

'''simple docstring'''
import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch
@is_pipeline_test
class 					_snake_case		(unittest.TestCase):
			__A  :	int        =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			__A  :	Optional[int]        =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      int						=  TextaTextGenerationPipeline(model=_snake_case			,tokenizer=_snake_case     )
				return generator, ["Something to write", "Something else"]






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Optional[int]						=  generator("Something there"     )
				self.assertEqual(_snake_case			,[{"generated_text": ANY(_snake_case     )}]     )
				# These are encoder decoder, they don't just append to incoming string
				self.assertFalse(outputs[0]["generated_text"].startswith("Something there"     )     )

				UpperCAmelCase_     :      List[str]						=  generator(["This is great !", "Something else"]			,num_return_sequences=2			,do_sample=_snake_case     )
				self.assertEqual(
				    _snake_case			,[
				        [{"generated_text": ANY(_snake_case     )}, {"generated_text": ANY(_snake_case     )}],
				        [{"generated_text": ANY(_snake_case     )}, {"generated_text": ANY(_snake_case     )}],
				    ]			,)

				UpperCAmelCase_     :      Union[str, Any]						=  generator(
				    ["This is great !", "Something else"]			,num_return_sequences=2			,batch_size=2			,do_sample=_snake_case     )
				self.assertEqual(
				    _snake_case			,[
				        [{"generated_text": ANY(_snake_case     )}, {"generated_text": ANY(_snake_case     )}],
				        [{"generated_text": ANY(_snake_case     )}, {"generated_text": ANY(_snake_case     )}],
				    ]			,)

				with self.assertRaises(_snake_case     ):
					generator(4     )






			@require_torch
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  pipeline("text2text-generation"			,model="patrickvonplaten/t5-tiny-random"			,framework="pt"     )
				# do_sample=False necessary for reproducibility
				UpperCAmelCase_     :      Dict						=  generator("Something there"			,do_sample=_snake_case     )
				self.assertEqual(_snake_case			,[{"generated_text": ""}]     )

				UpperCAmelCase_     :      str						=  3
				UpperCAmelCase_     :      str						=  generator(
				    "Something there"			,num_return_sequences=_snake_case			,num_beams=_snake_case			,)
				UpperCAmelCase_     :      Tuple						=  [
				    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
				    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
				    {"generated_text": ""},
				]
				self.assertEqual(_snake_case			,_snake_case     )

				UpperCAmelCase_     :      Optional[Any]						=  generator("This is a test"			,do_sample=_snake_case			,num_return_sequences=2			,return_tensors=_snake_case     )
				self.assertEqual(
				    _snake_case			,[
				        {"generated_token_ids": ANY(torch.Tensor     )},
				        {"generated_token_ids": ANY(torch.Tensor     )},
				    ]			,)
				UpperCAmelCase_     :      int						=  generator.model.config.eos_token_id
				UpperCAmelCase_     :      List[str]						=  "<pad>"
				UpperCAmelCase_     :      List[str]						=  generator(
				    ["This is a test", "This is a second test"]			,do_sample=_snake_case			,num_return_sequences=2			,batch_size=2			,return_tensors=_snake_case			,)
				self.assertEqual(
				    _snake_case			,[
				        [
				            {"generated_token_ids": ANY(torch.Tensor     )},
				            {"generated_token_ids": ANY(torch.Tensor     )},
				        ],
				        [
				            {"generated_token_ids": ANY(torch.Tensor     )},
				            {"generated_token_ids": ANY(torch.Tensor     )},
				        ],
				    ]			,)






			@require_tf
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  pipeline("text2text-generation"			,model="patrickvonplaten/t5-tiny-random"			,framework="tf"     )
				# do_sample=False necessary for reproducibility
				UpperCAmelCase_     :      Optional[Any]						=  generator("Something there"			,do_sample=_snake_case     )
				self.assertEqual(_snake_case			,[{"generated_text": ""}]     )








'''simple docstring'''






import argparse


lowercase				='docs/source/_static/js/custom.js'





def lowerCamelCase__     (		__lowerCamelCase			:				List[Any]				):







   '''simple docstring'''




   with open(__lowerCamelCase			,       encoding='utf-8'			,       newline='\n'				) as f:
      _UpperCAmelCase  :  Optional[Any]				       =f.readlines()
   _UpperCAmelCase  :  Dict				       =0

   # First let's put the right version
   while not lines[index].startswith('const stableVersion ='				):
      index += 1
   _UpperCAmelCase  :  Tuple				       =f"const stableVersion = \"v{version}\"\n"

   # Then update the dictionary
   while not lines[index].startswith('const versionMapping = {'				):
      index += 1

   # We go until the end
   while not lines[index].startswith('}'				):
      index += 1
   # We add the new version at the end
   lines[index - 1] += f"    \"v{version}\": \"v{version}\",\n"

   with open(__lowerCamelCase			,       'w'			,       encoding='utf-8'			,       newline='\n'				) as f:
      f.writelines(__lowerCamelCase				)


if __name__ == "__main__":
 lowercase				=argparse.ArgumentParser()
 parser.add_argument('--version', help='Release version.')
 lowercase				=parser.parse_args()
 update_custom_js(args.version)

