


'''simple docstring'''



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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__A	=logging.get_logger(__name__)

__A	={
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}


class 		_snake_case					(     a__	):
					lowerCAmelCase      :str   =					'''gptj'''
					lowerCAmelCase      :Dict   =					{
					    '''max_position_embeddings''': '''n_positions''',
					    '''hidden_size''': '''n_embd''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}




					def __init__(      self							,							_lowerCamelCase=5_0400							,							_lowerCamelCase=2048							,							_lowerCamelCase=4096							,							_lowerCamelCase=28							,							_lowerCamelCase=16							,							_lowerCamelCase=64							,							_lowerCamelCase=None							,							_lowerCamelCase="gelu_new"							,							_lowerCamelCase=0.0							,							_lowerCamelCase=0.0							,							_lowerCamelCase=0.0							,							_lowerCamelCase=1e-5							,							_lowerCamelCase=0.02							,							_lowerCamelCase=True							,							_lowerCamelCase=5_0256							,							_lowerCamelCase=5_0256							,							_lowerCamelCase=False							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					List[str]        =     vocab_size
								UpperCAmelCase__  :					Union[str, Any]        =     n_positions
								UpperCAmelCase__  :					Optional[int]        =     n_embd
								UpperCAmelCase__  :					Dict        =     n_layer
								UpperCAmelCase__  :					Dict        =     n_head
								UpperCAmelCase__  :					List[Any]        =     n_inner
								UpperCAmelCase__  :					Dict        =     rotary_dim
								UpperCAmelCase__  :					Any        =     activation_function
								UpperCAmelCase__  :					Tuple        =     resid_pdrop
								UpperCAmelCase__  :					List[str]        =     embd_pdrop
								UpperCAmelCase__  :					Union[str, Any]        =     attn_pdrop
								UpperCAmelCase__  :					Optional[int]        =     layer_norm_epsilon
								UpperCAmelCase__  :					Any        =     initializer_range
								UpperCAmelCase__  :					Any        =     use_cache

								UpperCAmelCase__  :					Union[str, Any]        =     bos_token_id
								UpperCAmelCase__  :					List[Any]        =     eos_token_id

								super().__init__(
								    bos_token_id=_lowerCamelCase							,							eos_token_id=_lowerCamelCase							,							tie_word_embeddings=_lowerCamelCase							,							**_lowerCamelCase)


class 		_snake_case					(     a__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase = "default"							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							):
								super().__init__(_lowerCamelCase							,							task=_lowerCamelCase							,							patching_specs=_lowerCamelCase							,							use_past=_lowerCamelCase)
								if not getattr(self._config							,							"""pad_token_id"""							,							_lowerCamelCase):
											# TODO: how to do that better?
											UpperCAmelCase__  :					List[Any]        =     0




					@property
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[int]        =     OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}})
								if self.use_past:
											self.fill_with_past_key_values_(_lowerCamelCase							,							direction="""inputs""")
											UpperCAmelCase__  :					str        =     {0: """batch""", 1: """past_sequence + sequence"""}
								else:
											UpperCAmelCase__  :					str        =     {0: """batch""", 1: """sequence"""}

								return common_inputs




					@property
					def 				snake_case__						(      self):
								return self._config.n_layer




					@property
					def 				snake_case__						(      self):
								return self._config.n_head




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = -1							,							_lowerCamelCase = -1							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							):
								UpperCAmelCase__  :					int        =     super(_lowerCamelCase							,							self).generate_dummy_inputs(
								    _lowerCamelCase							,							batch_size=_lowerCamelCase							,							seq_length=_lowerCamelCase							,							is_pair=_lowerCamelCase							,							framework=_lowerCamelCase)

								# We need to order the input in the way they appears in the forward()
								UpperCAmelCase__  :					Union[str, Any]        =     OrderedDict({"""input_ids""": common_inputs["""input_ids"""]})

								# Need to add the past_keys
								if self.use_past:
											if not is_torch_available():
														raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""")
											else:
														import torch

														UpperCAmelCase__   ,       UpperCAmelCase__  :					Union[str, Any]        =     common_inputs["""input_ids"""].shape
														# Not using the same length for past_key_values
														UpperCAmelCase__  :					Union[str, Any]        =     seqlen + 2
														UpperCAmelCase__  :					Tuple        =     (
														    batch,
														    self.num_attention_heads,
														    past_key_values_length,
														    self._config.hidden_size // self.num_attention_heads,
														)
														UpperCAmelCase__  :					Dict        =     [
														    (torch.zeros(_lowerCamelCase), torch.zeros(_lowerCamelCase)) for _ in range(self.num_layers)
														]

								UpperCAmelCase__  :					List[Any]        =     common_inputs["""attention_mask"""]
								if self.use_past:
											UpperCAmelCase__  :					List[Any]        =     ordered_inputs["""attention_mask"""].dtype
											UpperCAmelCase__  :					Optional[Any]        =     torch.cat(
											    [ordered_inputs["""attention_mask"""], torch.ones(_lowerCamelCase							,							_lowerCamelCase							,							dtype=_lowerCamelCase)]							,							dim=1)

								return ordered_inputs




					@property
					def 				snake_case__						(      self):
								return 13



'''simple docstring'''



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	=logging.getLogger()







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[Any]        =     argparse.ArgumentParser()
			parser.add_argument("""-f"""					)
			UpperCAmelCase__  :					Optional[Any]        =     parser.parse_args()
			return args.f


class 		_snake_case					(     a__	):




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     logging.StreamHandler(sys.stdout)
								logger.addHandler(_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Any        =     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):
														UpperCAmelCase__  :					Union[str, Any]        =     run_glue_deebert.main()
														for value in result.values():
																	self.assertGreaterEqual(_lowerCamelCase							,							0.666)




					@slow
					@require_torch_non_multi_gpu
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     """
            --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)

								UpperCAmelCase__  :					Dict        =     """
            --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)

								UpperCAmelCase__  :					Any        =     """
            --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)