


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

snake_case							:       Any										=	logging.get_logger(__name__)







@add_end_docstrings(_snake_case				)
class 						_snake_case    ( _snake_case				):




 def __init__(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None       ):
       a		:Optional[int]									=			{}
       a		:Tuple									=			{}

       if prompt is not None:
             a		:str									=			prompt

       if generate_kwargs is not None:
             a		:Dict									=			generate_kwargs
       if max_new_tokens is not None:
             if "generate_kwargs" not in forward_kwargs:
                   a		:str									=			{}
             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'''       )
             a		:Union[str, Any]									=			max_new_tokens
       return preprocess_params, forward_kwargs, {}




 def __call__(						self   ,    _lowerCamelCase   ,    **_lowerCamelCase       ):
       return super().__call__(_lowerCamelCase   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       a		:Any									=			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.'''       )

             a		:Optional[int]									=			self.model.config.model_type

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

             elif model_type == "pix2struct":
                   a		:int									=			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
                   a		:List[Any]									=			self.image_processor(images=_lowerCamelCase   ,    return_tensors=self.framework       )
                   a		:Optional[Any]									=			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:
             a		:Any									=			self.image_processor(images=_lowerCamelCase   ,    return_tensors=self.framework       )

       if self.model.config.model_type == "git" and prompt is None:
             a		:Union[str, Any]									=			None

       return model_inputs




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=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''']       )
       ):
             a		:str									=			None

       if generate_kwargs is None:
             a		:Union[str, Any]									=			{}
       # 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.
       a		:Optional[Any]									=			model_inputs.pop(self.model.main_input_name       )
       a		:List[str]									=			self.model.generate(_lowerCamelCase   ,    **_lowerCamelCase   ,    **_lowerCamelCase       )
       return model_outputs




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:List[Any]									=			[]
       for output_ids in model_outputs:
             a		:Optional[Any]									=			{
                 '''generated_text''': self.tokenizer.decode(
                     _lowerCamelCase   ,    skip_special_tokens=_lowerCamelCase   ,    )
             }
             records.append(_lowerCamelCase       )
       return records









import functools
import operator

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


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

snake_case							:       Optional[Any]										=	{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'wavlm'




 def __init__(						self   ,    _lowerCamelCase=32   ,    _lowerCamelCase=768   ,    _lowerCamelCase=12   ,    _lowerCamelCase=12   ,    _lowerCamelCase=3072   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=1e-5   ,    _lowerCamelCase="group"   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=(512, 512, 512, 512, 512, 512, 512)   ,    _lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)   ,    _lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)   ,    _lowerCamelCase=False   ,    _lowerCamelCase=128   ,    _lowerCamelCase=16   ,    _lowerCamelCase=320   ,    _lowerCamelCase=800   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=0.05   ,    _lowerCamelCase=10   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=10   ,    _lowerCamelCase=320   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=100   ,    _lowerCamelCase=256   ,    _lowerCamelCase=256   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase="mean"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=False   ,    _lowerCamelCase=256   ,    _lowerCamelCase=(512, 512, 512, 512, 1500)   ,    _lowerCamelCase=(5, 3, 3, 1, 1)   ,    _lowerCamelCase=(1, 2, 3, 1, 1)   ,    _lowerCamelCase=512   ,    _lowerCamelCase=80   ,    _lowerCamelCase=0   ,    _lowerCamelCase=1   ,    _lowerCamelCase=2   ,    _lowerCamelCase=False   ,    _lowerCamelCase=3   ,    _lowerCamelCase=2   ,    _lowerCamelCase=3   ,    _lowerCamelCase=None   ,    **_lowerCamelCase   ,    ):
       super().__init__(**_lowerCamelCase   ,    pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase       )
       a		:Union[str, Any]									=			hidden_size
       a		:List[Any]									=			feat_extract_norm
       a		:Tuple									=			feat_extract_activation
       a		:Tuple									=			list(_lowerCamelCase       )
       a		:Optional[Any]									=			list(_lowerCamelCase       )
       a		:Optional[Any]									=			list(_lowerCamelCase       )
       a		:int									=			conv_bias
       a		:Tuple									=			num_buckets
       a		:Tuple									=			max_bucket_distance
       a		:int									=			num_conv_pos_embeddings
       a		:List[str]									=			num_conv_pos_embedding_groups
       a		:Tuple									=			len(self.conv_dim       )
       a		:Any									=			num_hidden_layers
       a		:Tuple									=			intermediate_size
       a		:Any									=			hidden_act
       a		:List[str]									=			num_attention_heads
       a		:int									=			hidden_dropout
       a		:Optional[Any]									=			attention_dropout
       a		:Dict									=			activation_dropout
       a		:Any									=			feat_proj_dropout
       a		:Dict									=			final_dropout
       a		:int									=			layerdrop
       a		:Tuple									=			layer_norm_eps
       a		:Union[str, Any]									=			initializer_range
       a		:Union[str, Any]									=			num_ctc_classes
       a		:Optional[Any]									=			vocab_size
       a		:str									=			do_stable_layer_norm
       a		:Dict									=			use_weighted_layer_sum
       a		:Any									=			classifier_proj_size

       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
       a		:List[Any]									=			apply_spec_augment
       a		:Optional[int]									=			mask_time_prob
       a		:Optional[Any]									=			mask_time_length
       a		:Tuple									=			mask_time_min_masks
       a		:Dict									=			mask_feature_prob
       a		:List[Any]									=			mask_feature_length

       # parameters for pretraining with codevector quantized representations
       a		:Tuple									=			num_codevectors_per_group
       a		:List[str]									=			num_codevector_groups
       a		:List[Any]									=			contrastive_logits_temperature
       a		:Union[str, Any]									=			num_negatives
       a		:Union[str, Any]									=			codevector_dim
       a		:Dict									=			proj_codevector_dim
       a		:Union[str, Any]									=			diversity_loss_weight

       # ctc loss
       a		:Dict									=			ctc_loss_reduction
       a		:Optional[int]									=			ctc_zero_infinity

       # adapter
       a		:Any									=			add_adapter
       a		:str									=			adapter_kernel_size
       a		:Tuple									=			adapter_stride
       a		:Dict									=			num_adapter_layers
       a		:Any									=			output_hidden_size or hidden_size

       # SequenceClassification-specific parameter. Feel free to ignore for other classes.
       a		:Tuple									=			classifier_proj_size

       # XVector-specific parameters. Feel free to ignore for other classes.
       a		:List[Any]									=			list(_lowerCamelCase       )
       a		:Optional[int]									=			list(_lowerCamelCase       )
       a		:List[str]									=			list(_lowerCamelCase       )
       a		:Optional[int]									=			xvector_output_dim




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return functools.reduce(operator.mul   ,    self.conv_stride   ,    1       )





