


from __future__ import annotations

import unittest

from transformers import 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 numpy
		import tensorflow as tf

		from transformers import (
		    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BertConfig,
		    DPRConfig,
		    TFDPRContextEncoder,
		    TFDPRQuestionEncoder,
		    TFDPRReader,
		)

class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=13      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=99      ,   _SCREAMING_SNAKE_CASE=32      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=37      ,   _SCREAMING_SNAKE_CASE="gelu"      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=512      ,   _SCREAMING_SNAKE_CASE=16      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=0.02      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=0      ,   )			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      seq_length
									_UpperCAmelCase					=      is_training
									_UpperCAmelCase					=      use_input_mask
									_UpperCAmelCase					=      use_token_type_ids
									_UpperCAmelCase					=      use_labels
									_UpperCAmelCase					=      vocab_size
									_UpperCAmelCase					=      hidden_size
									_UpperCAmelCase					=      num_hidden_layers
									_UpperCAmelCase					=      num_attention_heads
									_UpperCAmelCase					=      intermediate_size
									_UpperCAmelCase					=      hidden_act
									_UpperCAmelCase					=      hidden_dropout_prob
									_UpperCAmelCase					=      attention_probs_dropout_prob
									_UpperCAmelCase					=      max_position_embeddings
									_UpperCAmelCase					=      type_vocab_size
									_UpperCAmelCase					=      type_sequence_label_size
									_UpperCAmelCase					=      initializer_range
									_UpperCAmelCase					=      num_labels
									_UpperCAmelCase					=      num_choices
									_UpperCAmelCase					=      scope
									_UpperCAmelCase					=      projection_dim
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size						)

									_UpperCAmelCase					=      None
									if self.use_input_mask:
																# follow test_modeling_tf_ctrl.py
																_UpperCAmelCase					=      random_attention_mask([self.batch_size, self.seq_length]						)

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

									_UpperCAmelCase					=      None
									_UpperCAmelCase					=      None
									_UpperCAmelCase					=      None
									if self.use_labels:
																_UpperCAmelCase					=      ids_tensor([self.batch_size]      ,   self.type_sequence_label_size						)
																_UpperCAmelCase					=      ids_tensor([self.batch_size, self.seq_length]      ,   self.num_labels						)
																_UpperCAmelCase					=      ids_tensor([self.batch_size]      ,   self.num_choices						)

									_UpperCAmelCase					=      BertConfig(
									    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      ,   is_decoder=_SCREAMING_SNAKE_CASE      ,   initializer_range=self.initializer_range      ,   )
									_UpperCAmelCase					=      DPRConfig(projection_dim=self.projection_dim      ,   **config.to_dict()						)

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      TFDPRContextEncoder(config=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   token_type_ids=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   token_type_ids=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(result.pooler_output.shape      ,   (self.batch_size, self.projection_dim or self.hidden_size)						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      TFDPRQuestionEncoder(config=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   token_type_ids=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   token_type_ids=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(result.pooler_output.shape      ,   (self.batch_size, self.projection_dim or self.hidden_size)						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      TFDPRReader(config=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE						)

									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)						)
									self.parent.assertEqual(result.relevance_logits.shape      ,   (self.batch_size,)						)






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




									"""simple docstring"""





									_UpperCAmelCase					=      self.prepare_config_and_inputs()
									(
									    (
									    _UpperCAmelCase
									)		,	(
									    _UpperCAmelCase
									)		,	(
									    _UpperCAmelCase
									)		,	(
									    _UpperCAmelCase
									)		,	(
									    _UpperCAmelCase
									)		,	(
									    _UpperCAmelCase
									)		,	(
									    _UpperCAmelCase
									)		,	
									)					=      config_and_inputs
									_UpperCAmelCase					=      {'input_ids': input_ids}
									return config, inputs_dict



@require_tf
class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Any           =     (
		    (
		        TFDPRContextEncoder,
		        TFDPRQuestionEncoder,
		        TFDPRReader,
		    )
		    if is_tf_available()
		    else ()
		)
		_a     :  Optional[int]           =     {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

		_a     :  Optional[Any]           =     False
		_a     :  int           =     False
		_a     :  str           =     False
		_a     :  Dict           =     False
		_a     :  int           =     False
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      TFDPRModelTester(self						)
									_UpperCAmelCase					=      ConfigTester(self      ,   config_class=_SCREAMING_SNAKE_CASE      ,   hidden_size=37						)
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_context_encoder(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_question_encoder(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_dpr_reader(*_SCREAMING_SNAKE_CASE						)






		@slow
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCAmelCase					=      TFDPRContextEncoder.from_pretrained(_SCREAMING_SNAKE_CASE						)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)

									for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCAmelCase					=      TFDPRContextEncoder.from_pretrained(_SCREAMING_SNAKE_CASE						)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)

									for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCAmelCase					=      TFDPRQuestionEncoder.from_pretrained(_SCREAMING_SNAKE_CASE						)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)

									for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCAmelCase					=      TFDPRReader.from_pretrained(_SCREAMING_SNAKE_CASE						)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)



@require_tf
class 		__a				(    unittest.TestCase      ):
		@slow
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'						)

									_UpperCAmelCase					=      tf.constant(
									    [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]						)  # [CLS] hello, is my dog cute? [SEP]
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)[0]  # embedding shape = (1, 768)
									# compare the actual values for a slice.
									_UpperCAmelCase					=      tf.constant(
									    [
									        [
									            0.03236253,
									            0.12753335,
									            0.16818509,
									            0.00279786,
									            0.3896933,
									            0.24264945,
									            0.2178971,
									            -0.02335227,
									            -0.08481959,
									            -0.14324117,
									        ]
									    ]						)
									self.assertTrue(numpy.allclose(output[:, :10].numpy()      ,   expected_slice.numpy()      ,   atol=1e-4						)						)




# 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__  :Dict             =							'''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''

def 	lowerCAmelCase__			(      a__:		Optional[Any]=None	)  ->		List[Any]:


							'''simple docstring'''





							if subparsers is not None:
														_UpperCAmelCase					=      subparsers.add_parser('tpu-config'      ,  description=_description	)
							else:
														_UpperCAmelCase					=      argparse.ArgumentParser('Accelerate tpu-config command'      ,  description=_description	)
							# Core arguments
							_UpperCAmelCase					=      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.'      ,  )
							_UpperCAmelCase					=      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 	lowerCAmelCase__			(      a__:		str	)  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      None

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

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

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							_UpperCAmelCase					=      ['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 	lowerCAmelCase__			(      )  ->		Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      tpu_command_parser()
							_UpperCAmelCase					=      parser.parse_args()

							tpu_command_launcher(a__	)
