





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

UpperCAmelCase__						:			List[Any]									=logging.get_logger(__name__)

@add_end_docstrings(a    )
class       __A     (						a    ):


				def __init__(		self   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								super().__init__(*UpperCAmelCase_   , **UpperCAmelCase_							)
								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 _snake_case       (		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , UpperCAmelCase_=None							):
								lowerCamelCase	 ={}
								lowerCamelCase	 ={}

								if prompt is not None:
												lowerCamelCase	 =prompt

								if generate_kwargs is not None:
												lowerCamelCase	 =generate_kwargs
								if max_new_tokens is not None:
												if "generate_kwargs" not in forward_kwargs:
																lowerCamelCase	 ={}
												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"""							)
												lowerCamelCase	 =max_new_tokens
								return preprocess_params, forward_kwargs, {}


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


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=None							):
								lowerCamelCase	 =load_image(UpperCAmelCase_							)

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

												lowerCamelCase	 =self.model.config.model_type

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

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

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

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

								else:
												lowerCamelCase	 =self.image_processor(images=UpperCAmelCase_   , return_tensors=self.framework							)

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

								return model_inputs


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=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"""]   , UpperCAmelCase_							)
								    and all(x is None for x in model_inputs["""input_ids"""]							)
								):
												lowerCamelCase	 =None

								if generate_kwargs is None:
												lowerCamelCase	 ={}
								# 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.
								lowerCamelCase	 =model_inputs.pop(self.model.main_input_name							)
								lowerCamelCase	 =self.model.generate(UpperCAmelCase_   , **UpperCAmelCase_   , **UpperCAmelCase_							)
								return model_outputs


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =[]
								for output_ids in model_outputs:
												lowerCamelCase	 ={
												    """generated_text""": self.tokenizer.decode(
												        UpperCAmelCase_   , skip_special_tokens=UpperCAmelCase_   , )
												}
												records.append(UpperCAmelCase_							)
								return records












import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__						:			int									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""projector.weight"""]
				lowerCamelCase	 =downstream_dict["""projector.bias"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  int:
				lowerCamelCase	 =UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""model.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""connector.weight"""]
				lowerCamelCase	 =downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
								lowerCamelCase	 =downstream_dict[
								    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
								]
								lowerCamelCase	 =downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
				lowerCamelCase	 =downstream_dict["""objective.W"""]
				return model





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  str:
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)

				lowerCamelCase	 =checkpoint["""Downstream"""]

				lowerCamelCase	 =UniSpeechSatConfig.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(
				    _UpperCAmelCase			,      return_attention_mask=_UpperCAmelCase			,      do_normalize=_UpperCAmelCase				)

				lowerCamelCase	 =hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""				):
								lowerCamelCase	 =convert_classification(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForAudioFrameClassification"""				):
								lowerCamelCase	 =convert_diarization(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForXVector"""				):
								lowerCamelCase	 =convert_xvector(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				else:
								raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

				if hf_config.use_weighted_layer_sum:
								lowerCamelCase	 =checkpoint["""Featurizer"""]["""weights"""]

				hf_feature_extractor.save_pretrained(_UpperCAmelCase				)
				hf_model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=argparse.ArgumentParser()
							parser.add_argument(
							    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
							)
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
							parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
							UpperCAmelCase__						:			List[Any]									=parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





