

"""simple docstring"""





import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
   from tokenizers import Tokenizer
   from tokenizers.models import WordPiece




class       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    int,      UpperCAmelCase__							:    Optional[int]			):
    __lowercase			=		list(tokenizer_class.max_model_input_sizes.keys()			)
    for model_name in sa_models[:1]:
       __lowercase			=		tokenizer_class.from_pretrained(UpperCAmelCase__			)
       self.assertIsNotNone(UpperCAmelCase__			)
       self.assertIsInstance(UpperCAmelCase__,      UpperCAmelCase__			)
       self.assertIsInstance(UpperCAmelCase__,      UpperCAmelCase__			)

       for special_tok in tokenizer.all_special_tokens:
          self.assertIsInstance(UpperCAmelCase__,      UpperCAmelCase__			)
          __lowercase			=		tokenizer.convert_tokens_to_ids(UpperCAmelCase__			)
          self.assertIsInstance(UpperCAmelCase__,      UpperCAmelCase__			)


 def       _lowercase				(    self							:    Union[str, Any],      UpperCAmelCase__							:    BatchEncoding,      UpperCAmelCase__							:    Optional[Callable] = None			):
    __lowercase			=		pickle.dumps(UpperCAmelCase__			)
    self.assertIsNotNone(UpperCAmelCase__			)

    __lowercase			=		pickle.loads(UpperCAmelCase__			)

    # Ensure is_fast is correctly restored
    self.assertEqual(be_restored.is_fast,      be_original.is_fast			)

    # Ensure encodings are potentially correctly restored
    if be_original.is_fast:
       self.assertIsNotNone(be_restored.encodings			)
    else:
       self.assertIsNone(be_restored.encodings			)

    # Ensure the keys are the same
    for original_v, restored_v in zip(be_original.values(),      be_restored.values()			):
       if equal_op:
          self.assertTrue(equal_op(UpperCAmelCase__,      UpperCAmelCase__			)			)
       else:
          self.assertEqual(UpperCAmelCase__,      UpperCAmelCase__			)


 @slow
 def       _lowercase				(    self							:    List[Any]			):
    self.check_tokenizer_from_pretrained(UpperCAmelCase__			)


 def       _lowercase				(    self							:    str			):
    self.assertEqual(TensorType("tf"			),      TensorType.TENSORFLOW			)
    self.assertEqual(TensorType("pt"			),      TensorType.PYTORCH			)
    self.assertEqual(TensorType("np"			),      TensorType.NUMPY			)


 @require_tokenizers
 def       _lowercase				(    self							:    str			):
    import numpy as np

    __lowercase			=		BertTokenizer.from_pretrained("bert-base-cased"			)
    __lowercase			=		BertTokenizerFast.from_pretrained("bert-base-cased"			)

    # Python no tensor
    with self.subTest("BatchEncoding (Python, return_tensors=None)"			):
       self.assert_dump_and_restore(tokenizer_p("Small example to encode"			)			)

    with self.subTest("BatchEncoding (Python, return_tensors=NUMPY)"			):
       self.assert_dump_and_restore(
           tokenizer_p("Small example to encode",      return_tensors=TensorType.NUMPY			),      np.array_equal			)

    with self.subTest("BatchEncoding (Rust, return_tensors=None)"			):
       self.assert_dump_and_restore(tokenizer_r("Small example to encode"			)			)

    with self.subTest("BatchEncoding (Rust, return_tensors=NUMPY)"			):
       self.assert_dump_and_restore(
           tokenizer_r("Small example to encode",      return_tensors=TensorType.NUMPY			),      np.array_equal			)


 @require_tf
 @require_tokenizers
 def       _lowercase				(    self							:    List[str]			):
    import tensorflow as tf

    def tf_array_equals(UpperCAmelCase__							:    Any,      UpperCAmelCase__							:    Union[str, Any]			):
       return tf.reduce_all(tf.equal(UpperCAmelCase__,      UpperCAmelCase__			)			)

    __lowercase			=		BertTokenizer.from_pretrained("bert-base-cased"			)
    __lowercase			=		BertTokenizerFast.from_pretrained("bert-base-cased"			)

    with self.subTest("BatchEncoding (Python, return_tensors=TENSORFLOW)"			):
       self.assert_dump_and_restore(
           tokenizer_p("Small example to encode",      return_tensors=TensorType.TENSORFLOW			),      UpperCAmelCase__			)

    with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"			):
       self.assert_dump_and_restore(
           tokenizer_r("Small example to encode",      return_tensors=TensorType.TENSORFLOW			),      UpperCAmelCase__			)


 @require_torch
 @require_tokenizers
 def       _lowercase				(    self							:    str			):
    import torch

    __lowercase			=		BertTokenizer.from_pretrained("bert-base-cased"			)
    __lowercase			=		BertTokenizerFast.from_pretrained("bert-base-cased"			)

    with self.subTest("BatchEncoding (Python, return_tensors=PYTORCH)"			):
       self.assert_dump_and_restore(
           tokenizer_p("Small example to encode",      return_tensors=TensorType.PYTORCH			),      torch.equal			)

    with self.subTest("BatchEncoding (Rust, return_tensors=PYTORCH)"			):
       self.assert_dump_and_restore(
           tokenizer_r("Small example to encode",      return_tensors=TensorType.PYTORCH			),      torch.equal			)


 @require_tokenizers
 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		BertTokenizer.from_pretrained("bert-base-cased"			)
    __lowercase			=		BertTokenizerFast.from_pretrained("bert-base-cased"			)

    with self.subTest("Python Tokenizer"			):
       self.assertFalse(tokenizer_p("Small example to_encode"			).is_fast			)

    with self.subTest("Rust Tokenizer"			):
       self.assertTrue(tokenizer_r("Small example to_encode"			).is_fast			)


 @require_tokenizers
 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		BertTokenizerFast.from_pretrained("bert-base-cased"			)
    __lowercase			=		tokenizer_r(["Test", "\xad", "test"],      is_split_into_words=UpperCAmelCase__			)

    self.assertEqual(encoded.word_to_tokens(0			),      TokenSpan(start=1,      end=2			)			)
    self.assertEqual(encoded.word_to_tokens(1			),      UpperCAmelCase__			)
    self.assertEqual(encoded.word_to_tokens(2			),      TokenSpan(start=2,      end=3			)			)


 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}			)
    __lowercase			=		batch.convert_to_tensors(tensor_type="np"			)
    self.assertEqual(tensor_batch["inputs"].shape,      (2, 3)			)
    self.assertEqual(tensor_batch["labels"].shape,      (2,)			)
    # test converting the converted
    with CaptureStderr() as cs:
       __lowercase			=		batch.convert_to_tensors(tensor_type="np"			)
    self.assertFalse(len(cs.err			),      msg=F"""should have no warning, but got {cs.err}"""			)

    __lowercase			=		BatchEncoding({"inputs": [1, 2, 3], "labels": 0}			)
    __lowercase			=		batch.convert_to_tensors(tensor_type="np",      prepend_batch_axis=UpperCAmelCase__			)
    self.assertEqual(tensor_batch["inputs"].shape,      (1, 3)			)
    self.assertEqual(tensor_batch["labels"].shape,      (1,)			)


 @require_torch
 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}			)
    __lowercase			=		batch.convert_to_tensors(tensor_type="pt"			)
    self.assertEqual(tensor_batch["inputs"].shape,      (2, 3)			)
    self.assertEqual(tensor_batch["labels"].shape,      (2,)			)
    # test converting the converted
    with CaptureStderr() as cs:
       __lowercase			=		batch.convert_to_tensors(tensor_type="pt"			)
    self.assertFalse(len(cs.err			),      msg=F"""should have no warning, but got {cs.err}"""			)

    __lowercase			=		BatchEncoding({"inputs": [1, 2, 3], "labels": 0}			)
    __lowercase			=		batch.convert_to_tensors(tensor_type="pt",      prepend_batch_axis=UpperCAmelCase__			)
    self.assertEqual(tensor_batch["inputs"].shape,      (1, 3)			)
    self.assertEqual(tensor_batch["labels"].shape,      (1,)			)


 @require_tf
 def       _lowercase				(    self							:    str			):
    __lowercase			=		BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}			)
    __lowercase			=		batch.convert_to_tensors(tensor_type="tf"			)
    self.assertEqual(tensor_batch["inputs"].shape,      (2, 3)			)
    self.assertEqual(tensor_batch["labels"].shape,      (2,)			)
    # test converting the converted
    with CaptureStderr() as cs:
       __lowercase			=		batch.convert_to_tensors(tensor_type="tf"			)
    self.assertFalse(len(cs.err			),      msg=F"""should have no warning, but got {cs.err}"""			)

    __lowercase			=		BatchEncoding({"inputs": [1, 2, 3], "labels": 0}			)
    __lowercase			=		batch.convert_to_tensors(tensor_type="tf",      prepend_batch_axis=UpperCAmelCase__			)
    self.assertEqual(tensor_batch["inputs"].shape,      (1, 3)			)
    self.assertEqual(tensor_batch["labels"].shape,      (1,)			)


 @require_flax
 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}			)
    __lowercase			=		batch.convert_to_tensors(tensor_type="jax"			)
    self.assertEqual(tensor_batch["inputs"].shape,      (2, 3)			)
    self.assertEqual(tensor_batch["labels"].shape,      (2,)			)
    # test converting the converted
    with CaptureStderr() as cs:
       __lowercase			=		batch.convert_to_tensors(tensor_type="jax"			)
    self.assertFalse(len(cs.err			),      msg=F"""should have no warning, but got {cs.err}"""			)

    __lowercase			=		BatchEncoding({"inputs": [1, 2, 3], "labels": 0}			)
    __lowercase			=		batch.convert_to_tensors(tensor_type="jax",      prepend_batch_axis=UpperCAmelCase__			)
    self.assertEqual(tensor_batch["inputs"].shape,      (1, 3)			)
    self.assertEqual(tensor_batch["labels"].shape,      (1,)			)


 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		[{"input_ids": np.array([0, 1, 2]			)}, {"input_ids": np.array([0, 1, 2, 3]			)}]
    __lowercase			=		BertTokenizer.from_pretrained("bert-base-cased"			)

    __lowercase			=		tokenizer.pad(UpperCAmelCase__,      padding=UpperCAmelCase__			)
    self.assertTrue(isinstance(batch["input_ids"],      np.ndarray			)			)
    self.assertEqual(batch["input_ids"].tolist(),      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]			)
    __lowercase			=		tokenizer.pad(UpperCAmelCase__,      padding=UpperCAmelCase__,      return_tensors="np"			)
    self.assertTrue(isinstance(batch["input_ids"],      np.ndarray			)			)
    self.assertEqual(batch["input_ids"].tolist(),      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]			)


 @require_torch
 def       _lowercase				(    self							:    str			):
    import torch

    __lowercase			=		[{"input_ids": torch.tensor([0, 1, 2]			)}, {"input_ids": torch.tensor([0, 1, 2, 3]			)}]
    __lowercase			=		BertTokenizer.from_pretrained("bert-base-cased"			)

    __lowercase			=		tokenizer.pad(UpperCAmelCase__,      padding=UpperCAmelCase__			)
    self.assertTrue(isinstance(batch["input_ids"],      torch.Tensor			)			)
    self.assertEqual(batch["input_ids"].tolist(),      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]			)
    __lowercase			=		tokenizer.pad(UpperCAmelCase__,      padding=UpperCAmelCase__,      return_tensors="pt"			)
    self.assertTrue(isinstance(batch["input_ids"],      torch.Tensor			)			)
    self.assertEqual(batch["input_ids"].tolist(),      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]			)


 @require_tf
 def       _lowercase				(    self							:    List[str]			):
    import tensorflow as tf

    __lowercase			=		[{"input_ids": tf.constant([0, 1, 2]			)}, {"input_ids": tf.constant([0, 1, 2, 3]			)}]
    __lowercase			=		BertTokenizer.from_pretrained("bert-base-cased"			)

    __lowercase			=		tokenizer.pad(UpperCAmelCase__,      padding=UpperCAmelCase__			)
    self.assertTrue(isinstance(batch["input_ids"],      tf.Tensor			)			)
    self.assertEqual(batch["input_ids"].numpy().tolist(),      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]			)
    __lowercase			=		tokenizer.pad(UpperCAmelCase__,      padding=UpperCAmelCase__,      return_tensors="tf"			)
    self.assertTrue(isinstance(batch["input_ids"],      tf.Tensor			)			)
    self.assertEqual(batch["input_ids"].numpy().tolist(),      [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]			)


 @require_tokenizers
 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		Tokenizer(WordPiece(unk_token="[UNK]"			)			)
    PreTrainedTokenizerFast(tokenizer_object=UpperCAmelCase__			)



 @require_tokenizers
 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		Tokenizer(WordPiece(unk_token="[UNK]"			)			)
    with tempfile.TemporaryDirectory() as tmpdirname:
       bert_tokenizer.save(os.path.join(UpperCAmelCase__,      "tokenizer.json"			)			)
       PreTrainedTokenizerFast(tokenizer_file=os.path.join(UpperCAmelCase__,      "tokenizer.json"			)			)







"""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)




