

"""simple docstring"""





import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY




@is_pipeline_test
class       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Tuple     	=			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 __UpperCAmelCase			:      Optional[int]     	=			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


 def       _lowercase				(    self							:    List[str],      UpperCAmelCase__							:    List[str],      UpperCAmelCase__							:    List[Any],      UpperCAmelCase__							:    Any			):



    if isinstance(model.config,      lowerCamelCase__			):
       __lowercase	,__lowercase			=		list(tokenizer.lang_code_to_id.keys()			)[:2]
       __lowercase			=		TranslationPipeline(model=lowerCamelCase__,      tokenizer=lowerCamelCase__,      src_lang=lowerCamelCase__,      tgt_lang=lowerCamelCase__			)
    else:
       __lowercase			=		TranslationPipeline(model=lowerCamelCase__,      tokenizer=lowerCamelCase__			)
    return translator, ["Some string", "Some other text"]


 def       _lowercase				(    self							:    List[Any],      UpperCAmelCase__							:    Tuple,      UpperCAmelCase__							:    int			):
    __lowercase			=		translator("Some string"			)
    self.assertEqual(lowerCamelCase__,      [{"translation_text": ANY(lowerCamelCase__			)}]			)

    __lowercase			=		translator(["Some string"]			)
    self.assertEqual(lowerCamelCase__,      [{"translation_text": ANY(lowerCamelCase__			)}]			)

    __lowercase			=		translator(["Some string", "other string"]			)
    self.assertEqual(lowerCamelCase__,      [{"translation_text": ANY(lowerCamelCase__			)}, {"translation_text": ANY(lowerCamelCase__			)}]			)


 @require_torch
 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		pipeline("translation_en_to_ro",      model="patrickvonplaten/t5-tiny-random",      framework="pt"			)
    __lowercase			=		translator("This is a test string",      max_length=2_0			)
    self.assertEqual(
        lowerCamelCase__,      [
            {
                "translation_text": (
                    "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                    " Beide Beide"
                )
            }
        ],      )


 @require_tf
 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		pipeline("translation_en_to_ro",      model="patrickvonplaten/t5-tiny-random",      framework="tf"			)
    __lowercase			=		translator("This is a test string",      max_length=2_0			)
    self.assertEqual(
        lowerCamelCase__,      [
            {
                "translation_text": (
                    "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                    " Beide Beide"
                )
            }
        ],      )


 @require_torch
 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		pipeline("translation_en_to_de",      model="patrickvonplaten/t5-tiny-random",      framework="pt"			)
    __lowercase			=		translator("This is a test string",      max_length=2_0			)
    self.assertEqual(
        lowerCamelCase__,      [
            {
                "translation_text": (
                    "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
                    " urine urine urine urine urine urine urine"
                )
            }
        ],      )



 @require_tf
 def       _lowercase				(    self							:    str			):
    __lowercase			=		pipeline("translation_en_to_de",      model="patrickvonplaten/t5-tiny-random",      framework="tf"			)
    __lowercase			=		translator("This is a test string",      max_length=2_0			)
    self.assertEqual(
        lowerCamelCase__,      [
            {
                "translation_text": (
                    "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
                    " urine urine urine urine urine urine urine"
                )
            }
        ],      )







class       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 @require_torch
 @slow
 def       _lowercase				(    self							:    Any			):



    with self.assertRaises(lowerCamelCase__			):
       pipeline(task="translation_cn_to_ar"			)

    # but we do for this one
    __lowercase			=		pipeline(task="translation_en_to_de"			)
    self.assertEqual(translator._preprocess_params["src_lang"],      "en"			)
    self.assertEqual(translator._preprocess_params["tgt_lang"],      "de"			)


 @require_torch
 @slow
 def       _lowercase				(    self							:    str			):
    __lowercase			=		MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"			)
    __lowercase			=		MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"			)

    __lowercase			=		pipeline(task="translation",      model=lowerCamelCase__,      tokenizer=lowerCamelCase__			)
    # Missing src_lang, tgt_lang
    with self.assertRaises(lowerCamelCase__			):
       translator("This is a test"			)

    __lowercase			=		translator("This is a test",      src_lang="en_XX",      tgt_lang="ar_AR"			)
    self.assertEqual(lowerCamelCase__,      [{"translation_text": "هذا إختبار"}]			)

    __lowercase			=		translator("This is a test",      src_lang="en_XX",      tgt_lang="hi_IN"			)
    self.assertEqual(lowerCamelCase__,      [{"translation_text": "यह एक परीक्षण है"}]			)

    # src_lang, tgt_lang can be defined at pipeline call time
    __lowercase			=		pipeline(task="translation",      model=lowerCamelCase__,      tokenizer=lowerCamelCase__,      src_lang="en_XX",      tgt_lang="ar_AR"			)
    __lowercase			=		translator("This is a test"			)
    self.assertEqual(lowerCamelCase__,      [{"translation_text": "هذا إختبار"}]			)


 @require_torch
 def       _lowercase				(    self							:    Any			):
    __lowercase			=		"patrickvonplaten/t5-tiny-random"
    __lowercase			=		pipeline(task="translation_cn_to_ar",      model=lowerCamelCase__			)
    self.assertEqual(translator._preprocess_params["src_lang"],      "cn"			)
    self.assertEqual(translator._preprocess_params["tgt_lang"],      "ar"			)


 @require_torch
 def       _lowercase				(    self							:    Any			):
    __lowercase			=		"patrickvonplaten/t5-tiny-random"
    with pytest.warns(lowerCamelCase__,      match=r".*translation_en_to_de.*"			):
       __lowercase			=		pipeline(task="translation",      model=lowerCamelCase__			)
    self.assertEqual(translator.task,      "translation_en_to_de"			)
    self.assertEqual(translator._preprocess_params["src_lang"],      "en"			)
    self.assertEqual(translator._preprocess_params["tgt_lang"],      "de"			)



 @require_torch
 def       _lowercase				(    self							:    Union[str, Any]			):



    with self.assertRaises(lowerCamelCase__			):
       pipeline(task="translation"			)



"""simple docstring"""





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_a				   =							logging.get_logger(__name__)

_a				   =							['UtteranceLevel']




@torch.no_grad()
def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : int,    UpperCamelCase_  : Optional[int],    UpperCamelCase_  : Union[str, Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		torch.load(UpperCamelCase_,    map_location="cpu")
   if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
      raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}""")

   __lowercase			=		checkpoint["Downstream"]

   __lowercase			=		HubertConfig.from_pretrained(UpperCamelCase_)
   __lowercase			=		HubertForSequenceClassification.from_pretrained(UpperCamelCase_,    config=UpperCamelCase_)
   __lowercase			=		WavaVecaFeatureExtractor.from_pretrained(
       UpperCamelCase_,    return_attention_mask=UpperCamelCase_,    do_normalize=UpperCamelCase_)

   if hf_congfig.use_weighted_layer_sum:
      __lowercase			=		checkpoint["Featurizer"]["weights"]

   __lowercase			=		downstream_dict["projector.weight"]
   __lowercase			=		downstream_dict["projector.bias"]
   __lowercase			=		downstream_dict["model.post_net.linear.weight"]
   __lowercase			=		downstream_dict["model.post_net.linear.bias"]

   hf_feature_extractor.save_pretrained(UpperCamelCase_)
   hf_model.save_pretrained(UpperCamelCase_)


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)




