



"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list






class 			UpperCAmelCase_						(       unittest.TestCase		):



      def 		snake_case_      (  self  :			List[str]		):
           _UpperCAmelCase    :			List[str]              =					"<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" "		)
           _UpperCAmelCase    :			Dict              =					dict(zip(A  ,			range(len(A		)		)		)		)

           _UpperCAmelCase    :			List[str]              =					{
               "pad_token": "<pad>",
               "unk_token": "<unk>",
               "bos_token": "<s>",
               "eos_token": "</s>",
           }
           _UpperCAmelCase    :			Dict              =					{
               "feature_size": 1,
               "padding_value": 0.0,
               "sampling_rate": 1_6_0_0_0,
               "return_attention_mask": False,
               "do_normalize": True,
           }

           _UpperCAmelCase    :			Tuple              =					tempfile.mkdtemp()
           _UpperCAmelCase    :			List[str]              =					os.path.join(self.tmpdirname  ,			VOCAB_FILES_NAMES["vocab_file"]		)
           _UpperCAmelCase    :			Dict              =					os.path.join(self.tmpdirname  ,			A		)
           with open(self.vocab_file  ,			"w"  ,			encoding="utf-8"		) as fp:
                fp.write(json.dumps(A		) + "\n"		)

           with open(self.feature_extraction_file  ,			"w"  ,			encoding="utf-8"		) as fp:
                fp.write(json.dumps(A		) + "\n"		)



      def 		snake_case_      (  self  :			Any  ,			**A  :			Tuple		):
           _UpperCAmelCase    :			Union[str, Any]              =					self.add_kwargs_tokens_map.copy()
           kwargs.update(A		)
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname  ,			**A		)



      def 		snake_case_      (  self  :			List[str]  ,			**A  :			Union[str, Any]		):
           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname  ,			**A		)



      def 		snake_case_      (  self  :			List[str]		):
           shutil.rmtree(self.tmpdirname		)



      def 		snake_case_      (  self  :			Tuple		):
           _UpperCAmelCase    :			List[str]              =					self.get_tokenizer()
           _UpperCAmelCase    :			Tuple              =					self.get_feature_extractor()

           _UpperCAmelCase    :			Dict              =					WavaVecaProcessor(tokenizer=A  ,			feature_extractor=A		)

           processor.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase    :			List[Any]              =					WavaVecaProcessor.from_pretrained(self.tmpdirname		)

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

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



      def 		snake_case_      (  self  :			List[Any]		):
           _UpperCAmelCase    :			Optional[int]              =					WavaVecaProcessor(tokenizer=self.get_tokenizer()  ,			feature_extractor=self.get_feature_extractor()		)
           processor.save_pretrained(self.tmpdirname		)

           _UpperCAmelCase    :			List[Any]              =					self.get_tokenizer(bos_token="(BOS)"  ,			eos_token="(EOS)"		)
           _UpperCAmelCase    :			int              =					self.get_feature_extractor(do_normalize=A  ,			padding_value=1.0		)

           _UpperCAmelCase    :			Union[str, Any]              =					WavaVecaProcessor.from_pretrained(
               self.tmpdirname  ,			bos_token="(BOS)"  ,			eos_token="(EOS)"  ,			do_normalize=A  ,			padding_value=1.0		)

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

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



      def 		snake_case_      (  self  :			Union[str, Any]		):
           _UpperCAmelCase    :			List[Any]              =					self.get_feature_extractor()
           _UpperCAmelCase    :			Dict              =					self.get_tokenizer()

           _UpperCAmelCase    :			Tuple              =					WavaVecaProcessor(tokenizer=A  ,			feature_extractor=A		)

           _UpperCAmelCase    :			Union[str, Any]              =					floats_list((3, 1_0_0_0)		)

           _UpperCAmelCase    :			Optional[int]              =					feature_extractor(A  ,			return_tensors="np"		)
           _UpperCAmelCase    :			Dict              =					processor(A  ,			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 		snake_case_      (  self  :			str		):
           _UpperCAmelCase    :			Dict              =					self.get_feature_extractor()
           _UpperCAmelCase    :			Any              =					self.get_tokenizer()

           _UpperCAmelCase    :			Dict              =					WavaVecaProcessor(tokenizer=A  ,			feature_extractor=A		)

           _UpperCAmelCase    :			Tuple              =					"This is a test string"

           _UpperCAmelCase    :			str              =					processor(text=A		)

           _UpperCAmelCase    :			List[Any]              =					tokenizer(A		)

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



      def 		snake_case_      (  self  :			str		):
           _UpperCAmelCase    :			Dict              =					self.get_feature_extractor()
           _UpperCAmelCase    :			Optional[Any]              =					self.get_tokenizer()

           _UpperCAmelCase    :			Union[str, Any]              =					WavaVecaProcessor(tokenizer=A  ,			feature_extractor=A		)

           _UpperCAmelCase    :			Optional[int]              =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _UpperCAmelCase    :			List[Any]              =					processor.batch_decode(A		)
           _UpperCAmelCase    :			str              =					tokenizer.batch_decode(A		)

           self.assertListEqual(A  ,			A		)



      def 		snake_case_      (  self  :			Optional[Any]		):
           _UpperCAmelCase    :			Any              =					self.get_feature_extractor()
           _UpperCAmelCase    :			str              =					self.get_tokenizer()

           _UpperCAmelCase    :			Optional[int]              =					WavaVecaProcessor(tokenizer=A  ,			feature_extractor=A		)

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





"""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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Dict:





     '''simple docstring'''

     _UpperCAmelCase    :			Any              =					{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
     _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					MODELS[model_size]  # set model hyperparameters
     _UpperCAmelCase    :			Tuple              =					ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE__		,						n_layer=SCREAMING_SNAKE_CASE__		,						n_head=SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase    :			int              =					ImageGPTForCausalLM(SCREAMING_SNAKE_CASE__       )

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

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


if __name__ == "__main__":
 _lowerCAmelCase       :      List[str]							=							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."
 )
 _lowerCAmelCase       :      Optional[int]							=							parser.parse_args()
 convert_imagegpt_checkpoint_to_pytorch(
     args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
 )
