lowerCAmelCase					:						Optional[Any]					=					activation_function
       __lowerCAmelCase					:						List[str]					=					init_std
       __lowerCAmelCase					:						Tuple					=					init_xavier_std
       __lowerCAmelCase					:						Any					=					encoder_layerdrop
       __lowerCAmelCase					:						List[Any]					=					decoder_layerdrop
       __lowerCAmelCase					:						Optional[Any]					=					encoder_layers
       __lowerCAmelCase					:						Optional[Any]					=					auxiliary_loss
       __lowerCAmelCase					:						Optional[Any]					=					position_embedding_type
       __lowerCAmelCase					:						Tuple					=					backbone
       __lowerCAmelCase					:						Any					=					use_pretrained_backbone
       __lowerCAmelCase					:						int					=					dilation
       # Hungarian matcher
       __lowerCAmelCase					:						Dict					=					class_cost
       __lowerCAmelCase					:						List[str]					=					bbox_cost
       __lowerCAmelCase					:						int					=					giou_cost
       # Loss coefficients
       __lowerCAmelCase					:						Optional[Any]					=					mask_loss_coefficient
       __lowerCAmelCase					:						Tuple					=					dice_loss_coefficient
       __lowerCAmelCase					:						int					=					bbox_loss_coefficient
       __lowerCAmelCase					:						List[Any]					=					giou_loss_coefficient
       __lowerCAmelCase					:						int					=					eos_coefficient
       super().__init__(is_encoder_decoder=A_      ,							**A_					)
 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.encoder_attention_heads





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.d_model







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  version.parse("""1.11""" )
 @property
 def 	UpperCamelCase__  (							self					)					->Mapping[str, Mapping[int, str]]:



       '''simple docstring'''





       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ]					)
 @property
 def 	UpperCamelCase__  (							self					)					->float:



       '''simple docstring'''





       return 1e-5





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return 12


