


import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM







@require_torch
class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase=99					,  _lowerCamelCase=13					,  _lowerCamelCase=16					,  _lowerCamelCase=7					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase=False					,  _lowerCamelCase=True					,  _lowerCamelCase=2					,  _lowerCamelCase=32					,  _lowerCamelCase=4					,  _lowerCamelCase=4					,  _lowerCamelCase=30					,  _lowerCamelCase=0					,  _lowerCamelCase=1					,  _lowerCamelCase=2					,  _lowerCamelCase=None					,  ):
       a						:Union[str, Any] 							=       parent
       a						:Dict 							=       batch_size
       a						:Tuple 							=       decoder_seq_length
       # For common tests
       a						:int 							=       self.decoder_seq_length
       a						:Optional[int] 							=       is_training
       a						:Optional[Any] 							=       use_attention_mask
       a						:Tuple 							=       use_labels

       a						:Any 							=       vocab_size
       a						:Union[str, Any] 							=       d_model
       a						:str 							=       d_model
       a						:int 							=       decoder_layers
       a						:Tuple 							=       decoder_layers
       a						:Optional[int] 							=       decoder_ffn_dim
       a						:str 							=       decoder_attention_heads
       a						:Optional[int] 							=       decoder_attention_heads
       a						:List[Any] 							=       eos_token_id
       a						:Tuple 							=       bos_token_id
       a						:Any 							=       pad_token_id
       a						:Union[str, Any] 							=       decoder_start_token_id
       a						:Optional[Any] 							=       use_cache
       a						:Optional[Any] 							=       max_position_embeddings

       a						:Dict 							=       None
       a						:Optional[Any] 							=       decoder_seq_length
       a						:Optional[int] 							=       2
       a						:int 							=       1




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       ids_tensor([self.batch_size, self.decoder_seq_length]					,  self.vocab_size    )

       a						:int 							=       None
       if self.use_attention_mask:
             a						:Optional[int] 							=       ids_tensor([self.batch_size, self.decoder_seq_length]					,  vocab_size=2    )

       a						:Dict 							=       None
       if self.use_labels:
             a						:Any 							=       ids_tensor([self.batch_size, self.decoder_seq_length]					,  self.vocab_size    )

       a						:List[Any] 							=       TrOCRConfig(
           vocab_size=self.vocab_size					,  d_model=self.d_model					,  decoder_layers=self.decoder_layers					,  decoder_ffn_dim=self.decoder_ffn_dim					,  decoder_attention_heads=self.decoder_attention_heads					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.bos_token_id					,  use_cache=self.use_cache					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.decoder_start_token_id					,  max_position_embeddings=self.max_position_embeddings					,  )

       return (config, input_ids, attention_mask, lm_labels)




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  ):
       a						:int 							=       True
       a						:Any 							=       TrOCRDecoder(config=_lowerCamelCase    ).to(_lowerCamelCase    ).eval()
       a						:Tuple 							=       input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       a						:Any 							=       model(_lowerCamelCase					,  use_cache=_lowerCamelCase    )
       a						:List[Any] 							=       model(_lowerCamelCase    )
       a						:Union[str, Any] 							=       model(_lowerCamelCase					,  use_cache=_lowerCamelCase    )

       self.parent.assertTrue(len(_lowerCamelCase    ) == len(_lowerCamelCase    )    )
       self.parent.assertTrue(len(_lowerCamelCase    ) == len(_lowerCamelCase    ) + 1    )

       a						:Optional[Any] 							=       outputs['''past_key_values''']

       # create hypothetical next token and extent to next_input_ids
       a						:int 							=       ids_tensor((2, 1)					,  config.vocab_size - 1    ) + 1

       # append to next input_ids and
       a						:str 							=       torch.cat([input_ids, next_tokens]					,  dim=-1    )

       a						:List[Any] 							=       model(_lowerCamelCase    )['''last_hidden_state''']
       a						:List[Any] 							=       model(_lowerCamelCase					,  past_key_values=_lowerCamelCase    )['''last_hidden_state''']

       # select random slice
       a						:List[str] 							=       ids_tensor((1,)					,  output_from_past.shape[-1]    ).item()
       a						:Optional[int] 							=       output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
       a						:List[Any] 							=       output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       assert torch.allclose(_lowerCamelCase					,  _lowerCamelCase					,  atol=1e-3    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       self.prepare_config_and_inputs()
       a							,				a							,				a							,				a						:Union[str, Any] 							=       config_and_inputs

       a						:Optional[Any] 							=       {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict







@require_torch
class 		_snake_case  (      _snake_case						,    _snake_case						,    _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
 SCREAMING_SNAKE_CASE__      =       (TrOCRForCausalLM,) if is_torch_available() else ()
 SCREAMING_SNAKE_CASE__      =       {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
 SCREAMING_SNAKE_CASE__      =       True
 SCREAMING_SNAKE_CASE__      =       False




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[int] 							=       TrOCRStandaloneDecoderModelTester(self					,  is_training=_lowerCamelCase    )
       a						:Tuple 							=       ConfigTester(self					,  config_class=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       self.config_tester.run_common_tests()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       return




 @unittest.skip('''The model doesn\'t support left padding'''    )  # and it's not used enough to be worth fixing :)
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass








'''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 		_A     (    A__=None      ):






 """simple docstring"""



 if subparsers is not None:
  __lowercase							  = subparsers.add_parser('''tpu-config'''     ,				description=_description      )
 else:
  __lowercase							  = argparse.ArgumentParser('''Accelerate tpu-config command'''     ,				description=_description      )
 # Core arguments
 __lowercase							  = parser.add_argument_group(
     '''Config Arguments'''     ,				'''Arguments that can be configured through `accelerate config`.'''      )
 config_args.add_argument(
     '''--config_file'''     ,				type=A__     ,				default=A__     ,				help='''Path to the config file to use for accelerate.'''     ,				)
 config_args.add_argument(
     '''--tpu_name'''     ,				default=A__     ,				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=A__     ,				help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''     ,				)
 __lowercase							  = 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=A__     ,				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=A__      )
 return parser


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = None

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

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

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






def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = tpu_command_parser()
 __lowercase							  = parser.parse_args()

 tpu_command_launcher(A__      )

