


import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def 			_lowerCAmelCase						(							)->Dict:
   '''simple docstring'''






   snake_case_					    =					set()
   # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
   for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
      snake_case_					    =					getattr(transformers.models.auto		,						lowerCAmelCase_		,						lowerCAmelCase_							)
      if module is None:
         continue
      # all mappings in a single auto modeling file
      snake_case_					    =					[
          x
          for x in dir(lowerCAmelCase_							)
          if x.endswith("_MAPPING_NAMES"							)
          and (x.startswith("MODEL_"							) or x.startswith("TF_MODEL_"							) or x.startswith("FLAX_MODEL_"							))
      ]
      for name in mapping_names:
         snake_case_					    =					getattr(lowerCAmelCase_		,						lowerCAmelCase_							)
         if mapping is not None:
            for v in mapping.values():
               if isinstance(lowerCAmelCase_		,						(list, tuple)							):
                  model_names.update(lowerCAmelCase_							)
               elif isinstance(lowerCAmelCase_		,						lowerCAmelCase_							):
                  model_names.add(lowerCAmelCase_							)

   return sorted(lowerCAmelCase_							)



def 			_lowerCAmelCase						(							)->List[Any]:
   '''simple docstring'''






   snake_case_					    =					set(get_all_model_names()							)

   with open("tests/utils/tiny_model_summary.json"							) as fp:
      snake_case_					    =					json.load(lowerCAmelCase_							)
   snake_case_					    =					set()
   for model_base_name in tiny_model_info:
      tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"]							)

   # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
   snake_case_					    =					model_names.difference(lowerCAmelCase_							)
   for model_name in copy.copy(lowerCAmelCase_							):
      if not model_name.startswith("TF"							) and F'''TF{model_name}''' in not_on_hub:
         tiny_models_names.remove(lowerCAmelCase_							)
      elif model_name.startswith("TF"							) and model_name[2:] in not_on_hub:
         tiny_models_names.remove(lowerCAmelCase_							)

   return sorted(lowerCAmelCase_							)



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Tuple							)->Union[str, Any]:
   '''simple docstring'''






   snake_case_					    =					COMPOSITE_MODELS.values()

   # All tiny model base names on Hub
   snake_case_					    =					get_all_model_names()
   snake_case_					    =					hf_api.list_models(
       filter=ModelFilter(
           author="hf-internal-testing"		,						)							)
   snake_case_					    =					set()
   for x in models:
      snake_case_					    =					x.modelId
      snake_case_    ,    snake_case_					    =					model.split("/"							)
      if not model.startswith("tiny-random-"							):
         continue
      snake_case_					    =					model.replace("tiny-random-"		,						""							)
      if not model[0].isupper():
         continue
      if model not in model_names and model not in special_models:
         continue
      _models.add(lowerCAmelCase_							)

   snake_case_					    =					sorted(_models							)
   # All tiny model names on Hub
   snake_case_					    =					{}
   for model in models:
      snake_case_					    =					F'''hf-internal-testing/tiny-random-{model}'''
      snake_case_					    =					model.split("-"							)[0]
      try:
         snake_case_					    =					hf_api.repo_info(lowerCAmelCase_							)
         snake_case_					    =					{
             "tokenizer_classes": set(),
             "processor_classes": set(),
             "model_classes": set(),
             "sha": repo_info.sha,
         }
      except Exception:
         continue
      try:
         time.sleep(1							)
         snake_case_					    =					AutoTokenizer.from_pretrained(lowerCAmelCase_							)
         content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__							)
      except Exception:
         pass
      try:
         time.sleep(1							)
         snake_case_					    =					AutoTokenizer.from_pretrained(lowerCAmelCase_		,						use_fast=lowerCAmelCase_							)
         content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__							)
      except Exception:
         pass
      try:
         time.sleep(1							)
         snake_case_					    =					AutoImageProcessor.from_pretrained(lowerCAmelCase_							)
         content["processor_classes"].add(img_p.__class__.__name__							)
      except Exception:
         pass
      try:
         time.sleep(1							)
         snake_case_					    =					AutoFeatureExtractor.from_pretrained(lowerCAmelCase_							)
         if not isinstance(lowerCAmelCase_		,						lowerCAmelCase_							):
            content["processor_classes"].add(feat_p.__class__.__name__							)
      except Exception:
         pass
      try:
         time.sleep(1							)
         snake_case_					    =					getattr(lowerCAmelCase_		,						lowerCAmelCase_							)
         snake_case_					    =					model_class.from_pretrained(lowerCAmelCase_							)
         content["model_classes"].add(m.__class__.__name__							)
      except Exception:
         pass
      try:
         time.sleep(1							)
         snake_case_					    =					getattr(lowerCAmelCase_		,						F'''TF{model}'''							)
         snake_case_					    =					model_class.from_pretrained(lowerCAmelCase_							)
         content["model_classes"].add(m.__class__.__name__							)
      except Exception:
         pass

      snake_case_					    =					sorted(content["tokenizer_classes"]							)
      snake_case_					    =					sorted(content["processor_classes"]							)
      snake_case_					    =					sorted(content["model_classes"]							)

      snake_case_					    =					content
      with open(os.path.join(lowerCAmelCase_		,						"hub_tiny_model_summary.json"							)		,						"w"							) as fp:
         json.dump(lowerCAmelCase_		,						lowerCAmelCase_		,						ensure_ascii=lowerCAmelCase_		,						indent=4							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE	:str  		=     argparse.ArgumentParser()
   parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
   SCREAMING_SNAKE_CASE	:str  		=     parser.parse_args()

   # This has to be `spawn` to avoid hanging forever!
   multiprocessing.set_start_method('''spawn''')

   SCREAMING_SNAKE_CASE	:Union[str, Any]  		=     '''tiny_models'''
   SCREAMING_SNAKE_CASE	:Tuple  		=     True
   SCREAMING_SNAKE_CASE	:Optional[Any]  		=     None
   SCREAMING_SNAKE_CASE	:Any  		=     get_tiny_model_names_from_repo()
   SCREAMING_SNAKE_CASE	:Dict  		=     True
   SCREAMING_SNAKE_CASE	:List[str]  		=     True
   SCREAMING_SNAKE_CASE	:Any  		=     '''hf-internal-testing'''

   create_tiny_models(
       output_path,
       all,
       model_types,
       models_to_skip,
       no_check,
       upload,
       organization,
       token=os.environ.get('''TOKEN''', None),
       num_workers=args.num_workers,
   )








import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
   pass

if is_speech_available():
   from transformers import EncodecFeatureExtractor, MusicgenProcessor


SCREAMING_SNAKE_CASE	:Union[str, Any]  		=     random.Random()



def 			_lowerCAmelCase						(							lowerCAmelCase_							:Union[str, Any]		,						lowerCAmelCase_							:Optional[int]=1.0		,						lowerCAmelCase_							:List[str]=None		,						lowerCAmelCase_							:str=None							)->Union[str, Any]:
   '''simple docstring'''






   if rng is None:
      snake_case_					    =					global_rng

   snake_case_					    =					[]
   for batch_idx in range(shape[0]							):
      values.append([]							)
      for _ in range(shape[1]							):
         values[-1].append(rng.random() * scale							)

   return values



@require_torch
@require_sentencepiece
class     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					"facebook/musicgen-small"
     snake_case_					    =					tempfile.mkdtemp()



  def 					lowerCAmelCase__  (     self       :       Tuple    ,				**_lowerCAmelCase       :       Tuple  )   -> Optional[int]:




     """simple docstring"""
     return TaTokenizer.from_pretrained(self.checkpoint    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Union[str, Any]    ,				**_lowerCAmelCase       :       Union[str, Any]  )   -> int:




     """simple docstring"""
     return EncodecFeatureExtractor.from_pretrained(self.checkpoint    ,				**_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Optional[int]:




     """simple docstring"""
     shutil.rmtree(self.tmpdirname  )



  def 					lowerCAmelCase__  (     self       :       int  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_tokenizer()
     snake_case_					    =					self.get_feature_extractor()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     processor.save_pretrained(self.tmpdirname  )
     snake_case_					    =					MusicgenProcessor.from_pretrained(self.tmpdirname  )

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

     self.assertEqual(processor.feature_extractor.to_json_string()    ,				feature_extractor.to_json_string()  )
     self.assertIsInstance(processor.feature_extractor    ,				_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Optional[Any]  )   -> Tuple:




     """simple docstring"""
     snake_case_					    =					MusicgenProcessor(tokenizer=self.get_tokenizer()    ,				feature_extractor=self.get_feature_extractor()  )
     processor.save_pretrained(self.tmpdirname  )

     snake_case_					    =					self.get_tokenizer(bos_token="(BOS)"    ,				eos_token="(EOS)"  )
     snake_case_					    =					self.get_feature_extractor(do_normalize=_lowerCAmelCase    ,				padding_value=1.0  )

     snake_case_					    =					MusicgenProcessor.from_pretrained(
         self.tmpdirname    ,				bos_token="(BOS)"    ,				eos_token="(EOS)"    ,				do_normalize=_lowerCAmelCase    ,				padding_value=1.0  )

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

     self.assertEqual(processor.feature_extractor.to_json_string()    ,				feature_extractor_add_kwargs.to_json_string()  )
     self.assertIsInstance(processor.feature_extractor    ,				_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> Optional[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					floats_list((3, 1_0_0_0)  )

     snake_case_					    =					feature_extractor(_lowerCAmelCase    ,				return_tensors="np"  )
     snake_case_					    =					processor(_lowerCAmelCase    ,				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 					lowerCAmelCase__  (     self       :       Tuple  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					"This is a test string"

     snake_case_					    =					processor(text=_lowerCAmelCase  )

     snake_case_					    =					tokenizer(_lowerCAmelCase  )

     for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]    ,				encoded_processor[key]  )



  def 					lowerCAmelCase__  (     self       :       Any  )   -> str:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     snake_case_					    =					processor.batch_decode(sequences=_lowerCAmelCase  )
     snake_case_					    =					tokenizer.batch_decode(_lowerCAmelCase  )

     self.assertListEqual(_lowerCAmelCase    ,				_lowerCAmelCase  )



  def 					lowerCAmelCase__  (     self       :       Optional[int]  )   -> Dict:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor()
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     self.assertListEqual(
         processor.model_input_names    ,				feature_extractor.model_input_names    ,				msg="`processor` and `feature_extractor` model input names do not match"    ,				)



  def 					lowerCAmelCase__  (     self       :       Tuple  )   -> int:




     """simple docstring"""
     snake_case_					    =					self.get_feature_extractor(padding_side="left"  )
     snake_case_					    =					self.get_tokenizer()

     snake_case_					    =					MusicgenProcessor(tokenizer=_lowerCAmelCase    ,				feature_extractor=_lowerCAmelCase  )

     snake_case_					    =					[floats_list((1, x)  )[0] for x in range(5    ,				2_0    ,				5  )]
     snake_case_					    =					processor(_lowerCAmelCase  ).padding_mask

     snake_case_					    =					np.asarray(floats_list((3, 2_0)  )  )[:, None, :]
     snake_case_					    =					processor.batch_decode(_lowerCAmelCase    ,				padding_mask=_lowerCAmelCase  )

     self.assertIsInstance(_lowerCAmelCase    ,				_lowerCAmelCase  )

     for audio in decoded_audios:
        self.assertIsInstance(_lowerCAmelCase    ,				np.ndarray  )

     self.assertTrue(decoded_audios[0].shape == (1, 1_0)  )
     self.assertTrue(decoded_audios[1].shape == (1, 1_5)  )
     self.assertTrue(decoded_audios[2].shape == (1, 2_0)  )




