



import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


a_   :Tuple        =      [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-classification",
        "language-modeling",
        "summarization",
        "token-classification",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
 import run_clm_flax
 import run_flax_glue
 import run_flax_ner
 import run_mlm_flax
 import run_qa
 import run_summarization_flax
 import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

a_   :Any        =      logging.getLogger()
def        lowercase_							():
       snake_case__							:	str   	=	argparse.ArgumentParser()
       parser.add_argument('-f'      )
       snake_case__							:	Union[str, Any]   	=	parser.parse_args()
       return args.f
def        lowercase_							(A : str		,					A : int="eval"      ):
       snake_case__							:	Optional[int]   	=	os.path.join(A		,					F'''{split}_results.json'''      )
       if os.path.exists(A      ):
              with open(A		,					'r'      ) as f:
                     return json.load(A      )
       raise ValueError(F'''can\'t find {path}'''      )


a_   :Union[str, Any]        =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def    lowercase_   (       self  :  Tuple					)   ->Optional[int]:
            snake_case__							:	List[Any]   	=	self.get_auto_remove_tmp_dir()
            snake_case__							:	List[Any]   	=	F'''
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

            with patch.object(_snake_case, 'argv', _snake_case					):
                   run_flax_glue.main()
                   snake_case__							:	int   	=	get_results(_snake_case					)
                   self.assertGreaterEqual(result['eval_accuracy'], 0.7_5					)






     @slow
     def    lowercase_   (       self  :  str					)   ->Optional[int]:
            snake_case__							:	str   	=	self.get_auto_remove_tmp_dir()
            snake_case__							:	Optional[int]   	=	F'''
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

            with patch.object(_snake_case, 'argv', _snake_case					):
                   run_clm_flax.main()
                   snake_case__							:	Dict   	=	get_results(_snake_case					)
                   self.assertLess(result['eval_perplexity'], 1_0_0					)






     @slow
     def    lowercase_   (       self  :  Any					)   ->Union[str, Any]:
            snake_case__							:	List[str]   	=	self.get_auto_remove_tmp_dir()
            snake_case__							:	Optional[Any]   	=	F'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        '''.split()

            with patch.object(_snake_case, 'argv', _snake_case					):
                   run_summarization_flax.main()
                   snake_case__							:	Any   	=	get_results(_snake_case, split='test'					)
                   self.assertGreaterEqual(result['test_rouge1'], 1_0					)
                   self.assertGreaterEqual(result['test_rouge2'], 2					)
                   self.assertGreaterEqual(result['test_rougeL'], 7					)
                   self.assertGreaterEqual(result['test_rougeLsum'], 7					)






     @slow
     def    lowercase_   (       self  :  str					)   ->int:
            snake_case__							:	Tuple   	=	self.get_auto_remove_tmp_dir()
            snake_case__							:	List[Any]   	=	F'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        '''.split()

            with patch.object(_snake_case, 'argv', _snake_case					):
                   run_mlm_flax.main()
                   snake_case__							:	int   	=	get_results(_snake_case					)
                   self.assertLess(result['eval_perplexity'], 4_2					)






     @slow
     def    lowercase_   (       self  :  Dict					)   ->str:
            snake_case__							:	List[str]   	=	self.get_auto_remove_tmp_dir()
            snake_case__							:	str   	=	F'''
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

            with patch.object(_snake_case, 'argv', _snake_case					):
                   run_ta_mlm_flax.main()
                   snake_case__							:	int   	=	get_results(_snake_case					)
                   self.assertGreaterEqual(result['eval_accuracy'], 0.4_2					)






     @slow
     def    lowercase_   (       self  :  Optional[int]					)   ->List[str]:
            # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
            snake_case__							:	List[Any]   	=	7 if get_gpu_count() > 1 else 2

            snake_case__							:	List[Any]   	=	self.get_auto_remove_tmp_dir()
            snake_case__							:	List[str]   	=	F'''
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

            with patch.object(_snake_case, 'argv', _snake_case					):
                   run_flax_ner.main()
                   snake_case__							:	str   	=	get_results(_snake_case					)
                   self.assertGreaterEqual(result['eval_accuracy'], 0.7_5					)
                   self.assertGreaterEqual(result['eval_f1'], 0.3					)






     @slow
     def    lowercase_   (       self  :  int					)   ->Union[str, Any]:
            snake_case__							:	Union[str, Any]   	=	self.get_auto_remove_tmp_dir()
            snake_case__							:	List[Any]   	=	F'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

            with patch.object(_snake_case, 'argv', _snake_case					):
                   run_qa.main()
                   snake_case__							:	List[str]   	=	get_results(_snake_case					)
                   self.assertGreaterEqual(result['eval_f1'], 3_0					)
                   self.assertGreaterEqual(result['eval_exact'], 3_0					)











from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_   :Tuple        =      logging.get_logger(__name__)

a_   :Union[str, Any]        =      {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""deberta-v2"""






     def __init__(       self  :  Union[str, Any], _snake_case  :  Dict=1_2_8_1_0_0, _snake_case  :  Any=1_5_3_6, _snake_case  :  Tuple=2_4, _snake_case  :  int=2_4, _snake_case  :  Optional[int]=6_1_4_4, _snake_case  :  Optional[int]="gelu", _snake_case  :  Optional[int]=0.1, _snake_case  :  List[str]=0.1, _snake_case  :  str=5_1_2, _snake_case  :  Optional[int]=0, _snake_case  :  Optional[int]=0.0_2, _snake_case  :  Dict=1e-7, _snake_case  :  int=False, _snake_case  :  Any=-1, _snake_case  :  List[str]=0, _snake_case  :  Tuple=True, _snake_case  :  Any=None, _snake_case  :  Union[str, Any]=0, _snake_case  :  Tuple="gelu", **_snake_case  :  Union[str, Any], )   ->Optional[int]:
            super().__init__(**_snake_case					)

            snake_case__							:	Dict   	=	hidden_size
            snake_case__							:	Optional[int]   	=	num_hidden_layers
            snake_case__							:	Any   	=	num_attention_heads
            snake_case__							:	List[Any]   	=	intermediate_size
            snake_case__							:	List[Any]   	=	hidden_act
            snake_case__							:	Union[str, Any]   	=	hidden_dropout_prob
            snake_case__							:	Dict   	=	attention_probs_dropout_prob
            snake_case__							:	List[str]   	=	max_position_embeddings
            snake_case__							:	List[str]   	=	type_vocab_size
            snake_case__							:	Optional[Any]   	=	initializer_range
            snake_case__							:	Optional[int]   	=	relative_attention
            snake_case__							:	Tuple   	=	max_relative_positions
            snake_case__							:	Union[str, Any]   	=	pad_token_id
            snake_case__							:	Optional[int]   	=	position_biased_input

            # Backwards compatibility
            if type(_snake_case					) == str:
                   snake_case__							:	int   	=	[x.strip() for x in pos_att_type.lower().split('|'					)]

            snake_case__							:	List[str]   	=	pos_att_type
            snake_case__							:	Union[str, Any]   	=	vocab_size
            snake_case__							:	Optional[int]   	=	layer_norm_eps

            snake_case__							:	Optional[int]   	=	kwargs.get('pooler_hidden_size', _snake_case					)
            snake_case__							:	int   	=	pooler_dropout
            snake_case__							:	str   	=	pooler_hidden_act




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     @property
     def    lowercase_   (       self  :  Optional[int]					)   ->Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                   snake_case__							:	List[Any]   	=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   snake_case__							:	int   	=	{0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]					)
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]					)






     @property
     def    lowercase_   (       self  :  Dict					)   ->int:
            return 1_2






     def    lowercase_   (       self  :  Tuple, _snake_case  :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  bool = False, _snake_case  :  Optional["TensorType"] = None, _snake_case  :  int = 3, _snake_case  :  int = 4_0, _snake_case  :  int = 4_0, _snake_case  :  "PreTrainedTokenizerBase" = None, )   ->Mapping[str, Any]:
            snake_case__							:	Union[str, Any]   	=	super().generate_dummy_inputs(preprocessor=_snake_case, framework=_snake_case					)
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs






