

'''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   lowerCamelCase__       (      )		->    Optional[int]:



    """simple docstring"""







    _snake_case : Dict												=		argparse.ArgumentParser()
    parser.add_argument('-f')
    _snake_case : Optional[Any]												=		parser.parse_args()
    return args.f

def   lowerCamelCase__       (      a__)		->    Union[str, Any]:



    """simple docstring"""







    _snake_case : List[Any]												=		{}
    _snake_case : str												=		os.path.join(a__			,      'all_results.json')
    if os.path.exists(a__):
        with open(a__			,      'r') as f:
            _snake_case : List[str]												=		json.load(a__)
    else:
        raise ValueError(F"""can't find {path}""")
    return results

def   lowerCamelCase__       (      )		->    Dict:



    """simple docstring"""







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


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




class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      @skip('Skipping until shape inference for to_tf_dataset PR is merged.'   )
      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          _snake_case : Tuple												=		self.get_auto_remove_tmp_dir()
          _snake_case : List[str]												=		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(snake_case     ,					'argv'     ,					snake_case   ):
              run_text_classification.main()
              # Reset the mixed precision policy so we don't break other tests
              tf.keras.mixed_precision.set_global_policy('float32'   )
              _snake_case : List[str]												=		get_results(snake_case   )
              self.assertGreaterEqual(result['eval_accuracy']     ,					0.75   )




      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          _snake_case : List[str]												=		self.get_auto_remove_tmp_dir()
          _snake_case : str												=		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(snake_case     ,					'argv'     ,					snake_case   ):
              run_clm.main()
              _snake_case : Optional[int]												=		get_results(snake_case   )
              self.assertLess(result['eval_perplexity']     ,					100   )




      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case : List[Any]												=		self.get_auto_remove_tmp_dir()
          _snake_case : int												=		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(snake_case     ,					'argv'     ,					snake_case   ):
              run_mlm.main()
              _snake_case : Optional[int]												=		get_results(snake_case   )
              self.assertLess(result['eval_perplexity']     ,					42   )




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : str												=		7 if get_gpu_count() > 1 else 2

          _snake_case : List[Any]												=		self.get_auto_remove_tmp_dir()
          _snake_case : Optional[Any]												=		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(snake_case     ,					'argv'     ,					snake_case   ):
              run_ner.main()
              _snake_case : str												=		get_results(snake_case   )
              self.assertGreaterEqual(result['accuracy']     ,					0.75   )




      def 	__UpperCAmelCase			(					self :		List[Any]   ):



          """simple docstring"""





          _snake_case : List[str]												=		self.get_auto_remove_tmp_dir()
          _snake_case : int												=		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(snake_case     ,					'argv'     ,					snake_case   ):
              run_squad.main()
              _snake_case : List[Any]												=		get_results(snake_case   )
              self.assertGreaterEqual(result['f1']     ,					30   )
              self.assertGreaterEqual(result['exact']     ,					30   )




      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case : Union[str, Any]												=		self.get_auto_remove_tmp_dir()
          _snake_case : List[str]												=		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(snake_case     ,					'argv'     ,					snake_case   ):
              run_swag.main()
              _snake_case : Optional[Any]												=		get_results(snake_case   )
              self.assertGreaterEqual(result['val_accuracy']     ,					0.8   )




      @slow
      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          _snake_case : Optional[int]												=		self.get_auto_remove_tmp_dir()
          _snake_case : 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
            --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(snake_case     ,					'argv'     ,					snake_case   ):
              run_summarization.main()
              _snake_case : Any												=		get_results(snake_case   )
              self.assertGreaterEqual(result['rouge1']     ,					10   )
              self.assertGreaterEqual(result['rouge2']     ,					2   )
              self.assertGreaterEqual(result['rougeL']     ,					7   )
              self.assertGreaterEqual(result['rougeLsum']     ,					7   )




      @slow
      def 	__UpperCAmelCase			(					self :		List[Any]   ):



          """simple docstring"""





          _snake_case : List[str]												=		self.get_auto_remove_tmp_dir()
          _snake_case : str												=		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(snake_case     ,					'argv'     ,					snake_case   ):
              run_translation.main()
              _snake_case : Tuple												=		get_results(snake_case   )
              self.assertGreaterEqual(result['bleu']     ,					30   )




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : int												=		self.get_auto_remove_tmp_dir()
          _snake_case : Optional[Any]												=		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(snake_case     ,					'argv'     ,					snake_case   ):
              run_image_classification.main()
              _snake_case : List[Any]												=		get_results(snake_case   )
              self.assertGreaterEqual(result['accuracy']     ,					0.7   )






'''simple docstring'''



import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
SCREAMING_SNAKE_CASE_    =   logging.getLogger(__name__)

def   lowerCamelCase__       (      a__)		->    List[Any]:



    """simple docstring"""







    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)

def   lowerCamelCase__       (      )		->    Tuple:



    """simple docstring"""







    _snake_case : Optional[int]												=		argparse.ArgumentParser()
    parser.add_argument(
        '--model_name_or_path'			,      default=a__			,      type=a__			,      required=a__			,      )
    parser.add_argument('--prompt'			,      type=a__			,      default='')
    parser.add_argument('--length'			,      type=a__			,      default=2_0)
    parser.add_argument('--stop_token'			,      type=a__			,      default=a__			,      help='Token at which text generation is stopped')
    parser.add_argument(
        '--temperature'			,      type=a__			,      default=1.0			,      help='temperature of 1.0 has no effect, lower tend toward greedy sampling'			,      )
    parser.add_argument(
        '--repetition_penalty'			,      type=a__			,      default=1.0			,      help='primarily useful for CTRL model; in that case, use 1.2')
    parser.add_argument('--k'			,      type=a__			,      default=0)
    parser.add_argument('--penalty_alpha'			,      type=a__			,      default=0.0)
    parser.add_argument('--p'			,      type=a__			,      default=0.9)

    parser.add_argument('--prefix'			,      type=a__			,      default=''			,      help='Text added prior to input.')
    parser.add_argument('--padding_text'			,      type=a__			,      default=''			,      help='Deprecated, the use of `--prefix` is preferred.')
    parser.add_argument('--xlm_language'			,      type=a__			,      default=''			,      help='Optional language when used with the XLM model.')

    parser.add_argument('--seed'			,      type=a__			,      default=4_2			,      help='random seed for initialization')
    parser.add_argument('--no_cuda'			,      action='store_true'			,      help='Avoid using CUDA when available')
    parser.add_argument(
        '--fp16'			,      action='store_true'			,      help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'			,      )
    _snake_case : Optional[int]												=		parser.parse_args()

    _snake_case : Optional[int]												=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu')
    _snake_case : int												=		0 if args.no_cuda else torch.cuda.device_count()

    logger.warning(F"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}""")

    set_seed(a__)

    # Initialize the model and tokenizer
    _snake_case : Dict												=		AutoTokenizer.from_pretrained(args.model_name_or_path)
    _snake_case : List[Any]												=		AutoModelForCausalLM.from_pretrained(args.model_name_or_path)

    # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
    # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
    model.to(args.device)

    if args.fpaa:
        model.half()

    logger.info(a__)
    _snake_case : Any												=		args.prompt if args.prompt else input('Model prompt >>> ')

    _snake_case : List[str]												=		tokenizer(a__			,      return_tensors='pt'			,      add_special_tokens=a__)
    _snake_case : List[str]												=		{key: value.to(args.device) for key, value in inputs.items()}

    _snake_case : Optional[int]												=		model.generate(
        **a__			,      max_length=args.length + len(inputs['input_ids'][0])			,      penalty_alpha=args.penalty_alpha			,      top_k=args.k			,      )

    _snake_case : int												=		[]
    for generated_sequence_idx, generated_sequence in enumerate(a__):
        print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===""")
        _snake_case : Any												=		generated_sequence.tolist()

        # Decode text
        _snake_case : str												=		tokenizer.decode(a__			,      clean_up_tokenization_spaces=a__			,      add_special_tokens=a__)

        # Remove all text after the stop token
        _snake_case : str												=		text[: text.find(args.stop_token) if args.stop_token else None]

        # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
        _snake_case : Tuple												=		(
            prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]			,      clean_up_tokenization_spaces=a__)) :]
        )

        generated_sequences.append(a__)
        print(a__)

    return generated_sequences


if __name__ == "__main__":
   main()



