
"""simple docstring"""






from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TFConvBertForMaskedLM,
				    TFConvBertForMultipleChoice,
				    TFConvBertForQuestionAnswering,
				    TFConvBertForSequenceClassification,
				    TFConvBertForTokenClassification,
				    TFConvBertModel,
				)




class 			_SCREAMING_SNAKE_CASE					:






			def __init__(					self       ,   __A       ,   __A=13       ,   __A=7       ,   __A=True       ,   __A=True       ,   __A=True       ,   __A=True       ,   __A=99       ,   __A=32       ,   __A=2       ,   __A=4       ,   __A=37       ,   __A="gelu"       ,   __A=0.1       ,   __A=0.1       ,   __A=512       ,   __A=16       ,   __A=2       ,   __A=0.0_2       ,   __A=3       ,   __A=4       ,   __A=None       ,   )			->     Union[str, Any]:
						lowerCAmelCase_    :str			     =  parent
						lowerCAmelCase_    :str			     =  13
						lowerCAmelCase_    :Tuple			     =  7
						lowerCAmelCase_    :Tuple			     =  True
						lowerCAmelCase_    :List[str]			     =  True
						lowerCAmelCase_    :int			     =  True
						lowerCAmelCase_    :str			     =  True
						lowerCAmelCase_    :List[str]			     =  99
						lowerCAmelCase_    :Tuple			     =  384
						lowerCAmelCase_    :Any			     =  2
						lowerCAmelCase_    :int			     =  4
						lowerCAmelCase_    :Any			     =  37
						lowerCAmelCase_    :Any			     =  """gelu"""
						lowerCAmelCase_    :Tuple			     =  0.1
						lowerCAmelCase_    :str			     =  0.1
						lowerCAmelCase_    :Dict			     =  512
						lowerCAmelCase_    :Dict			     =  16
						lowerCAmelCase_    :Tuple			     =  2
						lowerCAmelCase_    :List[Any]			     =  0.0_2
						lowerCAmelCase_    :Optional[int]			     =  3
						lowerCAmelCase_    :Tuple			     =  4
						lowerCAmelCase_    :Tuple			     =  128
						lowerCAmelCase_    :Any			     =  2
						lowerCAmelCase_    :Optional[Any]			     =  9
						lowerCAmelCase_    :List[str]			     =  1
						lowerCAmelCase_    :List[str]			     =  None






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :List[str]			     =  ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size       )

						lowerCAmelCase_    :int			     =  None
						if self.use_input_mask:
									lowerCAmelCase_    :str			     =  random_attention_mask([self.batch_size, self.seq_length]       )

						lowerCAmelCase_    :List[str]			     =  None
						if self.use_token_type_ids:
									lowerCAmelCase_    :str			     =  ids_tensor([self.batch_size, self.seq_length]       ,   self.type_vocab_size       )

						lowerCAmelCase_    :Union[str, Any]			     =  None
						lowerCAmelCase_    :int			     =  None
						lowerCAmelCase_    :Optional[Any]			     =  None
						if self.use_labels:
									lowerCAmelCase_    :Dict			     =  ids_tensor([self.batch_size]       ,   self.type_sequence_label_size       )
									lowerCAmelCase_    :Optional[int]			     =  ids_tensor([self.batch_size, self.seq_length]       ,   self.num_labels       )
									lowerCAmelCase_    :str			     =  ids_tensor([self.batch_size]       ,   self.num_choices       )

						lowerCAmelCase_    :List[Any]			     =  ConvBertConfig(
						    vocab_size=self.vocab_size       ,   hidden_size=self.hidden_size       ,   num_hidden_layers=self.num_hidden_layers       ,   num_attention_heads=self.num_attention_heads       ,   intermediate_size=self.intermediate_size       ,   hidden_act=self.hidden_act       ,   hidden_dropout_prob=self.hidden_dropout_prob       ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,   max_position_embeddings=self.max_position_embeddings       ,   type_vocab_size=self.type_vocab_size       ,   initializer_range=self.initializer_range       ,   return_dict=__A       ,   )

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     Union[str, Any]:
						lowerCAmelCase_    :Optional[int]			     =  TFConvBertModel(config=__A       )
						lowerCAmelCase_    :Any			     =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

						lowerCAmelCase_    :List[str]			     =  [input_ids, input_mask]
						lowerCAmelCase_    :List[str]			     =  model(__A       )

						lowerCAmelCase_    :str			     =  model(__A       )

						self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size)       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     str:
						lowerCAmelCase_    :List[Any]			     =  TFConvBertForMaskedLM(config=__A       )
						lowerCAmelCase_    :Dict			     =  {
						    """input_ids""": input_ids,
						    """attention_mask""": input_mask,
						    """token_type_ids""": token_type_ids,
						}
						lowerCAmelCase_    :Optional[int]			     =  model(__A       )
						self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.vocab_size)       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     int:
						lowerCAmelCase_    :Any			     =  self.num_labels
						lowerCAmelCase_    :List[str]			     =  TFConvBertForSequenceClassification(config=__A       )
						lowerCAmelCase_    :List[Any]			     =  {
						    """input_ids""": input_ids,
						    """attention_mask""": input_mask,
						    """token_type_ids""": token_type_ids,
						}

						lowerCAmelCase_    :Dict			     =  model(__A       )
						self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     Dict:
						lowerCAmelCase_    :Any			     =  self.num_choices
						lowerCAmelCase_    :int			     =  TFConvBertForMultipleChoice(config=__A       )
						lowerCAmelCase_    :List[str]			     =  tf.tile(tf.expand_dims(__A       ,   1       )       ,   (1, self.num_choices, 1)       )
						lowerCAmelCase_    :Tuple			     =  tf.tile(tf.expand_dims(__A       ,   1       )       ,   (1, self.num_choices, 1)       )
						lowerCAmelCase_    :Dict			     =  tf.tile(tf.expand_dims(__A       ,   1       )       ,   (1, self.num_choices, 1)       )
						lowerCAmelCase_    :Tuple			     =  {
						    """input_ids""": multiple_choice_inputs_ids,
						    """attention_mask""": multiple_choice_input_mask,
						    """token_type_ids""": multiple_choice_token_type_ids,
						}
						lowerCAmelCase_    :Any			     =  model(__A       )
						self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_choices)       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     Union[str, Any]:
						lowerCAmelCase_    :Optional[Any]			     =  self.num_labels
						lowerCAmelCase_    :Tuple			     =  TFConvBertForTokenClassification(config=__A       )
						lowerCAmelCase_    :Union[str, Any]			     =  {
						    """input_ids""": input_ids,
						    """attention_mask""": input_mask,
						    """token_type_ids""": token_type_ids,
						}
						lowerCAmelCase_    :Dict			     =  model(__A       )
						self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.num_labels)       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     Optional[int]:
						lowerCAmelCase_    :str			     =  TFConvBertForQuestionAnswering(config=__A       )
						lowerCAmelCase_    :List[Any]			     =  {
						    """input_ids""": input_ids,
						    """attention_mask""": input_mask,
						    """token_type_ids""": token_type_ids,
						}

						lowerCAmelCase_    :int			     =  model(__A       )
						self.parent.assertEqual(result.start_logits.shape       ,   (self.batch_size, self.seq_length)       )
						self.parent.assertEqual(result.end_logits.shape       ,   (self.batch_size, self.seq_length)       )






			def  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_    :Union[str, Any]			     =  self.prepare_config_and_inputs()
						(
						    (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       
						)    :List[Any]			     =  config_and_inputs
						lowerCAmelCase_    :Union[str, Any]			     =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict




@require_tf
class 			_SCREAMING_SNAKE_CASE					(			A__	, A__	, unittest.TestCase							):
			UpperCAmelCase_  :Union[str, Any]				 =						(
			    (
			        TFConvBertModel,
			        TFConvBertForMaskedLM,
			        TFConvBertForQuestionAnswering,
			        TFConvBertForSequenceClassification,
			        TFConvBertForTokenClassification,
			        TFConvBertForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCAmelCase_  :str				 =						(
			    {
			        "feature-extraction": TFConvBertModel,
			        "fill-mask": TFConvBertForMaskedLM,
			        "question-answering": TFConvBertForQuestionAnswering,
			        "text-classification": TFConvBertForSequenceClassification,
			        "token-classification": TFConvBertForTokenClassification,
			        "zero-shot": TFConvBertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			UpperCAmelCase_  :Union[str, Any]				 =						False
			UpperCAmelCase_  :List[str]				 =						False
			UpperCAmelCase_  :Union[str, Any]				 =						False






			def  __lowerCAmelCase (					self       )			->     List[str]:
						lowerCAmelCase_    :List[str]			     =  TFConvBertModelTester(self       )
						lowerCAmelCase_    :Optional[int]			     =  ConfigTester(self       ,   config_class=__A       ,   hidden_size=37       )






			def  __lowerCAmelCase (					self       )			->     Dict:
						self.config_tester.run_common_tests()






			def  __lowerCAmelCase (					self       )			->     Dict:
						lowerCAmelCase_    :Optional[Any]			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__A       )






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :Union[str, Any]			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__A       )






			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :Union[str, Any]			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*__A       )






			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :int			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__A       )






			def  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_    :Any			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__A       )






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :Any			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__A       )






			@slow
			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_     ,       lowerCAmelCase_    :Optional[Any]			     =  self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase_    :Optional[Any]			     =  True
						lowerCAmelCase_    :Optional[Any]			     =  True

						if hasattr(__A       ,   """use_cache"""       ):
									lowerCAmelCase_    :Dict			     =  True

						lowerCAmelCase_    :Tuple			     =  getattr(self.model_tester       ,   """encoder_seq_length"""       ,   self.model_tester.seq_length       )
						lowerCAmelCase_    :str			     =  getattr(self.model_tester       ,   """key_length"""       ,   __A       )

						for model_class in self.all_model_classes:
									lowerCAmelCase_    :Tuple			     =  self._prepare_for_class(__A       ,   __A       )
									lowerCAmelCase_    :Optional[Any]			     =  model_class(__A       )
									lowerCAmelCase_    :List[str]			     =  len(model(__A       )       )

									with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(__A       ,   saved_model=__A       )
												lowerCAmelCase_    :Optional[int]			     =  os.path.join(__A       ,   """saved_model"""       ,   """1"""       )
												lowerCAmelCase_    :int			     =  tf.keras.models.load_model(__A       )
												lowerCAmelCase_    :Optional[int]			     =  model(__A       )

												if self.is_encoder_decoder:
															lowerCAmelCase_    :Optional[int]			     =  outputs["""encoder_hidden_states"""]
															lowerCAmelCase_    :int			     =  outputs["""encoder_attentions"""]
												else:
															lowerCAmelCase_    :int			     =  outputs["""hidden_states"""]
															lowerCAmelCase_    :Optional[int]			     =  outputs["""attentions"""]

												self.assertEqual(len(__A       )       ,   __A       )

												lowerCAmelCase_    :Union[str, Any]			     =  getattr(
												    self.model_tester       ,   """expected_num_hidden_layers"""       ,   self.model_tester.num_hidden_layers + 1       )

												self.assertEqual(len(__A       )       ,   __A       )
												self.assertListEqual(
												    list(output_hidden_states[0].shape[-2:]       )       ,   [self.model_tester.seq_length, self.model_tester.hidden_size]       ,   )

												self.assertEqual(len(__A       )       ,   self.model_tester.num_hidden_layers       )
												self.assertListEqual(
												    list(output_attentions[0].shape[-3:]       )       ,   [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]       ,   )






			@slow
			def  __lowerCAmelCase (					self       )			->     Tuple:
						lowerCAmelCase_    :Dict			     =  TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""       )
						self.assertIsNotNone(__A       )






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_     ,       lowerCAmelCase_    :Optional[int]			     =  self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase_    :List[str]			     =  True
						lowerCAmelCase_    :List[Any]			     =  getattr(self.model_tester       ,   """decoder_seq_length"""       ,   self.model_tester.seq_length       )
						lowerCAmelCase_    :Dict			     =  getattr(self.model_tester       ,   """encoder_seq_length"""       ,   self.model_tester.seq_length       )
						lowerCAmelCase_    :Tuple			     =  getattr(self.model_tester       ,   """key_length"""       ,   __A       )
						lowerCAmelCase_    :List[Any]			     =  getattr(self.model_tester       ,   """key_length"""       ,   __A       )

						def check_decoder_attentions_output(__A       ):
									lowerCAmelCase_    :List[str]			     =  len(__A       )
									self.assertEqual(out_len % 2       ,   0       )
									lowerCAmelCase_    :Tuple			     =  outputs.decoder_attentions
									self.assertEqual(len(__A       )       ,   self.model_tester.num_hidden_layers       )
									self.assertListEqual(
									    list(decoder_attentions[0].shape[-3:]       )       ,   [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]       ,   )

						def check_encoder_attentions_output(__A       ):
									lowerCAmelCase_    :List[str]			     =  [
									    t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
									]
									self.assertEqual(len(__A       )       ,   self.model_tester.num_hidden_layers       )
									self.assertListEqual(
									    list(attentions[0].shape[-3:]       )       ,   [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]       ,   )

						for model_class in self.all_model_classes:
									lowerCAmelCase_    :Optional[int]			     =  True
									lowerCAmelCase_    :Tuple			     =  False
									lowerCAmelCase_    :Optional[int]			     =  model_class(__A       )
									lowerCAmelCase_    :Union[str, Any]			     =  model(self._prepare_for_class(__A       ,   __A       )       )
									lowerCAmelCase_    :Any			     =  len(__A       )
									self.assertEqual(config.output_hidden_states       ,   __A       )
									check_encoder_attentions_output(__A       )

									if self.is_encoder_decoder:
												lowerCAmelCase_    :Any			     =  model_class(__A       )
												lowerCAmelCase_    :Optional[Any]			     =  model(self._prepare_for_class(__A       ,   __A       )       )
												self.assertEqual(config.output_hidden_states       ,   __A       )
												check_decoder_attentions_output(__A       )

									# Check that output attentions can also be changed via the config
									del inputs_dict["output_attentions"]
									lowerCAmelCase_    :List[str]			     =  True
									lowerCAmelCase_    :Union[str, Any]			     =  model_class(__A       )
									lowerCAmelCase_    :Tuple			     =  model(self._prepare_for_class(__A       ,   __A       )       )
									self.assertEqual(config.output_hidden_states       ,   __A       )
									check_encoder_attentions_output(__A       )

									# Check attention is always last and order is fine
									lowerCAmelCase_    :Union[str, Any]			     =  True
									lowerCAmelCase_    :Tuple			     =  True
									lowerCAmelCase_    :List[Any]			     =  model_class(__A       )
									lowerCAmelCase_    :Tuple			     =  model(self._prepare_for_class(__A       ,   __A       )       )

									self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)       ,   len(__A       )       )
									self.assertEqual(model.config.output_hidden_states       ,   __A       )
									check_encoder_attentions_output(__A       )




@require_tf
class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			@slow
			def  __lowerCAmelCase (					self       )			->     Dict:
						lowerCAmelCase_    :Optional[int]			     =  TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""       )
						lowerCAmelCase_    :List[str]			     =  tf.constant([[0, 1, 2, 3, 4, 5]]       )
						lowerCAmelCase_    :List[Any]			     =  model(__A       )[0]

						lowerCAmelCase_    :Tuple			     =  [1, 6, 768]
						self.assertEqual(output.shape       ,   __A       )

						lowerCAmelCase_    :List[Any]			     =  tf.constant(
						    [
						        [
						            [-0.0_3_4_7_5_4_9_3, -0.4_6_8_6_0_3_4, -0.3_0_6_3_8_8_3_2],
						            [0.2_2_6_3_7_2_4_8, -0.2_6_9_8_8_6_4_6, -0.7_4_2_3_4_2_4],
						            [0.1_0_3_2_4_8_6_8, -0.4_5_0_1_3_5_0_8, -0.5_8_2_8_0_7_8_4],
						        ]
						    ]       )
						tf.debugging.assert_near(output[:, :3, :3]       ,   __A       ,   atol=1E-4       )










"""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


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




def    UpperCAmelCase__       (lowerCAmelCase_=None   ):




					'''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=lowerCAmelCase_  ,						default=lowerCAmelCase_  ,						help="Path to the config file to use for accelerate."  ,						)
					config_args.add_argument(
					    "--tpu_name"  ,						default=lowerCAmelCase_  ,						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=lowerCAmelCase_  ,						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=lowerCAmelCase_  ,						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=lowerCAmelCase_   )
					return parser




def    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''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(lowerCAmelCase_   ):
										__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   )  ,						lowerCAmelCase_   ):
										__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]  ,						lowerCAmelCase_   ):
										__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(lowerCAmelCase_   )

					# 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(lowerCAmelCase_   )}"""   )
										return
					subprocess.run(lowerCAmelCase_   )
					print("Successfully setup pod."   )




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				tpu_command_parser()
					__SCREAMING_SNAKE_CASE						   =				parser.parse_args()

					tpu_command_launcher(lowerCAmelCase_   )
