
"""simple docstring"""







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_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)







@add_end_docstrings(__UpperCAmelCase   )
class 							__magic_name__     (		__UpperCAmelCase   ):





					def __init__(				self   :							Any							,						*snake_case_   :							List[str]							,						**snake_case_   :							Any    ):
												super().__init__(*_lowerCamelCase							,						**_lowerCamelCase    )
												requires_backends(self							,						"vision"    )
												self.check_model_type(
												    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							Optional[int]=None							,						snake_case_   :							Dict=None							,						snake_case_   :							Any=None    ):
												__snake_case     			=       {}
												__snake_case     			=       {}

												if prompt is not None:
																			__snake_case     			=       prompt

												if generate_kwargs is not None:
																			__snake_case     			=       generate_kwargs
												if max_new_tokens is not None:
																			if "generate_kwargs" not in forward_kwargs:
																										__snake_case     			=       {}
																			if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																										raise ValueError(
																										    "\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,"
																										    " please use only one"    )
																			__snake_case     			=       max_new_tokens
												return preprocess_params, forward_kwargs, {}





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





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							List[str]							,						snake_case_   :							Dict=None    ):
												__snake_case     			=       load_image(_lowerCamelCase    )

												if prompt is not None:
																			if not isinstance(_lowerCamelCase							,						_lowerCamelCase    ):
																										raise ValueError(
																										    F'''Received an invalid text input, got - {type(_lowerCamelCase    )} - but expected a single string. '''
																										    "Note also that one single text can be provided for conditional image to text generation."    )

																			__snake_case     			=       self.model.config.model_type

																			if model_type == "git":
																										__snake_case     			=       self.image_processor(images=_lowerCamelCase							,						return_tensors=self.framework    )
																										__snake_case     			=       self.tokenizer(text=_lowerCamelCase							,						add_special_tokens=_lowerCamelCase    ).input_ids
																										__snake_case     			=       [self.tokenizer.cls_token_id] + input_ids
																										__snake_case     			=       torch.tensor(_lowerCamelCase    ).unsqueeze(0    )
																										model_inputs.update({"input_ids": input_ids}    )

																			elif model_type == "pix2struct":
																										__snake_case     			=       self.image_processor(images=_lowerCamelCase							,						header_text=_lowerCamelCase							,						return_tensors=self.framework    )

																			elif model_type != "vision-encoder-decoder":
																										# vision-encoder-decoder does not support conditional generation
																										__snake_case     			=       self.image_processor(images=_lowerCamelCase							,						return_tensors=self.framework    )
																										__snake_case     			=       self.tokenizer(_lowerCamelCase							,						return_tensors=self.framework    )
																										model_inputs.update(_lowerCamelCase    )

																			else:
																										raise ValueError(F'''Model type {model_type} does not support conditional text generation'''    )

												else:
																			__snake_case     			=       self.image_processor(images=_lowerCamelCase							,						return_tensors=self.framework    )

												if self.model.config.model_type == "git" and prompt is None:
																			__snake_case     			=       None

												return model_inputs





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							str							,						snake_case_   :							Dict=None    ):
												# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
												# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
												if (
												    "input_ids" in model_inputs
												    and isinstance(model_inputs["input_ids"]							,						_lowerCamelCase    )
												    and all(x is None for x in model_inputs["input_ids"]    )
												):
																			__snake_case     			=       None

												if generate_kwargs is None:
																			__snake_case     			=       {}
												# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
												#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
												#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
												#  in the `_prepare_model_inputs` method.
												__snake_case     			=       model_inputs.pop(self.model.main_input_name    )
												__snake_case     			=       self.model.generate(_lowerCamelCase							,						**_lowerCamelCase							,						**_lowerCamelCase    )
												return model_outputs





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							Union[str, Any]    ):
												__snake_case     			=       []
												for output_ids in model_outputs:
																			__snake_case     			=       {
																			    "generated_text": self.tokenizer.decode(
																			        _lowerCamelCase							,						skip_special_tokens=_lowerCamelCase							,						)
																			}
																			records.append(_lowerCamelCase    )
												return records


"""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    )
