





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


a__:      Optional[int]									=		logging.get_logger(__name__)

a__:      Tuple									=		{
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''marian'''
 __SCREAMING_SNAKE_CASE							       =	['''past_key_values''']
 __SCREAMING_SNAKE_CASE							       =	{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



 def __init__(							self,__lowerCamelCase=5_8101,__lowerCamelCase=None,__lowerCamelCase=1024,__lowerCamelCase=12,__lowerCamelCase=4096,__lowerCamelCase=16,__lowerCamelCase=12,__lowerCamelCase=4096,__lowerCamelCase=16,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase="gelu",__lowerCamelCase=1024,__lowerCamelCase=0.1,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.02,__lowerCamelCase=5_8100,__lowerCamelCase=False,__lowerCamelCase=5_8100,__lowerCamelCase=0,__lowerCamelCase=0,__lowerCamelCase=True,**__lowerCamelCase,):
        A__											=						vocab_size
        A__											=						decoder_vocab_size or vocab_size
        A__											=						max_position_embeddings
        A__											=						d_model
        A__											=						encoder_ffn_dim
        A__											=						encoder_layers
        A__											=						encoder_attention_heads
        A__											=						decoder_ffn_dim
        A__											=						decoder_layers
        A__											=						decoder_attention_heads
        A__											=						dropout
        A__											=						attention_dropout
        A__											=						activation_dropout
        A__											=						activation_function
        A__											=						init_std
        A__											=						encoder_layerdrop
        A__											=						decoder_layerdrop
        A__											=						use_cache
        A__											=						encoder_layers
        A__											=						scale_embedding  # scale factor will be sqrt(d_model) if True
        A__											=						share_encoder_decoder_embeddings
        super().__init__(
            pad_token_id=__lowerCamelCase,eos_token_id=__lowerCamelCase,is_encoder_decoder=__lowerCamelCase,decoder_start_token_id=__lowerCamelCase,forced_eos_token_id=__lowerCamelCase,**__lowerCamelCase,)




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
 def 				UpperCamelCase			(							self ):
        if self.task in ["default", "seq2seq-lm"]:
               A__											=						OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ] )

               if self.use_past:
                      A__											=						{0: '''batch'''}
                      A__											=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
               else:
                      A__											=						{0: '''batch''', 1: '''decoder_sequence'''}
                      A__											=						{0: '''batch''', 1: '''decoder_sequence'''}

               if self.use_past:
                      self.fill_with_past_key_values_(__lowerCamelCase,direction='''inputs''' )
        elif self.task == "causal-lm":
               # TODO: figure this case out.
               A__											=						OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ] )
               if self.use_past:
                      A__					,	A__											=						self.num_layers
                      for i in range(__lowerCamelCase ):
                             A__											=						{0: '''batch''', 2: '''past_sequence + sequence'''}
                             A__											=						{0: '''batch''', 2: '''past_sequence + sequence'''}
        else:
               A__											=						OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                       ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                   ] )

        return common_inputs



 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 				UpperCamelCase			(							self ):
        if self.task in ["default", "seq2seq-lm"]:
               A__											=						super().outputs
        else:
               A__											=						super(__lowerCamelCase,self ).outputs
               if self.use_past:
                      A__					,	A__											=						self.num_layers
                      for i in range(__lowerCamelCase ):
                             A__											=						{0: '''batch''', 2: '''past_sequence + sequence'''}
                             A__											=						{0: '''batch''', 2: '''past_sequence + sequence'''}
        return common_outputs



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = -1,__lowerCamelCase = -1,__lowerCamelCase = False,__lowerCamelCase = None,):
        A__											=						self._generate_dummy_inputs_for_encoder_and_decoder(
            __lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )

        # Generate decoder inputs
        A__											=						seq_length if not self.use_past else 1
        A__											=						self._generate_dummy_inputs_for_encoder_and_decoder(
            __lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
        A__											=						{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
        A__											=						dict(**__lowerCamelCase,**__lowerCamelCase )

        if self.use_past:
               if not is_torch_available():
                      raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
               else:
                      import torch
               A__					,	A__											=						common_inputs['''input_ids'''].shape
               A__											=						common_inputs['''decoder_input_ids'''].shape[1]
               A__					,	A__											=						self.num_attention_heads
               A__											=						(
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               A__											=						decoder_seq_length + 3
               A__											=						(
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               A__											=						torch.cat(
                   [common_inputs['''decoder_attention_mask'''], torch.ones(__lowerCamelCase,__lowerCamelCase )],dim=1 )

               A__											=						[]
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               A__					,	A__											=						self.num_layers
               A__											=						min(__lowerCamelCase,__lowerCamelCase )
               A__											=						max(__lowerCamelCase,__lowerCamelCase ) - min_num_layers
               A__											=						'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

               for _ in range(__lowerCamelCase ):
                      common_inputs["past_key_values"].append(
                          (
                              torch.zeros(__lowerCamelCase ),
                              torch.zeros(__lowerCamelCase ),
                              torch.zeros(__lowerCamelCase ),
                              torch.zeros(__lowerCamelCase ),
                          ) )
               # TODO: test this.
               A__											=						encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
               for _ in range(__lowerCamelCase,__lowerCamelCase ):
                      common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) )
        return common_inputs



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = -1,__lowerCamelCase = -1,__lowerCamelCase = False,__lowerCamelCase = None,):
        A__											=						self._generate_dummy_inputs_for_encoder_and_decoder(
            __lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )

        if self.use_past:
               if not is_torch_available():
                      raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
               else:
                      import torch
               A__					,	A__											=						common_inputs['''input_ids'''].shape
               # Not using the same length for past_key_values
               A__											=						seqlen + 2
               A__					,	A__											=						self.num_layers
               A__					,	A__											=						self.num_attention_heads
               A__											=						(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               A__											=						common_inputs['''attention_mask'''].dtype
               A__											=						torch.cat(
                   [common_inputs['''attention_mask'''], torch.ones(__lowerCamelCase,__lowerCamelCase,dtype=__lowerCamelCase )],dim=1 )
               A__											=						[
                   (torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(__lowerCamelCase )
               ]
        return common_inputs



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = -1,__lowerCamelCase = -1,__lowerCamelCase = False,__lowerCamelCase = None,):
        # Copied from OnnxConfig.generate_dummy_inputs
        # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        A__											=						compute_effective_axis_dimension(
            __lowerCamelCase,fixed_dimension=OnnxConfig.default_fixed_batch,num_token_to_add=0 )

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        A__											=						tokenizer.num_special_tokens_to_add(__lowerCamelCase )
        A__											=						compute_effective_axis_dimension(
            __lowerCamelCase,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=__lowerCamelCase )

        # Generate dummy inputs according to compute batch and sequence
        A__											=						[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
        A__											=						dict(tokenizer(__lowerCamelCase,return_tensors=__lowerCamelCase ) )
        return common_inputs



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = -1,__lowerCamelCase = -1,__lowerCamelCase = False,__lowerCamelCase = None,):
        if self.task in ["default", "seq2seq-lm"]:
               A__											=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   __lowerCamelCase,batch_size=__lowerCamelCase,seq_length=__lowerCamelCase,is_pair=__lowerCamelCase,framework=__lowerCamelCase )

        else:
               A__											=						self._generate_dummy_inputs_for_causal_lm(
                   __lowerCamelCase,batch_size=__lowerCamelCase,seq_length=__lowerCamelCase,is_pair=__lowerCamelCase,framework=__lowerCamelCase )

        return common_inputs



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        if self.task in ["default", "seq2seq-lm"]:
               A__											=						super()._flatten_past_key_values_(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
        else:
               A__											=						super(__lowerCamelCase,self )._flatten_past_key_values_(
                   __lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )




 @property
 def 				UpperCamelCase			(							self ):
        return 1E-4












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 )





