
"""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


SCREAMING_SNAKE_CASE_         =  [
    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)

SCREAMING_SNAKE_CASE_         =  logging.getLogger()




def       lowercase__  (   )	-> List[str]:






    """simple docstring"""




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




def       lowercase__  (   lowerCAmelCase			:   Any	)	-> Optional[Any]:






    """simple docstring"""




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




def       lowercase__  (   )	-> Dict:






    """simple docstring"""




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


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







class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_							):




      @skip('Skipping until shape inference for to_tf_dataset PR is merged.'							)
      def       a_ (							self							)  ->   str:
          UpperCAmelCase       =   self.get_auto_remove_tmp_dir()
          UpperCAmelCase       =   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(lowercase_				,    'argv'				,    lowercase_							):
              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(lowercase_							)
              self.assertGreaterEqual(result['eval_accuracy']				,    0.7_5							)




      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase       =   self.get_auto_remove_tmp_dir()
          UpperCAmelCase       =   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(lowercase_				,    'argv'				,    lowercase_							):
              run_clm.main()
              UpperCAmelCase       =   get_results(lowercase_							)
              self.assertLess(result['eval_perplexity']				,    1_0_0							)




      def       a_ (							self							)  ->   List[str]:
          UpperCAmelCase       =   self.get_auto_remove_tmp_dir()
          UpperCAmelCase       =   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(lowercase_				,    'argv'				,    lowercase_							):
              run_mlm.main()
              UpperCAmelCase       =   get_results(lowercase_							)
              self.assertLess(result['eval_perplexity']				,    4_2							)




      def       a_ (							self							)  ->   List[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"\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(lowercase_				,    'argv'				,    lowercase_							):
              run_ner.main()
              UpperCAmelCase       =   get_results(lowercase_							)
              self.assertGreaterEqual(result['accuracy']				,    0.7_5							)




      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase       =   self.get_auto_remove_tmp_dir()
          UpperCAmelCase       =   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(lowercase_				,    'argv'				,    lowercase_							):
              run_squad.main()
              UpperCAmelCase       =   get_results(lowercase_							)
              self.assertGreaterEqual(result['f1']				,    3_0							)
              self.assertGreaterEqual(result['exact']				,    3_0							)




      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase       =   self.get_auto_remove_tmp_dir()
          UpperCAmelCase       =   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(lowercase_				,    'argv'				,    lowercase_							):
              run_swag.main()
              UpperCAmelCase       =   get_results(lowercase_							)
              self.assertGreaterEqual(result['val_accuracy']				,    0.8							)




      @slow
      def       a_ (							self							)  ->   Any:
          UpperCAmelCase       =   self.get_auto_remove_tmp_dir()
          UpperCAmelCase       =   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(lowercase_				,    'argv'				,    lowercase_							):
              run_summarization.main()
              UpperCAmelCase       =   get_results(lowercase_							)
              self.assertGreaterEqual(result['rouge1']				,    1_0							)
              self.assertGreaterEqual(result['rouge2']				,    2							)
              self.assertGreaterEqual(result['rougeL']				,    7							)
              self.assertGreaterEqual(result['rougeLsum']				,    7							)




      @slow
      def       a_ (							self							)  ->   Union[str, Any]:
          UpperCAmelCase       =   self.get_auto_remove_tmp_dir()
          UpperCAmelCase       =   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(lowercase_				,    'argv'				,    lowercase_							):
              run_translation.main()
              UpperCAmelCase       =   get_results(lowercase_							)
              self.assertGreaterEqual(result['bleu']				,    3_0							)




      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase       =   self.get_auto_remove_tmp_dir()
          UpperCAmelCase       =   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(lowercase_				,    'argv'				,    lowercase_							):
              run_image_classification.main()
              UpperCAmelCase       =   get_results(lowercase_							)
              self.assertGreaterEqual(result['accuracy']				,    0.7							)





"""simple docstring"""

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_							):




      @register_to_config
      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = False				,    )  ->   List[Any]:
          super().__init__()

          UpperCAmelCase       =   nn.Linear(lowercase_				,    lowercase_				,    bias=lowercase_							)

          UpperCAmelCase       =   nn.Embedding(lowercase_				,    lowercase_							)
          UpperCAmelCase       =   False

          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)

          UpperCAmelCase       =   TaConfig(
              d_model=lowercase_				,    num_heads=lowercase_				,    d_kv=lowercase_				,    d_ff=lowercase_				,    feed_forward_proj=lowercase_				,    dropout_rate=lowercase_				,    is_decoder=lowercase_				,    is_encoder_decoder=lowercase_				,    )
          UpperCAmelCase       =   nn.ModuleList()
          for lyr_num in range(lowercase_							):
              UpperCAmelCase       =   TaBlock(lowercase_							)
              self.encoders.append(lowercase_							)

          UpperCAmelCase       =   TaLayerNorm(lowercase_							)
          UpperCAmelCase       =   nn.Dropout(p=lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   Union[str, Any]:
          UpperCAmelCase       =   self.input_proj(lowercase_							)

          # terminal relative positional encodings
          UpperCAmelCase       =   encoder_inputs.shape[1]
          UpperCAmelCase       =   torch.arange(lowercase_				,    device=encoder_inputs.device							)

          UpperCAmelCase       =   encoder_inputs_mask.sum(-1							)
          UpperCAmelCase       =   torch.roll(input_positions.unsqueeze(0							)				,    tuple(seq_lens.tolist()							)				,    dims=0							)
          x += self.position_encoding(lowercase_							)

          UpperCAmelCase       =   self.dropout_pre(lowercase_							)

          # inverted the attention mask
          UpperCAmelCase       =   encoder_inputs.size()
          UpperCAmelCase       =   self.get_extended_attention_mask(lowercase_				,    lowercase_							)

          for lyr in self.encoders:
              UpperCAmelCase       =   lyr(lowercase_				,    lowercase_							)[0]
          UpperCAmelCase       =   self.layer_norm(lowercase_							)

          return self.dropout_post(lowercase_							), encoder_inputs_mask



