


import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


A_								=   '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''







def 				UpperCAmelCase					(  UpperCAmelCase=None   )->		Union[str, Any]:







					'''simple docstring'''



					if subparsers is not None:
										SCREAMING_SNAKE_CASE_						   =							subparsers.add_parser('''tpu-config'''					,description=_description   )
					else:
										SCREAMING_SNAKE_CASE_						   =							argparse.ArgumentParser('''Accelerate tpu-config command'''					,description=_description   )
					# Core arguments
					SCREAMING_SNAKE_CASE_						   =							parser.add_argument_group(
					    '''Config Arguments'''					,'''Arguments that can be configured through `accelerate config`.'''   )
					config_args.add_argument(
					    '''--config_file'''					,type=__snake_case					,default=__snake_case					,help='''Path to the config file to use for accelerate.'''					,)
					config_args.add_argument(
					    '''--tpu_name'''					,default=__snake_case					,help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''					,)
					config_args.add_argument(
					    '''--tpu_zone'''					,default=__snake_case					,help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''					,)
					SCREAMING_SNAKE_CASE_						   =							parser.add_argument_group('''TPU Arguments'''					,'''Arguments for options ran inside the TPU.'''   )
					pod_args.add_argument(
					    '''--use_alpha'''					,action='''store_true'''					,help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''					,)
					pod_args.add_argument(
					    '''--command_file'''					,default=__snake_case					,help='''The path to the file containing the commands to run on the pod on startup.'''					,)
					pod_args.add_argument(
					    '''--command'''					,action='''append'''					,nargs='''+'''					,help='''A command to run on the pod. Can be passed multiple times.'''					,)
					pod_args.add_argument(
					    '''--install_accelerate'''					,action='''store_true'''					,help='''Whether to install accelerate on the pod. Defaults to False.'''					,)
					pod_args.add_argument(
					    '''--accelerate_version'''					,default='''latest'''					,help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''					,)
					pod_args.add_argument(
					    '''--debug'''					,action='''store_true'''					,help='''If set, will print the command that would be run instead of running it.'''   )

					if subparsers is not None:
										parser.set_defaults(func=__snake_case   )
					return parser







def 				UpperCAmelCase					(  UpperCAmelCase   )->		Union[str, Any]:







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_						   =							None

					# Get the default from the config file if it exists.
					if args.config_file is not None or os.path.isfile(__snake_case   ):
										SCREAMING_SNAKE_CASE_						   =							load_config_from_file(args.config_file   )
										if not args.command_file and defaults.command_file is not None and not args.command:
															SCREAMING_SNAKE_CASE_						   =							defaults.command_file
										if not args.command and defaults.commands is not None:
															SCREAMING_SNAKE_CASE_						   =							defaults.commands
										if not args.tpu_name:
															SCREAMING_SNAKE_CASE_						   =							defaults.tpu_name
										if not args.tpu_zone:
															SCREAMING_SNAKE_CASE_						   =							defaults.tpu_zone
					if args.accelerate_version == "dev":
										SCREAMING_SNAKE_CASE_						   =							'''git+https://github.com/huggingface/accelerate.git'''
					elif args.accelerate_version == "latest":
										SCREAMING_SNAKE_CASE_						   =							'''accelerate -U'''
					elif isinstance(parse(args.accelerate_version   )					,__snake_case   ):
										SCREAMING_SNAKE_CASE_						   =							f'''accelerate=={args.accelerate_version}'''

					if not args.command_file and not args.command:
										raise ValueError('''You must specify either a command file or a command to run on the pod.'''   )

					if args.command_file:
										with open(args.command_file					,'''r'''   ) as f:
															SCREAMING_SNAKE_CASE_						   =							[f.read().splitlines()]

    # To turn list of lists into list of strings
					if isinstance(args.command[0]					,__snake_case   ):
										SCREAMING_SNAKE_CASE_						   =							[line for cmd in args.command for line in cmd]
					# Default to the shared folder and install accelerate
					SCREAMING_SNAKE_CASE_						   =							['''cd /usr/share''']
					if args.install_accelerate:
										new_cmd += [f'''pip install {args.accelerate_version}''']
					new_cmd += args.command
					SCREAMING_SNAKE_CASE_						   =							'''; '''.join(__snake_case   )

					# Then send it to gcloud
					# Eventually try to use google-api-core to do this instead of subprocess
					SCREAMING_SNAKE_CASE_						   =							['''gcloud''']
					if args.use_alpha:
										cmd += ["alpha"]
					cmd += [
					    "compute",
					    "tpus",
					    "tpu-vm",
					    "ssh",
					    args.tpu_name,
					    "--zone",
					    args.tpu_zone,
					    "--command",
					    args.command,
					    "--worker",
					    "all",
					]
					if args.debug:
										print(f'''Running {' '.join(__snake_case   )}'''   )
										return
					subprocess.run(__snake_case   )
					print('''Successfully setup pod.'''   )







def 				UpperCAmelCase					(  )->		str:







					'''simple docstring'''



					SCREAMING_SNAKE_CASE_						   =							tpu_command_parser()
					SCREAMING_SNAKE_CASE_						   =							parser.parse_args()

					tpu_command_launcher(__snake_case   )



'''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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __lowerCAmelCase         =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any     )  ->			List[str]:
        _A    		=  TextaTextGenerationPipeline(model=UpperCamelCase__, tokenizer=UpperCamelCase__     )
        return generator, ["Something to write", "Something else"]
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[Any]     )  ->			List[str]:
        _A    		=  generator('Something there'     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ANY(UpperCamelCase__     )}]     )
        # These are encoder decoder, they don't just append to incoming string
        self.assertFalse(outputs[0]['generated_text'].startswith('Something there'     )     )

        _A    		=  generator(['This is great !', 'Something else'], num_return_sequences=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        _A    		=  generator(
            ['This is great !', 'Something else'], num_return_sequences=2, batch_size=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        with self.assertRaises(UpperCamelCase__     ):
             generator(4     )
   @require_torch
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='pt'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )

        _A    		=  3
        _A    		=  generator(
            'Something there', num_return_sequences=UpperCamelCase__, num_beams=UpperCamelCase__, )
        _A    		=  [
            {'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(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  generator('This is a test', do_sample=UpperCamelCase__, num_return_sequences=2, return_tensors=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                {'generated_token_ids': ANY(torch.Tensor     )},
                {'generated_token_ids': ANY(torch.Tensor     )},
            ], )
        _A    		=  generator.model.config.eos_token_id
        _A    		=  '<pad>'
        _A    		=  generator(
            ['This is a test', 'This is a second test'], do_sample=UpperCamelCase__, num_return_sequences=2, batch_size=2, return_tensors=UpperCamelCase__, )
        self.assertEqual(
            UpperCamelCase__, [
                [
                    {'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      :			Optional[Any]     )  ->			Optional[int]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='tf'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )



