

import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowercase    : int    	=  logging.get_logger("""transformers.models.speecht5""")

lowercase    : Any    	=  {
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
lowercase    : List[str]    	=  {
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
lowercase    : str    	=  {
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
lowercase    : Tuple    	=  {
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
lowercase    : Any    	=  {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
lowercase    : Dict    	=  {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
lowercase    : Tuple    	=  {
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
lowercase    : Optional[Any]    	=  {
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
lowercase    : Union[str, Any]    	=  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowercase    : Dict    	=  {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase    : Optional[int]    	=  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase    : Dict    	=  []
lowercase    : str    	=  [
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
lowercase    : Optional[int]    	=  IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
lowercase    : Dict    	=  IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
lowercase    : Optional[Any]    	=  IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       for attribute in key.split('''.'''					):
              _UpperCamelCase              =						getattr(lowerCAmelCase    ,       lowerCAmelCase					)

       if weight_type is not None:
              _UpperCamelCase              =						getattr(lowerCAmelCase    ,       lowerCAmelCase					).shape
       else:
              _UpperCamelCase              =						hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
                  f''' {value.shape} for {full_name}'''					)

       if weight_type == "weight":
              _UpperCamelCase              =						value
       elif weight_type == "weight_g":
              _UpperCamelCase              =						value
       elif weight_type == "weight_v":
              _UpperCamelCase              =						value
       elif weight_type == "bias":
              _UpperCamelCase              =						value
       elif weight_type == "running_mean":
              _UpperCamelCase              =						value
       elif weight_type == "running_var":
              _UpperCamelCase              =						value
       elif weight_type == "num_batches_tracked":
              _UpperCamelCase              =						value
       else:
              _UpperCamelCase              =						value

       logger.info(f'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'''					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       for key in ignore_keys:
              if key.endswith('''.*'''					):
                     if name.startswith(key[:-1]					):
                            return True
              elif ".*." in key:
                     _UpperCamelCase       ,		_UpperCamelCase              =						key.split('''.*.'''					)
                     if prefix in name and suffix in name:
                            return True
              elif key in name:
                     return True
       return False


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						[]

       if task == "s2t":
              _UpperCamelCase              =						hf_model.speechta.encoder.prenet.feature_encoder
              _UpperCamelCase              =						MAPPING_S2T
              _UpperCamelCase              =						IGNORE_KEYS_S2T
       elif task == "t2s":
              _UpperCamelCase              =						None
              _UpperCamelCase              =						MAPPING_T2S
              _UpperCamelCase              =						IGNORE_KEYS_T2S
       elif task == "s2s":
              _UpperCamelCase              =						hf_model.speechta.encoder.prenet.feature_encoder
              _UpperCamelCase              =						MAPPING_S2S
              _UpperCamelCase              =						IGNORE_KEYS_S2S
       else:
              raise ValueError(f'''Unsupported task: {task}'''					)

       for name, value in fairseq_dict.items():
              if should_ignore(lowerCAmelCase    ,       lowerCAmelCase					):
                     logger.info(f'''{name} was ignored'''					)
                     continue

              _UpperCamelCase              =						False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       hf_model.config.feat_extract_norm == '''group'''    ,       )
                     _UpperCamelCase              =						True
              else:
                     for key, mapped_key in MAPPING.items():
                            # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                            if "*" in key:
                                   _UpperCamelCase       ,		_UpperCamelCase              =						key.split('''.*.'''					)
                                   if prefix in name and suffix in name:
                                          _UpperCamelCase              =						suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                            if key in name:
                                   _UpperCamelCase              =						True
                                   if "*" in mapped_key:
                                          _UpperCamelCase              =						name.split(lowerCAmelCase					)[0].split('''.'''					)[-2]
                                          _UpperCamelCase              =						mapped_key.replace('''*'''    ,       lowerCAmelCase					)
                                   if "weight_g" in name:
                                          _UpperCamelCase              =						'''weight_g'''
                                   elif "weight_v" in name:
                                          _UpperCamelCase              =						'''weight_v'''
                                   elif "bias" in name:
                                          _UpperCamelCase              =						'''bias'''
                                   elif "weight" in name:
                                          _UpperCamelCase              =						'''weight'''
                                   elif "running_mean" in name:
                                          _UpperCamelCase              =						'''running_mean'''
                                   elif "running_var" in name:
                                          _UpperCamelCase              =						'''running_var'''
                                   elif "num_batches_tracked" in name:
                                          _UpperCamelCase              =						'''num_batches_tracked'''
                                   else:
                                          _UpperCamelCase              =						None
                                   set_recursively(lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					)
                            continue
              if not is_used:
                     unused_weights.append(lowerCAmelCase					)

       logger.warning(f'''Unused weights: {unused_weights}'''					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
       _UpperCamelCase              =						full_name.split('''conv_layers.'''					)[-1]
       _UpperCamelCase              =						name.split('''.'''					)
       _UpperCamelCase              =						int(items[0]					)
       _UpperCamelCase              =						int(items[1]					)

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                f'''{full_name} has size {value.shape}, but'''
                                f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''					)
                     _UpperCamelCase              =						value
                     logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                f'''{full_name} has size {value.shape}, but'''
                                f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''					)
                     _UpperCamelCase              =						value
                     logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                f'''{full_name} has size {value.shape}, but'''
                                f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''					)
                     _UpperCamelCase              =						value
                     logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                f'''{full_name} has size {value.shape}, but'''
                                f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''					)
                     _UpperCamelCase              =						value
                     logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
       else:
              unused_weights.append(lowerCAmelCase					)


@torch.no_grad()
def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase=None    ,       lowerCAmelCase=None    ,       lowerCAmelCase=None    ,       ):
       if config_path is not None:
              _UpperCamelCase              =						SpeechTaConfig.from_pretrained(lowerCAmelCase					)
       else:
              _UpperCamelCase              =						SpeechTaConfig()

       if task == "s2t":
              _UpperCamelCase              =						config.max_text_positions
              _UpperCamelCase              =						SpeechTaForSpeechToText(lowerCAmelCase					)
       elif task == "t2s":
              _UpperCamelCase              =						1_876
              _UpperCamelCase              =						600
              _UpperCamelCase              =						config.max_speech_positions
              _UpperCamelCase              =						SpeechTaForTextToSpeech(lowerCAmelCase					)
       elif task == "s2s":
              _UpperCamelCase              =						1_876
              _UpperCamelCase              =						config.max_speech_positions
              _UpperCamelCase              =						SpeechTaForSpeechToSpeech(lowerCAmelCase					)
       else:
              raise ValueError(f'''Unknown task name: {task}'''					)

       if vocab_path:
              _UpperCamelCase              =						SpeechTaTokenizer(lowerCAmelCase    ,       model_max_length=config.max_text_positions					)

              # Mask token behaves like a normal word, i.e. include the space before it
              _UpperCamelCase              =						AddedToken('''<mask>'''    ,       lstrip=lowerCAmelCase    ,       rstrip=lowerCAmelCase					)
              _UpperCamelCase              =						mask_token
              tokenizer.add_special_tokens({'''mask_token''': mask_token}					)
              tokenizer.add_tokens(['''<ctc_blank>''']					)

       _UpperCamelCase              =						SpeechTaFeatureExtractor()
       _UpperCamelCase              =						SpeechTaProcessor(tokenizer=lowerCAmelCase    ,       feature_extractor=lowerCAmelCase					)
       processor.save_pretrained(lowerCAmelCase					)

       _UpperCamelCase              =						torch.load(lowerCAmelCase					)
       recursively_load_weights(fairseq_checkpoint['''model''']    ,       lowerCAmelCase    ,       lowerCAmelCase					)

       model.save_pretrained(lowerCAmelCase					)

       if repo_id:
              print('''Pushing to the hub...'''					)
              processor.push_to_hub(lowerCAmelCase					)
              model.push_to_hub(lowerCAmelCase					)


if __name__ == "__main__":
       lowercase    : Union[str, Any]    	=  argparse.ArgumentParser()
       parser.add_argument(
           """--task""",
           default="""s2t""",
           type=str,
           help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
       )
       parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
       parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
       parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
       parser.add_argument(
           """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
       )

       lowercase    : Union[str, Any]    	=  parser.parse_args()
       convert_speechta_checkpoint(
           args.task,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.vocab_path,
           args.push_to_hub,
       )













import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowercase    : Optional[int]    	=  logging.get_logger(__name__)

lowercase    : List[Any]    	=  {
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class  __A(						__UpperCAmelCase ):
     __A	  =       "detr"
     __A	  =       ["past_key_values"]
     __A	  =       {
         "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,       ):





            """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.'''      )
                          _UpperCamelCase              =						CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']      )
                   elif isinstance(A,       A      ):
                          _UpperCamelCase              =						backbone_config.get('''model_type'''      )
                          _UpperCamelCase              =						CONFIG_MAPPING[backbone_model_type]
                          _UpperCamelCase              =						config_class.from_dict(A      )
                   # set timm attributes to None
                   _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						None, None, None

            _UpperCamelCase              =						use_timm_backbone
            _UpperCamelCase              =						backbone_config
            _UpperCamelCase              =						num_channels
            _UpperCamelCase              =						num_queries
            _UpperCamelCase              =						d_model
            _UpperCamelCase              =						encoder_ffn_dim
            _UpperCamelCase              =						encoder_layers
            _UpperCamelCase              =						encoder_attention_heads
            _UpperCamelCase              =						decoder_ffn_dim
            _UpperCamelCase              =						decoder_layers
            _UpperCamelCase              =						decoder_attention_heads
            _UpperCamelCase              =						dropout
            _UpperCamelCase              =						attention_dropout
            _UpperCamelCase              =						activation_dropout
            _UpperCamelCase              =						activation_function
            _UpperCamelCase              =						init_std
            _UpperCamelCase              =						init_xavier_std
            _UpperCamelCase              =						encoder_layerdrop
            _UpperCamelCase              =						decoder_layerdrop
            _UpperCamelCase              =						encoder_layers
            _UpperCamelCase              =						auxiliary_loss
            _UpperCamelCase              =						position_embedding_type
            _UpperCamelCase              =						backbone
            _UpperCamelCase              =						use_pretrained_backbone
            _UpperCamelCase              =						dilation
            # Hungarian matcher
            _UpperCamelCase              =						class_cost
            _UpperCamelCase              =						bbox_cost
            _UpperCamelCase              =						giou_cost
            # Loss coefficients
            _UpperCamelCase              =						mask_loss_coefficient
            _UpperCamelCase              =						dice_loss_coefficient
            _UpperCamelCase              =						bbox_loss_coefficient
            _UpperCamelCase              =						giou_loss_coefficient
            _UpperCamelCase              =						eos_coefficient
            super().__init__(is_encoder_decoder=A,       **A      )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.encoder_attention_heads

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.d_model

     @classmethod
     def 	_UpperCamelCase     (      cls,       A,       **A      ):





            """simple docstring"""

            return cls(backbone_config=A,       **A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						copy.deepcopy(self.__dict__      )
            if output["backbone_config"] is not None:
                   _UpperCamelCase              =						self.backbone_config.to_dict()
            _UpperCamelCase              =						self.__class__.model_type
            return output






class  __A(						__UpperCAmelCase ):
     __A	  =       version.parse("1.11" )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return OrderedDict(
                [
                    ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                    ('''pixel_mask''', {0: '''batch'''}),
                ]      )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 1E-5



     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 12






