

# Copyright 2021 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 json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


_lowerCamelCase								=os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
_lowerCamelCase								=os.path.join(hf_cache_home, "accelerate")
_lowerCamelCase								=os.path.join(cache_dir, "default_config.yaml")
_lowerCamelCase								=os.path.join(cache_dir, "default_config.yaml")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
 _lowerCamelCase								=default_yaml_config_file
else:
 _lowerCamelCase								=default_json_config_file







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     if config_file is not None:
          if not os.path.isfile(lowerCAmelCase_      ):
               raise FileNotFoundError(
                   F'The passed configuration file `{config_file}` does not exist. '
                   'Please pass an existing file to `accelerate launch`, or use the the default one '
                   'created through `accelerate config` and run `accelerate launch` '
                   'without the `--config_file` argument.'      )
     else:
          SCREAMING_SNAKE_CASE										=default_config_file
     with open(lowerCAmelCase_,   'r',   encoding='utf-8'      ) as f:
          if config_file.endswith('.json'      ):
               if (
                   json.load(lowerCAmelCase_      ).get('compute_environment',   ComputeEnvironment.LOCAL_MACHINE      )
                   == ComputeEnvironment.LOCAL_MACHINE
               ):
                    SCREAMING_SNAKE_CASE										=ClusterConfig
               else:
                    SCREAMING_SNAKE_CASE										=SageMakerConfig
               return config_class.from_json_file(json_file=lowerCAmelCase_      )
          else:
               if (
                   yaml.safe_load(lowerCAmelCase_      ).get('compute_environment',   ComputeEnvironment.LOCAL_MACHINE      )
                   == ComputeEnvironment.LOCAL_MACHINE
               ):
                    SCREAMING_SNAKE_CASE										=ClusterConfig
               else:
                    SCREAMING_SNAKE_CASE										=SageMakerConfig
               return config_class.from_yaml_file(yaml_file=lowerCAmelCase_      )



@dataclass
class 		a_	:



      """simple docstring"""







      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				42
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=self.__dict__
           # For serialization, it's best to convert Enums to strings (or their underlying value type).
           for key, value in result.items():
                if isinstance(snake_case      ,snake_case    ):
                     SCREAMING_SNAKE_CASE										=value.value
                if isinstance(snake_case      ,snake_case    ) and not bool(snake_case    ):
                     SCREAMING_SNAKE_CASE										=None
           SCREAMING_SNAKE_CASE										={k: v for k, v in result.items() if v is not None}
           return result
      @classmethod
      def 	_lowerCAmelCase					(				cls					:							List[Any]      ,snake_case					:							Optional[int]=None    ):
           SCREAMING_SNAKE_CASE										=default_json_config_file if json_file is None else json_file
           with open(snake_case      ,'r'      ,encoding='utf-8'    ) as f:
                SCREAMING_SNAKE_CASE										=json.load(snake_case    )
           if "compute_environment" not in config_dict:
                SCREAMING_SNAKE_CASE										=ComputeEnvironment.LOCAL_MACHINE
           if "mixed_precision" not in config_dict:
                SCREAMING_SNAKE_CASE										='fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
           if "fp16" in config_dict:  # Convert the config to the new format.
                del config_dict["fp16"]
           if "dynamo_backend" in config_dict:  # Convert the config to the new format.
                SCREAMING_SNAKE_CASE										=config_dict.pop('dynamo_backend'    )
                SCREAMING_SNAKE_CASE										={} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
           if "use_cpu" not in config_dict:
                SCREAMING_SNAKE_CASE										=False
           return cls(**snake_case    )
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]      ,snake_case					:							Dict    ):
           with open(snake_case      ,'w'      ,encoding='utf-8'    ) as f:
                SCREAMING_SNAKE_CASE										=json.dumps(self.to_dict()      ,indent=2      ,sort_keys=snake_case    ) + '\n'
                f.write(snake_case    )
      @classmethod
      def 	_lowerCAmelCase					(				cls					:							int      ,snake_case					:							Optional[Any]=None    ):
           SCREAMING_SNAKE_CASE										=default_yaml_config_file if yaml_file is None else yaml_file
           with open(snake_case      ,'r'      ,encoding='utf-8'    ) as f:
                SCREAMING_SNAKE_CASE										=yaml.safe_load(snake_case    )
           if "compute_environment" not in config_dict:
                SCREAMING_SNAKE_CASE										=ComputeEnvironment.LOCAL_MACHINE

           if "mixed_precision" not in config_dict:
                SCREAMING_SNAKE_CASE										='fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
           if isinstance(config_dict['mixed_precision']      ,snake_case    ) and not config_dict["mixed_precision"]:
                SCREAMING_SNAKE_CASE										='no'
           if "fp16" in config_dict:  # Convert the config to the new format.
                del config_dict["fp16"]
           if "dynamo_backend" in config_dict:  # Convert the config to the new format.
                SCREAMING_SNAKE_CASE										=config_dict.pop('dynamo_backend'    )
                SCREAMING_SNAKE_CASE										={} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
           if "use_cpu" not in config_dict:
                SCREAMING_SNAKE_CASE										=False
           return cls(**snake_case    )
      def 	_lowerCAmelCase					(				self					:							Optional[int]      ,snake_case					:							int    ):
           with open(snake_case      ,'w'      ,encoding='utf-8'    ) as f:
                yaml.safe_dump(self.to_dict()      ,snake_case    )


      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           if isinstance(self.compute_environment      ,snake_case    ):
                SCREAMING_SNAKE_CASE										=ComputeEnvironment(self.compute_environment    )
           if isinstance(self.distributed_type      ,snake_case    ):
                if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
                     SCREAMING_SNAKE_CASE										=SageMakerDistributedType(self.distributed_type    )
                else:
                     SCREAMING_SNAKE_CASE										=DistributedType(self.distributed_type    )
           if self.dynamo_config is None:
                SCREAMING_SNAKE_CASE										={}







@dataclass
class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				0
      __UpperCAmelCase      		=				1
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				"static"
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				"main"

      # args for deepspeed_plugin
      __UpperCAmelCase      		=				None
      # args for fsdp
      __UpperCAmelCase      		=				None
      # args for megatron_lm
      __UpperCAmelCase      		=				None
      # args for ipex
      __UpperCAmelCase      		=				None
      # args for TPU
      __UpperCAmelCase      		=				False

      # args for TPU pods
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None

      # args for dynamo
      __UpperCAmelCase      		=				None
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           if self.deepspeed_config is None:
                SCREAMING_SNAKE_CASE										={}
           if self.fsdp_config is None:
                SCREAMING_SNAKE_CASE										={}
           if self.megatron_lm_config is None:
                SCREAMING_SNAKE_CASE										={}
           if self.ipex_config is None:
                SCREAMING_SNAKE_CASE										={}
           return super().__post_init__()







@dataclass
class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				"us-east-1"
      __UpperCAmelCase      		=				1
      __UpperCAmelCase      		=				"all"
      __UpperCAmelCase      		=				F'''accelerate-sagemaker-{num_machines}'''
      __UpperCAmelCase      		=				SAGEMAKER_PYTORCH_VERSION
      __UpperCAmelCase      		=				SAGEMAKER_TRANSFORMERS_VERSION
      __UpperCAmelCase      		=				SAGEMAKER_PYTHON_VERSION
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None




from __future__ import annotations

import unittest

from transformers import MPNetConfig, 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.models.mpnet.modeling_tf_mpnet import (
     TFMPNetForMaskedLM,
     TFMPNetForMultipleChoice,
     TFMPNetForQuestionAnswering,
     TFMPNetForSequenceClassification,
     TFMPNetForTokenClassification,
     TFMPNetModel,
 )



class 		a_	:



      """simple docstring"""







      def __init__(				self					:							Any      ,snake_case					:							List[Any]      ,snake_case					:							Optional[Any]=13      ,snake_case					:							str=7      ,snake_case					:							List[Any]=True      ,snake_case					:							Optional[Any]=True      ,snake_case					:							int=False      ,snake_case					:							List[str]=True      ,snake_case					:							Optional[int]=99      ,snake_case					:							Any=64      ,snake_case					:							int=2      ,snake_case					:							str=4      ,snake_case					:							str=64      ,snake_case					:							List[Any]="gelu"      ,snake_case					:							Dict=0.1      ,snake_case					:							int=0.1      ,snake_case					:							List[Any]=512      ,snake_case					:							int=16      ,snake_case					:							List[Any]=2      ,snake_case					:							Union[str, Any]=0.02      ,snake_case					:							List[str]=3      ,snake_case					:							Any=4      ,snake_case					:							Tuple=None      ,):
           SCREAMING_SNAKE_CASE										=parent
           SCREAMING_SNAKE_CASE										=batch_size
           SCREAMING_SNAKE_CASE										=seq_length
           SCREAMING_SNAKE_CASE										=is_training
           SCREAMING_SNAKE_CASE										=use_input_mask
           SCREAMING_SNAKE_CASE										=use_token_type_ids
           SCREAMING_SNAKE_CASE										=use_labels
           SCREAMING_SNAKE_CASE										=vocab_size
           SCREAMING_SNAKE_CASE										=hidden_size
           SCREAMING_SNAKE_CASE										=num_hidden_layers
           SCREAMING_SNAKE_CASE										=num_attention_heads
           SCREAMING_SNAKE_CASE										=intermediate_size
           SCREAMING_SNAKE_CASE										=hidden_act
           SCREAMING_SNAKE_CASE										=hidden_dropout_prob
           SCREAMING_SNAKE_CASE										=attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE										=max_position_embeddings
           SCREAMING_SNAKE_CASE										=type_vocab_size
           SCREAMING_SNAKE_CASE										=type_sequence_label_size
           SCREAMING_SNAKE_CASE										=initializer_range
           SCREAMING_SNAKE_CASE										=num_labels
           SCREAMING_SNAKE_CASE										=num_choices
           SCREAMING_SNAKE_CASE										=scope
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

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

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

           SCREAMING_SNAKE_CASE										=MPNetConfig(
               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      ,initializer_range=self.initializer_range      ,)
           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							Any      ,snake_case					:							Optional[int]      ,snake_case					:							str      ,snake_case					:							Union[str, Any]      ,snake_case					:							Optional[Any]      ,snake_case					:							Any    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModel(config=snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           SCREAMING_SNAKE_CASE										=[input_ids, input_mask]
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)    )
      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							List[str]      ,snake_case					:							Tuple      ,snake_case					:							Dict      ,snake_case					:							Optional[Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Dict    ):
           SCREAMING_SNAKE_CASE										=TFMPNetForMaskedLM(config=snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							List[str]      ,snake_case					:							Dict      ,snake_case					:							List[Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Optional[int]      ,snake_case					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=TFMPNetForQuestionAnswering(config=snake_case    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': input_ids,
               'attention_mask': input_mask,
           }
           SCREAMING_SNAKE_CASE										=model(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)    )
      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							Union[str, Any]      ,snake_case					:							str      ,snake_case					:							str      ,snake_case					:							Any      ,snake_case					:							Tuple      ,snake_case					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=TFMPNetForSequenceClassification(snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							Any      ,snake_case					:							Union[str, Any]      ,snake_case					:							int      ,snake_case					:							List[str]      ,snake_case					:							Tuple      ,snake_case					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=self.num_choices
           SCREAMING_SNAKE_CASE										=TFMPNetForMultipleChoice(snake_case    )
           SCREAMING_SNAKE_CASE										=tf.tile(tf.expand_dims(snake_case      ,1    )      ,(1, self.num_choices, 1)    )
           SCREAMING_SNAKE_CASE										=tf.tile(tf.expand_dims(snake_case      ,1    )      ,(1, self.num_choices, 1)    )
           SCREAMING_SNAKE_CASE										={
               'input_ids': multiple_choice_inputs_ids,
               'attention_mask': multiple_choice_input_mask,
           }
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)    )
      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							Tuple      ,snake_case					:							Dict      ,snake_case					:							Tuple      ,snake_case					:							Union[str, Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=TFMPNetForTokenClassification(snake_case    )
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )


      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=self.prepare_config_and_inputs()
           ((SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE)						,  (SCREAMING_SNAKE_CASE))										=config_and_inputs
           SCREAMING_SNAKE_CASE										={'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict







@require_tf
class 		a_	(							lowerCamelCase_			,      lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				(
          (
              TFMPNetForMaskedLM,
              TFMPNetForMultipleChoice,
              TFMPNetForQuestionAnswering,
              TFMPNetForSequenceClassification,
              TFMPNetForTokenClassification,
              TFMPNetModel,
          )
          if is_tf_available()
          else ()
      )
      __UpperCAmelCase      		=				(
          {
              'feature-extraction': TFMPNetModel,
              'fill-mask': TFMPNetForMaskedLM,
              'question-answering': TFMPNetForQuestionAnswering,
              'text-classification': TFMPNetForSequenceClassification,
              'token-classification': TFMPNetForTokenClassification,
              'zero-shot': TFMPNetForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModelTester(self    )
           SCREAMING_SNAKE_CASE										=ConfigTester(self      ,config_class=snake_case      ,hidden_size=37    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           self.config_tester.run_common_tests()
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_model(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_masked_lm(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case    )


      @slow
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           for model_name in ["microsoft/mpnet-base"]:
                SCREAMING_SNAKE_CASE										=TFMPNetModel.from_pretrained(snake_case    )
                self.assertIsNotNone(snake_case    )







@require_tf
class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      @slow
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=TFMPNetModel.from_pretrained('microsoft/mpnet-base'    )
           SCREAMING_SNAKE_CASE										=tf.constant([[0, 1, 2, 3, 4, 5]]    )
           SCREAMING_SNAKE_CASE										=model(snake_case    )[0]

           SCREAMING_SNAKE_CASE										=[1, 6, 768]
           self.assertEqual(output.shape      ,snake_case    )

           SCREAMING_SNAKE_CASE										=tf.constant(
               [
                   [
                       [-0.1_067_172, 0.08_216_473, 0.0_024_543],
                       [-0.03_465_879, 0.8_354_118, -0.03_252_288],
                       [-0.06_569_476, -0.12_424_111, -0.0_494_436],
                   ]
               ]    )
           tf.debugging.assert_near(output[:, :3, :3]      ,snake_case      ,atol=1e-4    )

