

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
       import tensorflow as tf

if is_keras_nlp_available():
       from transformers.models.gpta import TFGPTaTokenizer


_SCREAMING_SNAKE_CASE     :  Optional[Any]   			=			['gpt2']
_SCREAMING_SNAKE_CASE     :  Optional[Any]   			=			'gpt2'

if is_tf_available():






       class        UpperCAmelCase__		(       tf.Module       ):






         """simple docstring"""


         def __init__(							self						:		Optional[Any]	,   __lowerCamelCase						:		Union[str, Any]					)		->							Tuple:
              super().__init__()
              SCREAMING_SNAKE_CASE__    =    tokenizer
              SCREAMING_SNAKE_CASE__    =    AutoConfig.from_pretrained(__lowerCamelCase					)
              SCREAMING_SNAKE_CASE__    =    TFGPTaLMHeadModel.from_config(__lowerCamelCase					)







         @tf.function(input_signature=(tf.TensorSpec((None,)	,   tf.string	,   name='''text'''					),)					)
         def       lowercase_		(							self						:		Any	,   __lowerCamelCase						:		Optional[int]					)		->							Optional[Any]:
              SCREAMING_SNAKE_CASE__    =    self.tokenizer(__lowerCamelCase					)
              SCREAMING_SNAKE_CASE__    =    tokenized['''input_ids'''].to_tensor()

              SCREAMING_SNAKE_CASE__    =    tf.cast(input_ids_dense > 0	,   tf.intaa					)
              # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

              SCREAMING_SNAKE_CASE__    =    self.model(input_ids=__lowerCamelCase	,   attention_mask=__lowerCamelCase					)['''logits''']

              return outputs






@require_tf
@require_keras_nlp
class        UpperCAmelCase__		(       unittest.TestCase       ):






  """simple docstring"""


  def       lowercase_		(							self						:		Tuple					)		->							Optional[int]:
       super().setUp()

       SCREAMING_SNAKE_CASE__    =    [GPTaTokenizer.from_pretrained(__lowerCamelCase					) for checkpoint in (TOKENIZER_CHECKPOINTS)]
       SCREAMING_SNAKE_CASE__    =    [TFGPTaTokenizer.from_pretrained(__lowerCamelCase					) for checkpoint in TOKENIZER_CHECKPOINTS]
       assert len(self.tokenizers					) == len(self.tf_tokenizers					)

       SCREAMING_SNAKE_CASE__    =    [
           '''This is a straightforward English test sentence.''',
           '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
           '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
           '''And some much more rare Chinese: 齉 堃 齉堃''',
           '''Je vais aussi écrire en français pour tester les accents''',
           '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
       ]
       SCREAMING_SNAKE_CASE__    =    list(zip(self.test_sentences	,   self.test_sentences[::-1]					)					)






  def       lowercase_		(							self						:		Optional[int]					)		->							Union[str, Any]:
       for tokenizer, tf_tokenizer in zip(self.tokenizers	,   self.tf_tokenizers					):
            for test_inputs in self.test_sentences:
                 SCREAMING_SNAKE_CASE__    =    tokenizer([test_inputs]	,   return_tensors='''tf'''					)
                 SCREAMING_SNAKE_CASE__    =    tf_tokenizer([test_inputs]					)

                 for key in python_outputs.keys():
                      # convert them to numpy to avoid messing with ragged tensors
                      SCREAMING_SNAKE_CASE__    =    python_outputs[key].numpy()
                      SCREAMING_SNAKE_CASE__    =    tf_outputs[key].numpy()

                      self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape					)					)
                      self.assertTrue(tf.reduce_all(tf.cast(__lowerCamelCase	,   tf.intaa					) == tf_outputs_values					)					)






  @slow
  def       lowercase_		(							self						:		Optional[int]					)		->							Optional[Any]:
       for tf_tokenizer in self.tf_tokenizers:
            SCREAMING_SNAKE_CASE__    =    tf.function(__lowerCamelCase					)
            for test_inputs in self.test_sentences:
                 SCREAMING_SNAKE_CASE__    =    tf.constant(__lowerCamelCase					)
                 SCREAMING_SNAKE_CASE__    =    compiled_tokenizer(__lowerCamelCase					)
                 SCREAMING_SNAKE_CASE__    =    tf_tokenizer(__lowerCamelCase					)

                 for key in eager_outputs.keys():
                      self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]					)					)






  @slow
  def       lowercase_		(							self						:		Tuple					)		->							List[Any]:
       for tf_tokenizer in self.tf_tokenizers:
            SCREAMING_SNAKE_CASE__    =    ModelToSave(tokenizer=__lowerCamelCase					)
            SCREAMING_SNAKE_CASE__    =    tf.convert_to_tensor([self.test_sentences[0]]					)
            SCREAMING_SNAKE_CASE__    =    model.serving(__lowerCamelCase					)  # Build model with some sample inputs
            with TemporaryDirectory() as tempdir:
                 SCREAMING_SNAKE_CASE__    =    Path(__lowerCamelCase					) / '''saved.model'''
                 tf.saved_model.save(__lowerCamelCase	,   __lowerCamelCase	,   signatures={'''serving_default''': model.serving}					)
                 SCREAMING_SNAKE_CASE__    =    tf.saved_model.load(__lowerCamelCase					)
            SCREAMING_SNAKE_CASE__    =    loaded_model.signatures['''serving_default'''](__lowerCamelCase					)['''output_0''']
            # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
            self.assertTrue(tf.reduce_all(out == loaded_output					)					)






  @slow
  def       lowercase_		(							self						:		Any					)		->							List[Any]:
       for tf_tokenizer in self.tf_tokenizers:
            SCREAMING_SNAKE_CASE__    =    tf.convert_to_tensor([self.test_sentences[0]]					)
            SCREAMING_SNAKE_CASE__    =    tf_tokenizer(__lowerCamelCase					)  # Build model with some sample inputs

            SCREAMING_SNAKE_CASE__    =    tf_tokenizer.get_config()
            SCREAMING_SNAKE_CASE__    =    TFGPTaTokenizer.from_config(__lowerCamelCase					)
            SCREAMING_SNAKE_CASE__    =    model_from_config(__lowerCamelCase					)

            for key in from_config_output.keys():
                 self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]					)					)







  @slow
  def       lowercase_		(							self						:		Dict					)		->							Optional[Any]:
       for tf_tokenizer in self.tf_tokenizers:
            # for the test to run
            SCREAMING_SNAKE_CASE__    =    12_3123

            for max_length in [3, 5, 1024]:
                 SCREAMING_SNAKE_CASE__    =    tf.convert_to_tensor([self.test_sentences[0]]					)
                 SCREAMING_SNAKE_CASE__    =    tf_tokenizer(__lowerCamelCase	,   max_length=__lowerCamelCase					)

                 SCREAMING_SNAKE_CASE__    =    out['''input_ids'''].numpy().shape[1]

                 assert out_length == max_length









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

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

a__:      List[Any]									=		logging.getLogger()


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						argparse.ArgumentParser()
       parser.add_argument('''-f'''  )
       A__											=						parser.parse_args()
       return args.f




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        A__											=						logging.StreamHandler(sys.stdout )
        logger.addHandler(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						get_gpu_count()

        if n_gpu > 1:
               pass
               # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
               # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
               # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
               # cmd = [sys.executable] + distributed_args + args
               # execute_subprocess_async(cmd, env=self.get_env())
               # XXX: test the results - need to save them first into .json file
        else:
               args.insert(0,'''run_glue_deebert.py''' )
               with patch.object(__lowerCamelCase,'''argv''',__lowerCamelCase ):
                      A__											=						run_glue_deebert.main()
                      for value in result.values():
                             self.assertGreaterEqual(__lowerCamelCase,0.666 )




 @slow
 @require_torch_non_multi_gpu
 def 				UpperCamelCase			(							self ):
        A__											=						'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )

        A__											=						'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
        self.run_and_check(__lowerCamelCase )





