

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							(       ):
    SCREAMING_SNAKE_CASE__         =    argparse.ArgumentParser()
    parser.add_argument("-f"						)
    SCREAMING_SNAKE_CASE__         =    parser.parse_args()
    return args.f





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    {}
    SCREAMING_SNAKE_CASE__         =    os.path.join(__A			,							"all_results.json"						)
    if os.path.exists(__A						):
        with open(__A			,							"r"						) as f:
            SCREAMING_SNAKE_CASE__         =    json.load(__A						)
    else:
        raise ValueError(f"can\'t find {path}"						)
    return results





def 				__a							(       ):
    return bool(tf.config.list_physical_devices("GPU"						)						)


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







class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""



  @skip("Skipping until shape inference for to_tf_dataset PR is merged."						)
  def   _UpperCamelCase     (						self     :					Any						)       ->			int:
      SCREAMING_SNAKE_CASE__         =    self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE__         =    f"\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".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"						)
          SCREAMING_SNAKE_CASE__         =    get_results(lowerCamelCase_						)
          self.assertGreaterEqual(result["eval_accuracy"]  ,	0.75						)



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__         =    self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE__         =    f"\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".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()
          SCREAMING_SNAKE_CASE__         =    get_results(lowerCamelCase_						)
          self.assertLess(result["eval_perplexity"]  ,	100						)



  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE__         =    f"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        ".split()

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



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    7 if get_gpu_count() > 1 else 2

      SCREAMING_SNAKE_CASE__         =    self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE__         =    f"\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        ".split()

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



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE__         =    f"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

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



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

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



  @slow
  def   _UpperCamelCase     (						self     :					Tuple						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE__         =    f"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

      with patch.object(lowerCamelCase_  ,	"argv"  ,	lowerCamelCase_						):
          run_summarization.main()
          SCREAMING_SNAKE_CASE__         =    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   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			int:
      SCREAMING_SNAKE_CASE__         =    self.get_auto_remove_tmp_dir()
      SCREAMING_SNAKE_CASE__         =    f"\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        ".split()

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







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

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







from collections import OrderedDict
from typing import Mapping

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


_lowercase:					int      =						logging.get_logger(__name__)

_lowercase:					Dict      =						{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A		      =				"mobilebert"




	def __init__(self						,			lowerCamelCase_=30522						,			lowerCamelCase_=512						,			lowerCamelCase_=24						,			lowerCamelCase_=4						,			lowerCamelCase_=512						,			lowerCamelCase_="relu"						,			lowerCamelCase_=0.0						,			lowerCamelCase_=0.1						,			lowerCamelCase_=512						,			lowerCamelCase_=2						,			lowerCamelCase_=0.02						,			lowerCamelCase_=1E-1_2						,			lowerCamelCase_=0						,			lowerCamelCase_=128						,			lowerCamelCase_=True						,			lowerCamelCase_=True						,			lowerCamelCase_=128						,			lowerCamelCase_=False						,			lowerCamelCase_=True						,			lowerCamelCase_=4						,			lowerCamelCase_="no_norm"						,			lowerCamelCase_=True						,			lowerCamelCase_=None						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						super().__init__(pad_token_id=lowerCamelCase_						,			**lowerCamelCase_						)

						a   	=  vocab_size
						a   	=  hidden_size
						a   	=  num_hidden_layers
						a   	=  num_attention_heads
						a   	=  hidden_act
						a   	=  intermediate_size
						a   	=  hidden_dropout_prob
						a   	=  attention_probs_dropout_prob
						a   	=  max_position_embeddings
						a   	=  type_vocab_size
						a   	=  initializer_range
						a   	=  layer_norm_eps
						a   	=  embedding_size
						a   	=  trigram_input
						a   	=  use_bottleneck
						a   	=  intra_bottleneck_size
						a   	=  use_bottleneck_attention
						a   	=  key_query_shared_bottleneck
						a   	=  num_feedforward_networks
						a   	=  normalization_type
						a   	=  classifier_activation

						if self.use_bottleneck:
											a   	=  intra_bottleneck_size
						else:
											a   	=  hidden_size

						a   	=  classifier_dropout




class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						if self.task == "multiple-choice":
											a   	=  {0: "batch", 1: "choice", 2: "sequence"}
						else:
											a   	=  {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						        ("token_type_ids", dynamic_axis),
						    ]						)
