




import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CTRLForSequenceClassification,
		    CTRLLMHeadModel,
		    CTRLModel,
		)
class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     str       ,	__lowercase							:     List[str]       ,	__lowercase							:     Optional[int]=14       ,	__lowercase							:     Any=7       ,	__lowercase							:     List[Any]=True       ,	__lowercase							:     Union[str, Any]=True       ,	__lowercase							:     Any=True       ,	__lowercase							:     Optional[int]=True       ,	__lowercase							:     Union[str, Any]=True       ,	__lowercase							:     Union[str, Any]=99       ,	__lowercase							:     Any=32       ,	__lowercase							:     str=5       ,	__lowercase							:     int=4       ,	__lowercase							:     str=37       ,	__lowercase							:     Optional[Any]="gelu"       ,	__lowercase							:     Any=0.1       ,	__lowercase							:     Any=0.1       ,	__lowercase							:     Tuple=5_12       ,	__lowercase							:     str=16       ,	__lowercase							:     str=2       ,	__lowercase							:     List[Any]=0.02       ,	__lowercase							:     int=3       ,	__lowercase							:     List[str]=4       ,	__lowercase							:     int=None       ,	):






							"""simple docstring"""






							snake_case_		    =     parent
							snake_case_		    =     batch_size
							snake_case_		    =     seq_length
							snake_case_		    =     is_training
							snake_case_		    =     use_token_type_ids
							snake_case_		    =     use_input_mask
							snake_case_		    =     use_labels
							snake_case_		    =     use_mc_token_ids
							snake_case_		    =     vocab_size
							snake_case_		    =     hidden_size
							snake_case_		    =     num_hidden_layers
							snake_case_		    =     num_attention_heads
							snake_case_		    =     intermediate_size
							snake_case_		    =     hidden_act
							snake_case_		    =     hidden_dropout_prob
							snake_case_		    =     attention_probs_dropout_prob
							snake_case_		    =     max_position_embeddings
							snake_case_		    =     type_vocab_size
							snake_case_		    =     type_sequence_label_size
							snake_case_		    =     initializer_range
							snake_case_		    =     num_labels
							snake_case_		    =     num_choices
							snake_case_		    =     scope
							snake_case_		    =     self.vocab_size - 1






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size						)

							snake_case_		    =     None
							if self.use_input_mask:
								snake_case_		    =     random_attention_mask([self.batch_size, self.seq_length]						)

							snake_case_		    =     None
							if self.use_token_type_ids:
								snake_case_		    =     ids_tensor([self.batch_size, self.seq_length]       ,	self.type_vocab_size						)

							snake_case_		    =     None
							if self.use_mc_token_ids:
								snake_case_		    =     ids_tensor([self.batch_size, self.num_choices]       ,	self.seq_length						)

							snake_case_		    =     None
							snake_case_		    =     None
							snake_case_		    =     None
							if self.use_labels:
								snake_case_		    =     ids_tensor([self.batch_size]       ,	self.type_sequence_label_size						)
								snake_case_		    =     ids_tensor([self.batch_size, self.seq_length]       ,	self.num_labels						)
								snake_case_		    =     ids_tensor([self.batch_size]       ,	self.num_choices						)

							snake_case_		    =     self.get_config()

							snake_case_		    =     ids_tensor([self.num_hidden_layers, self.num_attention_heads]       ,	2						)

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							    token_type_ids,
							    mc_token_ids,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							)






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							return CTRLConfig(
							    vocab_size=self.vocab_size       ,	n_embd=self.hidden_size       ,	n_layer=self.num_hidden_layers       ,	n_head=self.num_attention_heads       ,	n_positions=self.max_position_embeddings       ,	pad_token_id=self.pad_token_id       ,	)






						def        snake_case__						(						self							:     Any       ,	__lowercase							:     Optional[int]       ,	__lowercase							:     Dict       ,	__lowercase							:     List[Any]       ,	__lowercase							:     Any       ,	__lowercase							:     str       ,	*__lowercase							:     str						):






							"""simple docstring"""






							snake_case_		    =     CTRLModel(config=lowercase_						)
							model.to(lowercase_						)
							model.eval()

							model(lowercase_       ,	token_type_ids=lowercase_       ,	head_mask=lowercase_						)
							model(lowercase_       ,	token_type_ids=lowercase_						)
							snake_case_		    =     model(lowercase_						)
							self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)						)
							self.parent.assertEqual(len(result.past_key_values						)       ,	config.n_layer						)






						def        snake_case__						(						self							:     Optional[int]       ,	__lowercase							:     Dict       ,	__lowercase							:     Tuple       ,	__lowercase							:     Dict       ,	__lowercase							:     List[str]       ,	__lowercase							:     Any       ,	*__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     CTRLLMHeadModel(lowercase_						)
							model.to(lowercase_						)
							model.eval()

							snake_case_		    =     model(lowercase_       ,	token_type_ids=lowercase_       ,	labels=lowercase_						)
							self.parent.assertEqual(result.loss.shape       ,	()						)
							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size)						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     self.prepare_config_and_inputs()

							(
							    snake_case_
							)		    =     config_and_inputs

							snake_case_		    =     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

							return config, inputs_dict






						def        snake_case__						(						self							:     Any       ,	__lowercase							:     str       ,	__lowercase							:     str       ,	__lowercase							:     str       ,	__lowercase							:     Optional[int]       ,	*__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     self.num_labels
							snake_case_		    =     CTRLForSequenceClassification(lowercase_						)
							model.to(lowercase_						)
							model.eval()
							snake_case_		    =     ids_tensor([self.batch_size]       ,	self.type_sequence_label_size						)
							snake_case_		    =     model(lowercase_       ,	token_type_ids=lowercase_       ,	labels=lowercase_						)
							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)						)







@require_torch
class 		UpperCAmelCase   (				a_       ,   a_       ,   a_       ,   unittest.TestCase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
						lowerCAmelCase_     		=   (CTRLLMHeadModel,) if is_torch_available() else ()
						lowerCAmelCase_     		=   (
						    {
						        '''feature-extraction''': CTRLModel,
						        '''text-classification''': CTRLForSequenceClassification,
						        '''text-generation''': CTRLLMHeadModel,
						        '''zero-shot''': CTRLForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						lowerCAmelCase_     		=   True
						lowerCAmelCase_     		=   False
						lowerCAmelCase_     		=   False






						def        snake_case__						(						self							:     Dict       ,	__lowercase							:     Dict       ,	__lowercase							:     Any       ,	__lowercase							:     List[str]       ,	__lowercase							:     str       ,	__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
								# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
								# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
								# config could not be created.
								return True

							return False






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     CTRLModelTester(self						)
							snake_case_		    =     ConfigTester(self       ,	config_class=lowercase_       ,	n_embd=37						)






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							super().tearDown()
							# clean-up as much as possible GPU memory occupied by PyTorch
							gc.collect()
							torch.cuda.empty_cache()






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_ctrl_model(*lowercase_						)






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head_model(*lowercase_						)






						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
						def        snake_case__						(						self							:     List[str]						):






							"""simple docstring"""






							pass






						@slow
						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_		    =     CTRLModel.from_pretrained(lowercase_						)
								self.assertIsNotNone(lowercase_						)






						@unittest.skip("The model doesn\'t support left padding"						)  # and it's not used enough to be worth fixing :)
						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							pass







@require_torch
class 		UpperCAmelCase   (				unittest.TestCase			):







						'''simple docstring'''





						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							super().tearDown()
							# clean-up as much as possible GPU memory occupied by PyTorch
							gc.collect()
							torch.cuda.empty_cache()






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






							"""simple docstring"""






							snake_case_		    =     CTRLLMHeadModel.from_pretrained("ctrl"						)
							model.to(lowercase_						)
							snake_case_		    =     torch.tensor(
							    [[1_18_59, 0, 16_11, 8]]       ,	dtype=torch.long       ,	device=lowercase_						)  # Legal the president is
							snake_case_		    =     [
							    1_18_59,
							    0,
							    16_11,
							    8,
							    5,
							    1_50,
							    2_64_49,
							    2,
							    19,
							    3_48,
							    4_69,
							    3,
							    25_95,
							    48,
							    2_07_40,
							    24_65_33,
							    24_65_33,
							    19,
							    30,
							    5,
							]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

							snake_case_		    =     model.generate(lowercase_       ,	do_sample=lowercase_						)
							self.assertListEqual(output_ids[0].tolist()       ,	lowercase_						)



"""simple docstring"""





# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def   __SCREAMING_SNAKE_CASE  (      A_=None ):
	if subparsers is not None:
		lowerCAmelCase__	:						Optional[Any]  				=	subparsers.add_parser('''test''' )
	else:
		lowerCAmelCase__	:						List[str]  				=	argparse.ArgumentParser('''Accelerate test command''' )

	parser.add_argument(
	    '''--config_file'''						,     default=A_						,     help=(
	        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
	        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
	        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
	        '''with \'huggingface\'.'''
	    )						,     )

	if subparsers is not None:
		parser.set_defaults(func=A_ )
	return parser

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Optional[int]  				=	os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )

	if args.config_file is None:
		lowerCAmelCase__	:						Optional[Any]  				=	script_name
	else:
		lowerCAmelCase__	:						Any  				=	f'--config_file={args.config_file} {script_name}'

	lowerCAmelCase__	:						List[Any]  				=	['''accelerate-launch'''] + test_args.split()
	lowerCAmelCase__	:						int  				=	execute_subprocess_async(A_						,     env=os.environ.copy() )
	if result.returncode == 0:
		print('''Test is a success! You are ready for your distributed training!''' )

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Any  				=	test_command_parser()
	lowerCAmelCase__	:						List[Any]  				=	parser.parse_args()
	test_command(A_ )


if __name__ == "__main__":
							main()




