
"""simple docstring"""







from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)







@add_end_docstrings(lowercase__   )
class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Tuple							,						**snake_case_   :							str    ):
												super().__init__(**snake_case_    )

												requires_backends(self							,						"vision"    )
												self.check_model_type(
												    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
												    if self.framework == "tf"
												    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING    )





					def __call__(				self   :							Optional[Any]							,						snake_case_   :							Union[str, List[str], "Image", List["Image"]]							,						**snake_case_   :							Optional[int]    ):
												return super().__call__(snake_case_							,						**snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple							,						**snake_case_   :							Any    ):
												__snake_case     			=       {}
												if "candidate_labels" in kwargs:
																			__snake_case     			=       kwargs["candidate_labels"]
												if "hypothesis_template" in kwargs:
																			__snake_case     			=       kwargs["hypothesis_template"]

												return preprocess_params, {}, {}





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]=None							,						snake_case_   :							int="This is a photo of {}."    ):
												__snake_case     			=       load_image(snake_case_    )
												__snake_case     			=       self.image_processor(images=[image]							,						return_tensors=self.framework    )
												__snake_case     			=       candidate_labels
												__snake_case     			=       [hypothesis_template.format(snake_case_    ) for x in candidate_labels]
												__snake_case     			=       self.tokenizer(snake_case_							,						return_tensors=self.framework							,						padding=snake_case_    )
												__snake_case     			=       [text_inputs]
												return inputs





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Dict    ):
												__snake_case     			=       model_inputs.pop("candidate_labels"    )
												__snake_case     			=       model_inputs.pop("text_inputs"    )
												if isinstance(text_inputs[0]							,						snake_case_    ):
																			__snake_case     			=       text_inputs[0]
												else:
																			# Batching case.
																			__snake_case     			=       text_inputs[0][0]

												__snake_case     			=       self.model(**snake_case_							,						**snake_case_    )

												__snake_case     			=       {
												    "candidate_labels": candidate_labels,
												    "logits": outputs.logits_per_image,
												}
												return model_outputs





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Any    ):
												__snake_case     			=       model_outputs.pop("candidate_labels"    )
												__snake_case     			=       model_outputs["logits"][0]
												if self.framework == "pt":
																			__snake_case     			=       logits.softmax(dim=-1    ).squeeze(-1    )
																			__snake_case     			=       probs.tolist()
																			if not isinstance(snake_case_							,						snake_case_    ):
																										__snake_case     			=       [scores]
												elif self.framework == "tf":
																			__snake_case     			=       stable_softmax(snake_case_							,						axis=-1    )
																			__snake_case     			=       probs.numpy().tolist()
												else:
																			raise ValueError(F'''Unsupported framework: {self.framework}'''    )

												__snake_case     			=       [
												    {"score": score, "label": candidate_label}
												    for score, candidate_label in sorted(zip(snake_case_							,						snake_case_    )							,						key=lambda snake_case_    : -x[0]    )
												]
												return result


"""simple docstring"""







import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      {
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Any     							=   'data2vec-audio'





					def __init__(				self   :							Any							,						snake_case_   :							Optional[Any]=32							,						snake_case_   :							Tuple=768							,						snake_case_   :							Optional[int]=12							,						snake_case_   :							str=12							,						snake_case_   :							int=3072							,						snake_case_   :							Tuple="gelu"							,						snake_case_   :							Optional[Any]=0.1							,						snake_case_   :							str=0.1							,						snake_case_   :							int=0.1							,						snake_case_   :							int=0.0							,						snake_case_   :							Optional[int]=0.1							,						snake_case_   :							Dict=0.1							,						snake_case_   :							Dict=0.02							,						snake_case_   :							Optional[int]=1e-5							,						snake_case_   :							Optional[Any]="gelu"							,						snake_case_   :							Tuple=(512, 512, 512, 512, 512, 512, 512)							,						snake_case_   :							int=(5, 2, 2, 2, 2, 2, 2)							,						snake_case_   :							Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)							,						snake_case_   :							Optional[int]=False							,						snake_case_   :							int=16							,						snake_case_   :							List[Any]=19							,						snake_case_   :							str=5							,						snake_case_   :							List[Any]=0.05							,						snake_case_   :							Dict=10							,						snake_case_   :							Dict=2							,						snake_case_   :							Dict=0.0							,						snake_case_   :							Union[str, Any]=10							,						snake_case_   :							Optional[Any]=0							,						snake_case_   :							Optional[Any]="sum"							,						snake_case_   :							List[str]=False							,						snake_case_   :							List[Any]=False							,						snake_case_   :							Optional[int]=256							,						snake_case_   :							int=(512, 512, 512, 512, 1500)							,						snake_case_   :							List[Any]=(5, 3, 3, 1, 1)							,						snake_case_   :							Any=(1, 2, 3, 1, 1)							,						snake_case_   :							List[Any]=512							,						snake_case_   :							Optional[int]=0							,						snake_case_   :							Dict=1							,						snake_case_   :							List[str]=2							,						snake_case_   :							Optional[Any]=False							,						snake_case_   :							Optional[Any]=3							,						snake_case_   :							Tuple=2							,						snake_case_   :							Any=3							,						snake_case_   :							Union[str, Any]=None							,						**snake_case_   :							int							,						):
												super().__init__(**snake_case_							,						pad_token_id=snake_case_							,						bos_token_id=snake_case_							,						eos_token_id=snake_case_    )
												__snake_case     			=       hidden_size
												__snake_case     			=       feat_extract_activation
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       conv_bias
												__snake_case     			=       num_conv_pos_embeddings
												__snake_case     			=       num_conv_pos_embedding_groups
												__snake_case     			=       conv_pos_kernel_size
												__snake_case     			=       len(self.conv_dim    )
												__snake_case     			=       num_hidden_layers
												__snake_case     			=       intermediate_size
												__snake_case     			=       hidden_act
												__snake_case     			=       num_attention_heads
												__snake_case     			=       hidden_dropout
												__snake_case     			=       attention_dropout
												__snake_case     			=       activation_dropout
												__snake_case     			=       feat_proj_dropout
												__snake_case     			=       final_dropout
												__snake_case     			=       layerdrop
												__snake_case     			=       layer_norm_eps
												__snake_case     			=       initializer_range
												__snake_case     			=       vocab_size
												__snake_case     			=       use_weighted_layer_sum

												if (
												    (len(self.conv_stride    ) != self.num_feat_extract_layers)
												    or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
												    or (len(self.conv_dim    ) != self.num_feat_extract_layers)
												):
																			raise ValueError(
																			    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																			    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																			    F''' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'''
																			    F''' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												__snake_case     			=       mask_time_prob
												__snake_case     			=       mask_time_length
												__snake_case     			=       mask_time_min_masks
												__snake_case     			=       mask_feature_prob
												__snake_case     			=       mask_feature_length
												__snake_case     			=       mask_feature_min_masks

												# ctc loss
												__snake_case     			=       ctc_loss_reduction
												__snake_case     			=       ctc_zero_infinity

												# adapter
												__snake_case     			=       add_adapter
												__snake_case     			=       adapter_kernel_size
												__snake_case     			=       adapter_stride
												__snake_case     			=       num_adapter_layers
												__snake_case     			=       output_hidden_size or hidden_size

												# SequenceClassification-specific parameter. Feel free to ignore for other classes.
												__snake_case     			=       classifier_proj_size

												# XVector-specific parameters. Feel free to ignore for other classes.
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       xvector_output_dim





					@property
					def 							lowerCAmelCase      (				self   :							List[str]    ):
												return math.prod(self.conv_stride    )
