

'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
class        A__   (   unittest.TestCase      ):
    lowercase						       =		MODEL_FOR_CAUSAL_LM_MAPPING
    lowercase						       =		TF_MODEL_FOR_CAUSAL_LM_MAPPING



    @require_torch
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   pipeline(task="""text-generation"""		,					model="""sshleifer/tiny-ctrl"""		,					framework="""pt"""							)
           # Using `do_sample=False` to force deterministic output
           A_						       =   text_generator("""This is a test"""		,					do_sample=UpperCamelCase__							)
           self.assertEqual(
               UpperCamelCase__		,					[
                   {
                       """generated_text""": (
                           """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
                           """ oscope. FiliFili@@"""
                       )
                   }
               ]		,					)

           A_						       =   text_generator(["""This is a test""", """This is a second test"""]							)
           self.assertEqual(
               UpperCamelCase__		,					[
                   [
                       {
                           """generated_text""": (
                               """This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."""
                               """ oscope. FiliFili@@"""
                           )
                       }
                   ],
                   [
                       {
                           """generated_text""": (
                               """This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"""
                               """ oscope. oscope. FiliFili@@"""
                           )
                       }
                   ],
               ]		,					)

           A_						       =   text_generator("""This is a test"""		,					do_sample=UpperCamelCase__		,					num_return_sequences=2		,					return_tensors=UpperCamelCase__							)
           self.assertEqual(
               UpperCamelCase__		,					[
                   {"""generated_token_ids""": ANY(UpperCamelCase__							)},
                   {"""generated_token_ids""": ANY(UpperCamelCase__							)},
               ]		,					)
           A_						       =   text_generator.model.config.eos_token_id
           A_						       =   """<pad>"""
           A_						       =   text_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(UpperCamelCase__							)},
                       {"""generated_token_ids""": ANY(UpperCamelCase__							)},
                   ],
                   [
                       {"""generated_token_ids""": ANY(UpperCamelCase__							)},
                       {"""generated_token_ids""": ANY(UpperCamelCase__							)},
                   ],
               ]		,					)



    @require_tf
    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   pipeline(task="""text-generation"""		,					model="""sshleifer/tiny-ctrl"""		,					framework="""tf"""							)

           # Using `do_sample=False` to force deterministic output
           A_						       =   text_generator("""This is a test"""		,					do_sample=UpperCamelCase__							)
           self.assertEqual(
               UpperCamelCase__		,					[
                   {
                       """generated_text""": (
                           """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
                           """ please,"""
                       )
                   }
               ]		,					)

           A_						       =   text_generator(["""This is a test""", """This is a second test"""]		,					do_sample=UpperCamelCase__							)
           self.assertEqual(
               UpperCamelCase__		,					[
                   [
                       {
                           """generated_text""": (
                               """This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"""
                               """ please,"""
                           )
                       }
                   ],
                   [
                       {
                           """generated_text""": (
                               """This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"""
                               """ Cannes 閲閲Cannes Cannes Cannes 攵 please,"""
                           )
                       }
                   ],
               ]		,					)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Optional[int]:





           '''simple docstring'''
           A_						       =   TextGenerationPipeline(model=UpperCamelCase__		,					tokenizer=UpperCamelCase__							)
           return text_generator, ["This is a test", "Another test"]



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   """Hello I believe in"""
           A_						       =   pipeline("""text-generation"""		,					model="""hf-internal-testing/tiny-random-gpt2"""							)
           A_						       =   text_generator(UpperCamelCase__							)
           self.assertEqual(
               UpperCamelCase__		,					[{"""generated_text""": """Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"""}]		,					)

           A_						       =   text_generator(UpperCamelCase__		,					stop_sequence=""" fe"""							)
           self.assertEqual(UpperCamelCase__		,					[{"""generated_text""": """Hello I believe in fe"""}]							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Optional[int]:





           '''simple docstring'''
           A_						       =   text_generator.model
           A_						       =   text_generator.tokenizer

           A_						       =   text_generator("""This is a test"""							)
           self.assertEqual(UpperCamelCase__		,					[{"""generated_text""": ANY(UpperCamelCase__							)}]							)
           self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""							)							)

           A_						       =   text_generator("""This is a test"""		,					return_full_text=UpperCamelCase__							)
           self.assertEqual(UpperCamelCase__		,					[{"""generated_text""": ANY(UpperCamelCase__							)}]							)
           self.assertNotIn("""This is a test"""		,					outputs[0]["""generated_text"""]							)

           A_						       =   pipeline(task="""text-generation"""		,					model=UpperCamelCase__		,					tokenizer=UpperCamelCase__		,					return_full_text=UpperCamelCase__							)
           A_						       =   text_generator("""This is a test"""							)
           self.assertEqual(UpperCamelCase__		,					[{"""generated_text""": ANY(UpperCamelCase__							)}]							)
           self.assertNotIn("""This is a test"""		,					outputs[0]["""generated_text"""]							)

           A_						       =   text_generator("""This is a test"""		,					return_full_text=UpperCamelCase__							)
           self.assertEqual(UpperCamelCase__		,					[{"""generated_text""": ANY(UpperCamelCase__							)}]							)
           self.assertTrue(outputs[0]["""generated_text"""].startswith("""This is a test"""							)							)

           A_						       =   text_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__							)}],
               ]		,					)

           if text_generator.tokenizer.pad_token is not None:
                  A_						       =   text_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__							):
                  A_						       =   text_generator("""test"""		,					return_full_text=UpperCamelCase__		,					return_text=UpperCamelCase__							)
           with self.assertRaises(UpperCamelCase__							):
                  A_						       =   text_generator("""test"""		,					return_full_text=UpperCamelCase__		,					return_tensors=UpperCamelCase__							)
           with self.assertRaises(UpperCamelCase__							):
                  A_						       =   text_generator("""test"""		,					return_text=UpperCamelCase__		,					return_tensors=UpperCamelCase__							)

           # Empty prompt is slighly special
           # it requires BOS token to exist.
           # Special case for Pegasus which will always append EOS so will
           # work even without BOS.
           if (
               text_generator.tokenizer.bos_token_id is not None
               or "Pegasus" in tokenizer.__class__.__name__
               or "Git" in model.__class__.__name__
           ):
                  A_						       =   text_generator(""""""							)
                  self.assertEqual(UpperCamelCase__		,					[{"""generated_text""": ANY(UpperCamelCase__							)}]							)
           else:
                  with self.assertRaises((ValueError, AssertionError)							):
                         A_						       =   text_generator(""""""							)

           if text_generator.framework == "tf":
                  # TF generation does not support max_new_tokens, and it's impossible
                  # to control long generation with only max_length without
                  # fancy calculation, dismissing tests for now.
                  return
           # We don't care about infinite range models.
           # They already work.
           # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
           A_						       =   ["""RwkvForCausalLM""", """XGLMForCausalLM""", """GPTNeoXForCausalLM"""]
           if (
               tokenizer.model_max_length < 10000
               and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
           ):
                  # Handling of large generations
                  with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)							):
                         text_generator("""This is a test""" * 500		,					max_new_tokens=20							)

                  A_						       =   text_generator("""This is a test""" * 500		,					handle_long_generation="""hole"""		,					max_new_tokens=20							)
                  # Hole strategy cannot work
                  with self.assertRaises(UpperCamelCase__							):
                         text_generator(
                             """This is a test""" * 500		,					handle_long_generation="""hole"""		,					max_new_tokens=tokenizer.model_max_length + 10		,					)



    @require_torch
    @require_accelerate
    @require_torch_gpu
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           import torch

           # Classic `model_kwargs`
           A_						       =   pipeline(
               model="""hf-internal-testing/tiny-random-bloom"""		,					model_kwargs={"""device_map""": """auto""", """torch_dtype""": torch.bfloataa}		,					)
           self.assertEqual(pipe.model.device		,					torch.device(0							)							)
           self.assertEqual(pipe.model.lm_head.weight.dtype		,					torch.bfloataa							)
           A_						       =   pipe("""This is a test"""							)
           self.assertEqual(
               UpperCamelCase__		,					[
                   {
                       """generated_text""": (
                           """This is a test test test test test test test test test test test test test test test test"""
                           """ test"""
                       )
                   }
               ]		,					)

           # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
           A_						       =   pipeline(model="""hf-internal-testing/tiny-random-bloom"""		,					device_map="""auto"""		,					torch_dtype=torch.bfloataa							)
           self.assertEqual(pipe.model.device		,					torch.device(0							)							)
           self.assertEqual(pipe.model.lm_head.weight.dtype		,					torch.bfloataa							)
           A_						       =   pipe("""This is a test"""							)
           self.assertEqual(
               UpperCamelCase__		,					[
                   {
                       """generated_text""": (
                           """This is a test test test test test test test test test test test test test test test test"""
                           """ test"""
                       )
                   }
               ]		,					)

           # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
           A_						       =   pipeline(model="""hf-internal-testing/tiny-random-bloom"""		,					device_map="""auto"""							)
           self.assertEqual(pipe.model.device		,					torch.device(0							)							)
           self.assertEqual(pipe.model.lm_head.weight.dtype		,					torch.floataa							)
           A_						       =   pipe("""This is a test"""							)
           self.assertEqual(
               UpperCamelCase__		,					[
                   {
                       """generated_text""": (
                           """This is a test test test test test test test test test test test test test test test test"""
                           """ test"""
                       )
                   }
               ]		,					)



    @require_torch
    @require_torch_gpu
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           import torch

           A_						       =   pipeline(model="""hf-internal-testing/tiny-random-bloom"""		,					device=0		,					torch_dtype=torch.floataa							)
           pipe("""This is a test"""							)



    @require_torch
    @require_accelerate
    @require_torch_gpu
    def        snake_case_      (       self							)    ->   Optional[int]:





           '''simple docstring'''
           import torch

           A_						       =   pipeline(model="""hf-internal-testing/tiny-random-bloom"""		,					device_map="""auto"""		,					torch_dtype=torch.floataa							)
           pipe("""This is a test"""		,					do_sample=UpperCamelCase__		,					top_p=0.5							)







    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   """Hello world"""
           A_						       =   pipeline("""text-generation"""		,					model="""hf-internal-testing/tiny-random-gpt2"""							)
           if text_generator.model.framework == "tf":
                  A_						       =   logging.get_logger("""transformers.generation.tf_utils"""							)
           else:
                  A_						       =   logging.get_logger("""transformers.generation.utils"""							)
           A_						       =   """Both `max_new_tokens`"""  # The beggining of the message to be checked in this test

           # Both are set by the user -> log warning
           with CaptureLogger(UpperCamelCase__							) as cl:
                  A_						       =   text_generator(UpperCamelCase__		,					max_length=10		,					max_new_tokens=1							)
           self.assertIn(UpperCamelCase__		,					cl.out							)

           # The user only sets one -> no warning
           with CaptureLogger(UpperCamelCase__							) as cl:
                  A_						       =   text_generator(UpperCamelCase__		,					max_new_tokens=1							)
           self.assertNotIn(UpperCamelCase__		,					cl.out							)

           with CaptureLogger(UpperCamelCase__							) as cl:
                  A_						       =   text_generator(UpperCamelCase__		,					max_length=10							)
           self.assertNotIn(UpperCamelCase__		,					cl.out							)



'''simple docstring'''







# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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


__lowerCamelCase									=     '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def    UpperCAmelCase__     (   UpperCAmelCase__=None					)      ->      List[str]:
       if subparsers is not None:
              A_						       =   subparsers.add_parser("""tpu-config""",							description=_description					)
       else:
              A_						       =   argparse.ArgumentParser("""Accelerate tpu-config command""",							description=_description					)
       # Core arguments
       A_						       =   parser.add_argument_group(
           """Config Arguments""",							"""Arguments that can be configured through `accelerate config`."""					)
       config_args.add_argument(
           """--config_file""",							type=UpperCAmelCase__,							default=UpperCAmelCase__,							help="""Path to the config file to use for accelerate.""",							)
       config_args.add_argument(
           """--tpu_name""",							default=UpperCAmelCase__,							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=UpperCAmelCase__,							help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file.""",							)
       A_						       =   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=UpperCAmelCase__,							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=UpperCAmelCase__					)
       return parser
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      List[Any]:
       A_						       =   None

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

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

       # Then send it to gcloud
       # Eventually try to use google-api-core to do this instead of subprocess
       A_						       =   ["""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(UpperCAmelCase__					)}'''					)
              return
       subprocess.run(UpperCAmelCase__					)
       print("""Successfully setup pod."""					)







def    UpperCAmelCase__     (   )      ->      int:
       A_						       =   tpu_command_parser()
       A_						       =   parser.parse_args()

       tpu_command_launcher(UpperCAmelCase__					)
