
'''simple docstring'''





import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     A							     = False


if is_vision_available():
     from PIL import Image
else:



     class 			lowerCAmelCase__ :



       '''simple docstring'''






       @staticmethod
       def 		_lowerCamelCase						(*lowerCAmelCase_	:			str						,			**lowerCAmelCase_	:			List[str]     ) ->							List[Any]:
             pass



@is_pipeline_test
@require_vision
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  _UpperCamelCase							=			MODEL_FOR_VISION_2_SEQ_MAPPING
  _UpperCamelCase							=			TF_MODEL_FOR_VISION_2_SEQ_MAPPING

  def 		_lowerCamelCase						(self	:			List[str]						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			List[Any]     ) ->							List[Any]:
        snake_case_    							=				pipeline("image-to-text"						,			model=lowerCAmelCase_						,			tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )
        snake_case_    							=				[
            Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]
        return pipe, examples

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			Union[str, Any]     ) ->							Optional[int]:
        snake_case_    							=				pipe(lowerCAmelCase_     )
        self.assertEqual(
            lowerCAmelCase_						,			[
                [{"generated_text": ANY(lowerCAmelCase_     )}],
                [{"generated_text": ANY(lowerCAmelCase_     )}],
            ]						,			)

  @require_tf
  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Optional[Any]:
        snake_case_    							=				pipeline("image-to-text"						,			model="hf-internal-testing/tiny-random-vit-gpt2"						,			framework="tf"     )
        snake_case_    							=				"./tests/fixtures/tests_samples/COCO/000000039769.png"

        snake_case_    							=				pipe(lowerCAmelCase_     )
        self.assertEqual(
            lowerCAmelCase_						,			[
                {
                    "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                },
            ]						,			)

        snake_case_    							=				pipe([image, image]     )
        self.assertEqual(
            lowerCAmelCase_						,			[
                [
                    {
                        "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                    }
                ],
                [
                    {
                        "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                    }
                ],
            ]						,			)

        snake_case_    							=				pipe(lowerCAmelCase_						,			max_new_tokens=1     )
        self.assertEqual(
            lowerCAmelCase_						,			[{"generated_text": "growth"}]						,			)

  @require_torch
  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Dict:
        snake_case_    							=				pipeline("image-to-text"						,			model="hf-internal-testing/tiny-random-vit-gpt2"     )
        snake_case_    							=				"./tests/fixtures/tests_samples/COCO/000000039769.png"

        snake_case_    							=				pipe(lowerCAmelCase_     )
        self.assertEqual(
            lowerCAmelCase_						,			[
                {
                    "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                },
            ]						,			)

        snake_case_    							=				pipe([image, image]     )
        self.assertEqual(
            lowerCAmelCase_						,			[
                [
                    {
                        "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                    }
                ],
                [
                    {
                        "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
                    }
                ],
            ]						,			)

  @require_torch
  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							str:
        snake_case_    							=				pipeline("image-to-text"						,			model="hf-internal-testing/tiny-random-BlipForConditionalGeneration"     )
        snake_case_    							=				"./tests/fixtures/tests_samples/COCO/000000039769.png"
        snake_case_    							=				"a photo of"

        snake_case_    							=				pipe(lowerCAmelCase_						,			prompt=lowerCAmelCase_     )
        self.assertTrue(outputs[0]["generated_text"].startswith(lowerCAmelCase_     )     )

  @slow
  @require_torch
  def 		_lowerCamelCase						(self	:			Dict     ) ->							int:
        snake_case_    							=				pipeline("image-to-text"						,			model="ydshieh/vit-gpt2-coco-en"     )
        snake_case_    							=				"./tests/fixtures/tests_samples/COCO/000000039769.png"

        snake_case_    							=				pipe(lowerCAmelCase_     )
        self.assertEqual(lowerCAmelCase_						,			[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]     )

        snake_case_    							=				pipe([image, image]     )
        self.assertEqual(
            lowerCAmelCase_						,			[
                [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
            ]						,			)

  @slow
  @require_torch
  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							List[str]:
        snake_case_    							=				pipeline("image-to-text"						,			model="Salesforce/blip-image-captioning-base"     )
        snake_case_    							=				"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
        snake_case_    							=				Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_     ).raw     )

        snake_case_    							=				pipe(lowerCAmelCase_     )
        self.assertEqual(lowerCAmelCase_						,			[{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}]     )

  @slow
  @require_torch
  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Union[str, Any]:
        snake_case_    							=				pipeline("image-to-text"						,			model="microsoft/git-base-coco"     )
        snake_case_    							=				"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
        snake_case_    							=				Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_     ).raw     )

        snake_case_    							=				pipe(lowerCAmelCase_     )
        self.assertEqual(lowerCAmelCase_						,			[{"generated_text": "a cartoon of a purple character."}]     )

  @slow
  @require_torch
  def 		_lowerCamelCase						(self	:			str     ) ->							List[str]:
        snake_case_    							=				pipeline("image-to-text"						,			model="Salesforce/blip-image-captioning-base"     )
        snake_case_    							=				"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
        snake_case_    							=				Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_     ).raw     )

        snake_case_    							=				"a photography of"

        snake_case_    							=				pipe(lowerCAmelCase_						,			prompt=lowerCAmelCase_     )
        self.assertEqual(lowerCAmelCase_						,			[{"generated_text": "a photography of a volcano"}]     )

        with self.assertRaises(lowerCAmelCase_     ):
              snake_case_    							=				pipe([image, image]						,			prompt=[prompt, prompt]     )

  @slow
  @require_torch
  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							Dict:
        snake_case_    							=				pipeline("image-to-text"						,			model="microsoft/git-base-coco"     )
        snake_case_    							=				"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
        snake_case_    							=				Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_     ).raw     )

        snake_case_    							=				"a photo of a"

        snake_case_    							=				pipe(lowerCAmelCase_						,			prompt=lowerCAmelCase_     )
        self.assertEqual(lowerCAmelCase_						,			[{"generated_text": "a photo of a tent with a tent and a tent in the background."}]     )

        with self.assertRaises(lowerCAmelCase_     ):
              snake_case_    							=				pipe([image, image]						,			prompt=[prompt, prompt]     )

  @unittest.skipIf(
      not is_torch_greater_or_equal_than_1_11						,			reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."     )
  @slow
  @require_torch
  def 		_lowerCamelCase						(self	:			Dict     ) ->							Dict:
        snake_case_    							=				pipeline("image-to-text"						,			model="google/pix2struct-ai2d-base"     )
        snake_case_    							=				"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
        snake_case_    							=				Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_     ).raw     )

        snake_case_    							=				"What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

        snake_case_    							=				pipe(lowerCAmelCase_						,			prompt=lowerCAmelCase_     )
        self.assertEqual(lowerCAmelCase_						,			[{"generated_text": "ash cloud"}]     )

        with self.assertRaises(lowerCAmelCase_     ):
              snake_case_    							=				pipe([image, image]						,			prompt=[prompt, prompt]     )


  @slow
  @require_tf
  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Tuple:
        snake_case_    							=				pipeline("image-to-text"						,			model="ydshieh/vit-gpt2-coco-en"						,			framework="tf"     )
        snake_case_    							=				"./tests/fixtures/tests_samples/COCO/000000039769.png"

        snake_case_    							=				pipe(lowerCAmelCase_     )
        self.assertEqual(lowerCAmelCase_						,			[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]     )

        snake_case_    							=				pipe([image, image]     )
        self.assertEqual(
            lowerCAmelCase_						,			[
                [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
                [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
            ]						,			)



'''simple docstring'''





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				WavLMForSequenceClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["projector.weight"]
      snake_case_    							=				downstream_dict["projector.bias"]
      snake_case_    							=				downstream_dict["model.post_net.linear.weight"]
      snake_case_    							=				downstream_dict["model.post_net.linear.bias"]
      return model
def 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[Any]			,       _a  :				List[str]      ):
      snake_case_    							=				WavLMForAudioFrameClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["model.linear.weight"]
      snake_case_    							=				downstream_dict["model.linear.bias"]
      return model
def 	snake_case_							(				_a  :				Dict			,       _a  :				Tuple			,       _a  :				Any      ):
      snake_case_    							=				WavLMForXVector.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["connector.weight"]
      snake_case_    							=				downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
            snake_case_    							=				downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            snake_case_    							=				downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      snake_case_    							=				downstream_dict["objective.W"]
      return model
@torch.no_grad()
def 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				torch.load(_a			,       map_location="cpu"      )

      snake_case_    							=				checkpoint["Downstream"]

      snake_case_    							=				WavLMConfig.from_pretrained(_a      )
      snake_case_    							=				WavaVecaFeatureExtractor.from_pretrained(
          _a			,       return_attention_mask=_a			,       do_normalize=_a      )

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

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

      hf_feature_extractor.save_pretrained(_a      )
      hf_model.save_pretrained(_a      )


if __name__ == "__main__":
     A							     = 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.")
     A							     = parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

