


"""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_   )




'''simple docstring'''

from __future__ import annotations

import unittest

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

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


if is_tf_available():
     import numpy
     import tensorflow as tf

     from transformers.models.esm.modeling_tf_esm import (
         TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFEsmForMaskedLM,
         TFEsmForSequenceClassification,
         TFEsmForTokenClassification,
         TFEsmModel,
     )





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  parent
   lowerCamelCase_      =  13
   lowerCamelCase_      =  7
   lowerCamelCase_      =  True
   lowerCamelCase_      =  True
   lowerCamelCase_      =  True
   lowerCamelCase_      =  99
   lowerCamelCase_      =  32
   lowerCamelCase_      =  2
   lowerCamelCase_      =  4
   lowerCamelCase_      =  37
   lowerCamelCase_      =  "gelu"
   lowerCamelCase_      =  0.1
   lowerCamelCase_      =  0.1
   lowerCamelCase_      =  512
   lowerCamelCase_      =  16
   lowerCamelCase_      =  2
   lowerCamelCase_      =  0.02
   lowerCamelCase_      =  3
   lowerCamelCase_      =  4
   lowerCamelCase_      =  None






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size     )

   lowerCamelCase_      =  None
   if self.use_input_mask:
    lowerCamelCase_      =  random_attention_mask([self.batch_size, self.seq_length]     )

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

   lowerCamelCase_      =  EsmConfig(
       vocab_size=self.vocab_size			,  hidden_size=self.hidden_size			,  num_hidden_layers=self.num_hidden_layers			,  pad_token_id=1			,  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 config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






  def   snake_case		(    self     ):







   """simple docstring"""
   (
       (
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,
   )      =  self.prepare_config_and_inputs()

   lowerCamelCase_      =  True
   lowerCamelCase_      =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
   lowerCamelCase_      =  ids_tensor([self.batch_size, self.seq_length]			,  vocab_size=2     )

   return (
       config,
       input_ids,
       input_mask,
       sequence_labels,
       token_labels,
       choice_labels,
       encoder_hidden_states,
       encoder_attention_mask,
   )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmModel(config=UpperCamelCase     )
   lowerCamelCase_      =  {"input_ids": input_ids, "attention_mask": input_mask}
   lowerCamelCase_      =  model(UpperCamelCase     )

   lowerCamelCase_      =  [input_ids, input_mask]
   lowerCamelCase_      =  model(UpperCamelCase     )

   lowerCamelCase_      =  model(UpperCamelCase     )

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






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  True

   lowerCamelCase_      =  TFEsmModel(config=UpperCamelCase     )
   lowerCamelCase_      =  {
       "input_ids": input_ids,
       "attention_mask": input_mask,
       "encoder_hidden_states": encoder_hidden_states,
       "encoder_attention_mask": encoder_attention_mask,
   }
   lowerCamelCase_      =  model(UpperCamelCase     )

   lowerCamelCase_      =  [input_ids, input_mask]
   lowerCamelCase_      =  model(UpperCamelCase			,  encoder_hidden_states=UpperCamelCase     )

   # Also check the case where encoder outputs are not passed
   lowerCamelCase_      =  model(UpperCamelCase			,  attention_mask=UpperCamelCase     )

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






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmForMaskedLM(config=UpperCamelCase     )
   lowerCamelCase_      =  model([input_ids, input_mask]     )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.num_labels
   lowerCamelCase_      =  TFEsmForTokenClassification(config=UpperCamelCase     )
   lowerCamelCase_      =  {"input_ids": input_ids, "attention_mask": input_mask}
   lowerCamelCase_      =  model(UpperCamelCase     )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.num_labels)     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   (
       (
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,
   )      =  config_and_inputs
   lowerCamelCase_      =  {"input_ids": input_ids, "attention_mask": input_mask}
   return config, inputs_dict





@require_tf
class      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (
      (
          TFEsmModel,
          TFEsmForMaskedLM,
          TFEsmForSequenceClassification,
          TFEsmForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  _lowerCamelCase		    =  (
      {
          "feature-extraction": TFEsmModel,
          "fill-mask": TFEsmForMaskedLM,
          "text-classification": TFEsmForSequenceClassification,
          "token-classification": TFEsmForTokenClassification,
          "zero-shot": TFEsmForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmModelTester(self     )
   lowerCamelCase_      =  ConfigTester(self			,  config_class=UpperCamelCase			,  hidden_size=37     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self.config_tester.run_common_tests()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*UpperCamelCase     )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  TFEsmModel.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )






  @unittest.skip("Protein models do not support embedding resizing."     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  @unittest.skip("Protein models do not support embedding resizing."     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    lowerCamelCase_      =  model_class(UpperCamelCase     )
    assert isinstance(model.get_input_embeddings()			,  tf.keras.layers.Layer     )
    if model_class is TFEsmForMaskedLM:
     # Output embedding test differs from the main test because they're a matrix, not a layer
     lowerCamelCase_      =  model.get_bias()
     assert isinstance(UpperCamelCase			,  UpperCamelCase     )
     for k, v in name.items():
      assert isinstance(UpperCamelCase			,  tf.Variable     )
    else:
     lowerCamelCase_      =  model.get_output_embeddings()
     assert x is None
     lowerCamelCase_      =  model.get_bias()
     assert name is None





@require_tf
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"     )

   lowerCamelCase_      =  tf.constant([[0, 1, 2, 3, 4, 5]]     )
   lowerCamelCase_      =  model(UpperCamelCase     )[0]
   lowerCamelCase_      =  [1, 6, 33]
   self.assertEqual(list(output.numpy().shape     )			,  UpperCamelCase     )
   # compare the actual values for a slice.
   lowerCamelCase_      =  tf.constant(
       [
           [
               [8.921_518, -10.589_814, -6.4_671_307],
               [-6.3_967_156, -13.911_377, -1.1_211_915],
               [-7.781_247, -13.951_557, -3.740_592],
           ]
       ]     )
   self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()			,  expected_slice.numpy()			,  atol=1e-2     )     )







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"     )

   lowerCamelCase_      =  tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]     )
   lowerCamelCase_      =  model(UpperCamelCase     )[0]
   # compare the actual values for a slice.
   lowerCamelCase_      =  tf.constant(
       [
           [
               [0.14_443_092, 0.54_125_327, 0.3_247_739],
               [0.30_340_484, 0.00_526_676, 0.31_077_722],
               [0.32_278_043, -0.24_987_096, 0.3_414_628],
           ]
       ]     )
   self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()			,  expected_slice.numpy()			,  atol=1e-4     )     )



