

'''simple docstring'''

import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 				A_					(	snake_case ,    snake_case ,    snake_case	):
  # Construct configuration depending on size
  SCREAMING_SNAKE_CASE:List[Any]   =   {"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
  SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:Dict   =   MODELS[model_size]  # set model hyperparameters
  SCREAMING_SNAKE_CASE:int   =   ImageGPTConfig(n_embd=snake_case ,    n_layer=snake_case ,    n_head=snake_case	)
  SCREAMING_SNAKE_CASE:List[str]   =   ImageGPTForCausalLM(snake_case	)

  # Load weights from numpy
  load_tf_weights_in_imagegpt(snake_case ,    snake_case ,    snake_case	)

  # Save pytorch-model
  SCREAMING_SNAKE_CASE:Optional[Any]   =   pytorch_dump_folder_path + "/" + WEIGHTS_NAME
  SCREAMING_SNAKE_CASE:str   =   pytorch_dump_folder_path + "/" + CONFIG_NAME
  print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
  torch.save(model.state_dict() ,    snake_case	)
  print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
  with open(snake_case ,    "w" ,    encoding="utf-8"	) as f:
    f.write(config.to_json_string()	)


if __name__ == "__main__":
    A_      =							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--imagegpt_checkpoint_path",
        default=None,
        type=str,
        required=True,
        help="Path to the TensorFlow checkpoint path.",
    )
    parser.add_argument(
        "--model_size",
        default=None,
        type=str,
        required=True,
        help="Size of the model (can be either 'small', 'medium' or 'large').",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    A_      =							parser.parse_args()
    convert_imagegpt_checkpoint_to_pytorch(
        args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
    )









'''simple docstring'''

import shutil
import tempfile
import unittest

import pytest

from transformers import WhisperTokenizer, is_speech_available
from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio

from .test_feature_extraction_whisper import floats_list


if is_speech_available():
    from transformers import WhisperFeatureExtractor, WhisperProcessor


A_      =							5_03_58
A_      =							5_03_62





@require_torch
@require_torchaudio
@require_sentencepiece
class 							_snake_case	(    unittest.TestCase  ):
   def       __UpperCamelCase  (	self     : Any							):
     SCREAMING_SNAKE_CASE:int   =   "openai/whisper-small.en"
     SCREAMING_SNAKE_CASE:Optional[Any]   =   tempfile.mkdtemp()
   def       __UpperCamelCase  (	self     : Any       ,**SCREAMING_SNAKE_CASE__     : List[str]							):
     return WhisperTokenizer.from_pretrained(self.checkpoint       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : List[str]       ,**SCREAMING_SNAKE_CASE__     : Dict							):
     return WhisperFeatureExtractor.from_pretrained(self.checkpoint       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : str							):
     shutil.rmtree(self.tmpdirname							)
   def       __UpperCamelCase  (	self     : Any							):
     SCREAMING_SNAKE_CASE:Dict   =   self.get_tokenizer()
     SCREAMING_SNAKE_CASE:Optional[int]   =   self.get_feature_extractor()

     SCREAMING_SNAKE_CASE:Dict   =   WhisperProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__							)

     processor.save_pretrained(self.tmpdirname							)
     SCREAMING_SNAKE_CASE:List[Any]   =   WhisperProcessor.from_pretrained(self.tmpdirname							)

     self.assertEqual(processor.tokenizer.get_vocab()       ,tokenizer.get_vocab()							)
     self.assertIsInstance(processor.tokenizer       ,SCREAMING_SNAKE_CASE__							)

     self.assertEqual(processor.feature_extractor.to_json_string()       ,feature_extractor.to_json_string()							)
     self.assertIsInstance(processor.feature_extractor       ,SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Union[str, Any]							):
     SCREAMING_SNAKE_CASE:int   =   WhisperProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor()							)
     processor.save_pretrained(self.tmpdirname							)

     SCREAMING_SNAKE_CASE:List[str]   =   self.get_tokenizer(bos_token="(BOS)"       ,eos_token="(EOS)"							)
     SCREAMING_SNAKE_CASE:Tuple   =   self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE__       ,padding_value=1.0							)

     SCREAMING_SNAKE_CASE:Any   =   WhisperProcessor.from_pretrained(
         self.tmpdirname       ,bos_token="(BOS)"       ,eos_token="(EOS)"       ,do_normalize=SCREAMING_SNAKE_CASE__       ,padding_value=1.0							)

     self.assertEqual(processor.tokenizer.get_vocab()       ,tokenizer_add_kwargs.get_vocab()							)
     self.assertIsInstance(processor.tokenizer       ,SCREAMING_SNAKE_CASE__							)

     self.assertEqual(processor.feature_extractor.to_json_string()       ,feature_extractor_add_kwargs.to_json_string()							)
     self.assertIsInstance(processor.feature_extractor       ,SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : List[Any]							):
     SCREAMING_SNAKE_CASE:Any   =   self.get_feature_extractor()
     SCREAMING_SNAKE_CASE:List[Any]   =   self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Optional[Any]   =   WhisperProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Any   =   floats_list((3, 1_000)							)

     SCREAMING_SNAKE_CASE:Any   =   feature_extractor(SCREAMING_SNAKE_CASE__       ,return_tensors="np"							)
     SCREAMING_SNAKE_CASE:List[str]   =   processor(SCREAMING_SNAKE_CASE__       ,return_tensors="np"							)

     for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()       ,input_processor[key].sum()       ,delta=1e-2							)
   def       __UpperCamelCase  (	self     : List[Any]							):
     SCREAMING_SNAKE_CASE:int   =   self.get_feature_extractor()
     SCREAMING_SNAKE_CASE:Tuple   =   self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   WhisperProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str   =   "This is a test string"

     SCREAMING_SNAKE_CASE:int   =   processor(text=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str   =   tokenizer(SCREAMING_SNAKE_CASE__							)

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]       ,encoded_processor[key]							)
   def       __UpperCamelCase  (	self     : str							):
     SCREAMING_SNAKE_CASE:List[Any]   =   self.get_feature_extractor()
     SCREAMING_SNAKE_CASE:List[str]   =   self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   WhisperProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[Any]   =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     SCREAMING_SNAKE_CASE:Any   =   processor.batch_decode(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : int							):
     SCREAMING_SNAKE_CASE:Tuple   =   self.get_feature_extractor()
     SCREAMING_SNAKE_CASE:Optional[int]   =   self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   WhisperProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(
         processor.model_input_names       ,feature_extractor.model_input_names       ,msg="`processor` and `feature_extractor` model input names do not match"       ,)
   def       __UpperCamelCase  (	self     : int							):
     SCREAMING_SNAKE_CASE:Tuple   =   self.get_feature_extractor()
     SCREAMING_SNAKE_CASE:Any   =   self.get_tokenizer()

     SCREAMING_SNAKE_CASE:Any   =   WhisperProcessor(tokenizer=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   processor.get_decoder_prompt_ids(task="transcribe"       ,no_timestamps=SCREAMING_SNAKE_CASE__							)

     self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							)
     for ids in forced_decoder_ids:
       self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,(list, tuple)							)

     SCREAMING_SNAKE_CASE:Any   =   [TRANSCRIBE, NOTIMESTAMPS]
     self.assertListEqual([ids[-1] for ids in forced_decoder_ids]       ,SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Dict							):
     SCREAMING_SNAKE_CASE:Tuple   =   WhisperProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor()							)
     SCREAMING_SNAKE_CASE:Dict   =   processor.get_prompt_ids("Mr. Quilter"							)
     SCREAMING_SNAKE_CASE:int   =   processor.tokenizer.decode(SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(prompt_ids.tolist()       ,[50_360, 1_770, 13, 2_264, 346, 353]							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,"<|startofprev|> Mr. Quilter"							)
   def       __UpperCamelCase  (	self     : Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Dict   =   WhisperProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor()							)
     SCREAMING_SNAKE_CASE:List[Any]   =   processor.get_prompt_ids(""							)
     SCREAMING_SNAKE_CASE:Tuple   =   processor.tokenizer.decode(SCREAMING_SNAKE_CASE__							)

     self.assertListEqual(prompt_ids.tolist()       ,[50_360, 220]							)
     self.assertEqual(SCREAMING_SNAKE_CASE__       ,"<|startofprev|> "							)







   def       __UpperCamelCase  (	self     : Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Tuple   =   WhisperProcessor(tokenizer=self.get_tokenizer()       ,feature_extractor=self.get_feature_extractor()							)

     def _test_prompt_error_raised_helper(SCREAMING_SNAKE_CASE__     : int       ,SCREAMING_SNAKE_CASE__     : List[str]							):
       with pytest.raises(SCREAMING_SNAKE_CASE__							) as excinfo:
         processor.get_prompt_ids(SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:List[Any]   =   F'''Encountered text in the prompt corresponding to disallowed special token: {special_token}.'''
       self.assertEqual(SCREAMING_SNAKE_CASE__       ,str(excinfo.value							)							)

     _test_prompt_error_raised_helper("<|startofprev|> test"       ,"<|startofprev|>"							)
     _test_prompt_error_raised_helper("test <|notimestamps|>"       ,"<|notimestamps|>"							)
     _test_prompt_error_raised_helper("test <|zh|> test <|transcribe|>"       ,"<|zh|>"							)






