


"""simple docstring"""







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

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__a      			= [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-generation",
        "text-classification",
        "token-classification",
        "language-modeling",
        "multiple-choice",
        "question-answering",
        "summarization",
        "translation",
        "image-classification",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
						import run_clm
						import run_image_classification
						import run_mlm
						import run_ner
						import run_qa as run_squad
						import run_summarization
						import run_swag
						import run_text_classification
						import run_translation


logging.basicConfig(level=logging.DEBUG)

__a      			= logging.getLogger()






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     argparse.ArgumentParser()
						parser.add_argument('''-f'''			)
						UpperCAmelCase__												=     parser.parse_args()
						return args.f






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						UpperCAmelCase__												=     {}
						UpperCAmelCase__												=     os.path.join(_UpperCamelCase						,      '''all_results.json'''			)
						if os.path.exists(_UpperCamelCase			):
												with open(_UpperCamelCase						,      '''r'''			) as f:
																		UpperCAmelCase__												=     json.load(_UpperCamelCase			)
						else:
												raise ValueError(F"""can't find {path}"""			)
						return results






def        a__			(							):





						"""simple docstring"""

						return bool(tf.config.list_physical_devices('''GPU'''			)			)


__a      			= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""






							@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''					)
							def        lowerCAmelCase  (    self			:  Tuple					):
													UpperCAmelCase__												=     self.get_auto_remove_tmp_dir()
													UpperCAmelCase__												=     f"""
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

													if is_cuda_available():
																			testargs.append('''--fp16'''					)

													with patch.object(lowerCamelCase_		,			'''argv'''		,			lowerCamelCase_					):
																			run_text_classification.main()
																			# Reset the mixed precision policy so we don't break other tests
																			tf.keras.mixed_precision.set_global_policy('''float32'''					)
																			UpperCAmelCase__												=     get_results(lowerCamelCase_					)
																			self.assertGreaterEqual(result['''eval_accuracy''']		,			0.75					)






							def        lowerCAmelCase  (    self			:  int					):
													UpperCAmelCase__												=     self.get_auto_remove_tmp_dir()
													UpperCAmelCase__												=     f"""
            run_clm.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 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

													if len(tf.config.list_physical_devices('''GPU'''					)					) > 1:
																			# Skipping because there are not enough batches to train the model + would need a drop_last to work.
																			return

													with patch.object(lowerCamelCase_		,			'''argv'''		,			lowerCamelCase_					):
																			run_clm.main()
																			UpperCAmelCase__												=     get_results(lowerCamelCase_					)
																			self.assertLess(result['''eval_perplexity''']		,			100					)






							def        lowerCAmelCase  (    self			:  str					):
													UpperCAmelCase__												=     self.get_auto_remove_tmp_dir()
													UpperCAmelCase__												=     f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

													with patch.object(lowerCamelCase_		,			'''argv'''		,			lowerCamelCase_					):
																			run_mlm.main()
																			UpperCAmelCase__												=     get_results(lowerCamelCase_					)
																			self.assertLess(result['''eval_perplexity''']		,			42					)






							def        lowerCAmelCase  (    self			:  Union[str, Any]					):
													# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
													UpperCAmelCase__												=     7 if get_gpu_count() > 1 else 2

													UpperCAmelCase__												=     self.get_auto_remove_tmp_dir()
													UpperCAmelCase__												=     f"""
            run_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
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

													with patch.object(lowerCamelCase_		,			'''argv'''		,			lowerCamelCase_					):
																			run_ner.main()
																			UpperCAmelCase__												=     get_results(lowerCamelCase_					)
																			self.assertGreaterEqual(result['''accuracy''']		,			0.75					)






							def        lowerCAmelCase  (    self			:  List[str]					):
													UpperCAmelCase__												=     self.get_auto_remove_tmp_dir()
													UpperCAmelCase__												=     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
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

													with patch.object(lowerCamelCase_		,			'''argv'''		,			lowerCamelCase_					):
																			run_squad.main()
																			UpperCAmelCase__												=     get_results(lowerCamelCase_					)
																			self.assertGreaterEqual(result['''f1''']		,			30					)
																			self.assertGreaterEqual(result['''exact''']		,			30					)






							def        lowerCAmelCase  (    self			:  List[str]					):
													UpperCAmelCase__												=     self.get_auto_remove_tmp_dir()
													UpperCAmelCase__												=     f"""
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

													with patch.object(lowerCamelCase_		,			'''argv'''		,			lowerCamelCase_					):
																			run_swag.main()
																			UpperCAmelCase__												=     get_results(lowerCamelCase_					)
																			self.assertGreaterEqual(result['''val_accuracy''']		,			0.8					)






							@slow
							def        lowerCAmelCase  (    self			:  List[Any]					):
													UpperCAmelCase__												=     self.get_auto_remove_tmp_dir()
													UpperCAmelCase__												=     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
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

													with patch.object(lowerCamelCase_		,			'''argv'''		,			lowerCamelCase_					):
																			run_summarization.main()
																			UpperCAmelCase__												=     get_results(lowerCamelCase_					)
																			self.assertGreaterEqual(result['''rouge1''']		,			10					)
																			self.assertGreaterEqual(result['''rouge2''']		,			2					)
																			self.assertGreaterEqual(result['''rougeL''']		,			7					)
																			self.assertGreaterEqual(result['''rougeLsum''']		,			7					)






							@slow
							def        lowerCAmelCase  (    self			:  List[Any]					):
													UpperCAmelCase__												=     self.get_auto_remove_tmp_dir()
													UpperCAmelCase__												=     f"""
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        """.split()

													with patch.object(lowerCamelCase_		,			'''argv'''		,			lowerCamelCase_					):
																			run_translation.main()
																			UpperCAmelCase__												=     get_results(lowerCamelCase_					)
																			self.assertGreaterEqual(result['''bleu''']		,			30					)






							def        lowerCAmelCase  (    self			:  Optional[Any]					):
													UpperCAmelCase__												=     self.get_auto_remove_tmp_dir()
													UpperCAmelCase__												=     f"""
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            """.split()

													with patch.object(lowerCamelCase_		,			'''argv'''		,			lowerCamelCase_					):
																			run_image_classification.main()
																			UpperCAmelCase__												=     get_results(lowerCamelCase_					)
																			self.assertGreaterEqual(result['''accuracy''']		,			0.7					)



"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


__a      			= logging.get_logger(__name__)

__a      			= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a      			= {
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

__a      			= {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

__a      			= {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE              =   PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE              =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE              =   ["""input_ids""", """attention_mask""", """token_type_ids"""]
							SCREAMING_SNAKE_CASE              =   DebertaTokenizer






							def __init__(    self			:  Dict		,			lowerCamelCase_			:  Dict=None		,			lowerCamelCase_			:  Union[str, Any]=None		,			lowerCamelCase_			:  Tuple=None		,			lowerCamelCase_			:  Dict="replace"		,			lowerCamelCase_			:  List[Any]="[CLS]"		,			lowerCamelCase_			:  Optional[Any]="[SEP]"		,			lowerCamelCase_			:  List[Any]="[SEP]"		,			lowerCamelCase_			:  str="[CLS]"		,			lowerCamelCase_			:  List[str]="[UNK]"		,			lowerCamelCase_			:  Any="[PAD]"		,			lowerCamelCase_			:  Tuple="[MASK]"		,			lowerCamelCase_			:  List[str]=False		,			**lowerCamelCase_			:  List[str]		,			):
													super().__init__(
													    lowerCamelCase_		,			lowerCamelCase_		,			tokenizer_file=lowerCamelCase_		,			errors=lowerCamelCase_		,			bos_token=lowerCamelCase_		,			eos_token=lowerCamelCase_		,			unk_token=lowerCamelCase_		,			sep_token=lowerCamelCase_		,			cls_token=lowerCamelCase_		,			pad_token=lowerCamelCase_		,			mask_token=lowerCamelCase_		,			add_prefix_space=lowerCamelCase_		,			**lowerCamelCase_		,			)
													UpperCAmelCase__												=     kwargs.pop('''add_bos_token'''		,			lowerCamelCase_					)

													UpperCAmelCase__												=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
													if pre_tok_state.get('''add_prefix_space'''		,			lowerCamelCase_					) != add_prefix_space:
																			UpperCAmelCase__												=     getattr(lowerCamelCase_		,			pre_tok_state.pop('''type'''					)					)
																			UpperCAmelCase__												=     add_prefix_space
																			UpperCAmelCase__												=     pre_tok_class(**lowerCamelCase_					)

													UpperCAmelCase__												=     add_prefix_space






							@property
							def        lowerCAmelCase  (    self			:  int					):
													if self._mask_token is None:
																			if self.verbose:
																									logger.error('''Using mask_token, but it is not set yet.'''					)
																			return None
													return str(self._mask_token					)






							@mask_token.setter
							def        lowerCAmelCase  (    self			:  Union[str, Any]		,			lowerCamelCase_			:  Union[str, Any]					):
													UpperCAmelCase__												=     AddedToken(lowerCamelCase_		,			lstrip=lowerCamelCase_		,			rstrip=lowerCamelCase_					) if isinstance(lowerCamelCase_		,			lowerCamelCase_					) else value
													UpperCAmelCase__												=     value






							def        lowerCAmelCase  (    self			:  int		,			lowerCamelCase_			:  List[int]		,			lowerCamelCase_			:  Optional[List[int]] = None					):
													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													UpperCAmelCase__												=     [self.cls_token_id]
													UpperCAmelCase__												=     [self.sep_token_id]
													return cls + token_ids_a + sep + token_ids_a + sep






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  List[int]		,			lowerCamelCase_			:  Optional[List[int]] = None					):
													UpperCAmelCase__												=     [self.sep_token_id]
													UpperCAmelCase__												=     [self.cls_token_id]

													if token_ids_a is None:
																			return len(cls + token_ids_a + sep					) * [0]
													return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






							def        lowerCAmelCase  (    self			:  Tuple		,			*lowerCamelCase_			:  Optional[int]		,			**lowerCamelCase_			:  Union[str, Any]					):
													UpperCAmelCase__												=     kwargs.get('''is_split_into_words'''		,			lowerCamelCase_					)
													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._batch_encode_plus(*lowerCamelCase_		,			**lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Tuple		,			*lowerCamelCase_			:  str		,			**lowerCamelCase_			:  Any					):
													UpperCAmelCase__												=     kwargs.get('''is_split_into_words'''		,			lowerCamelCase_					)

													assert self.add_prefix_space or not is_split_into_words, (
													    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
													    "to use it with pretokenized inputs."
													)

													return super()._encode_plus(*lowerCamelCase_		,			**lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Dict		,			lowerCamelCase_			:  str		,			lowerCamelCase_			:  Optional[str] = None					):
													UpperCAmelCase__												=     self._tokenizer.model.save(lowerCamelCase_		,			name=lowerCamelCase_					)
													return tuple(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  "Conversation"					):
													UpperCAmelCase__												=     []
													for is_user, text in conversation.iter_texts():
																			input_ids.extend(self.encode(lowerCamelCase_		,			add_special_tokens=lowerCamelCase_					) + [self.eos_token_id]					)

													if len(lowerCamelCase_					) > self.model_max_length:
																			UpperCAmelCase__												=     input_ids[-self.model_max_length :]
													return input_ids