
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     lowercase :



		def __init__(			self    ,				A_    ,				A_=14    ,				A_=7    ,				A_=True    ,				A_=True    ,				A_=True    ,				A_=True    ,				A_=True    ,				A_=99    ,				A_=32    ,				A_=5    ,				A_=4    ,				A_=37    ,				A_="gelu"    ,				A_=0.1    ,				A_=0.1    ,				A_=512    ,				A_=16    ,				A_=2    ,				A_=0.02    ,				A_=3    ,				A_=4    ,				A_=None    ,				)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					seq_length
				UpperCamelCase          =					is_training
				UpperCamelCase          =					use_token_type_ids
				UpperCamelCase          =					use_input_mask
				UpperCamelCase          =					use_labels
				UpperCamelCase          =					use_mc_token_ids
				UpperCamelCase          =					vocab_size
				UpperCamelCase          =					hidden_size
				UpperCamelCase          =					num_hidden_layers
				UpperCamelCase          =					num_attention_heads
				UpperCamelCase          =					intermediate_size
				UpperCamelCase          =					hidden_act
				UpperCamelCase          =					hidden_dropout_prob
				UpperCamelCase          =					attention_probs_dropout_prob
				UpperCamelCase          =					max_position_embeddings
				UpperCamelCase          =					type_vocab_size
				UpperCamelCase          =					type_sequence_label_size
				UpperCamelCase          =					initializer_range
				UpperCamelCase          =					num_labels
				UpperCamelCase          =					num_choices
				UpperCamelCase          =					scope
				UpperCamelCase          =					self.vocab_size - 1



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size	)

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

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

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

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

				UpperCamelCase          =					self.get_config()

				UpperCamelCase          =					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 __UpperCamelCase			(			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 __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				*A_	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					CTRLModel(config=A_	)
				model.to(A_	)
				model.eval()

				model(A_    ,				token_type_ids=A_    ,				head_mask=A_	)
				model(A_    ,				token_type_ids=A_	)
				UpperCamelCase          =					model(A_	)
				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 __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				*A_	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					CTRLLMHeadModel(A_	)
				model.to(A_	)
				model.eval()

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



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					self.prepare_config_and_inputs()

				(
				    (
				    UpperCamelCase  
				)     ,  (
				    UpperCamelCase  
				)     ,  (
				    UpperCamelCase  
				)     ,  (
				    UpperCamelCase  
				)     ,  (
				    UpperCamelCase  
				)     ,  (
				    UpperCamelCase  
				)     ,  (
				    UpperCamelCase  
				)     ,  (
				    UpperCamelCase  
				)     ,  (
				    UpperCamelCase  
				)     ,    
				)          =					config_and_inputs

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

				return config, inputs_dict



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				*A_	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.num_labels
				UpperCamelCase          =					CTRLForSequenceClassification(A_	)
				model.to(A_	)
				model.eval()
				UpperCamelCase          =					ids_tensor([self.batch_size]    ,				self.type_sequence_label_size	)
				UpperCamelCase          =					model(A_    ,				token_type_ids=A_    ,				labels=A_	)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_labels)	)






@require_torch
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	int     					=    (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
		__lowercase	:	Optional[Any]     					=    (CTRLLMHeadModel,) if is_torch_available() else ()
		__lowercase	:	Optional[int]     					=    (
		    {
		        "feature-extraction": CTRLModel,
		        "text-classification": CTRLForSequenceClassification,
		        "text-generation": CTRLLMHeadModel,
		        "zero-shot": CTRLForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		__lowercase	:	int     					=    True
		__lowercase	:	Dict     					=    False
		__lowercase	:	int     					=    False



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> Dict:
				"""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 __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					CTRLModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=A_    ,				n_embd=37	)



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*A_	)



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head_model(*A_	)



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					CTRLModel.from_pretrained(A_	)
						self.assertIsNotNone(A_	)



		@unittest.skip('The model doesn\'t support left padding'	)  # and it's not used enough to be worth fixing :)
		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				pass






@require_torch
class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()



		@slow
		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					CTRLLMHeadModel.from_pretrained('ctrl'	)
				model.to(A_	)
				UpperCamelCase          =					torch.tensor(
				    [[11_859, 0, 1_611, 8]]    ,				dtype=torch.long    ,				device=A_	)  # Legal the president is
				UpperCamelCase          =					[
				    11_859,
				    0,
				    1_611,
				    8,
				    5,
				    150,
				    26_449,
				    2,
				    19,
				    348,
				    469,
				    3,
				    2_595,
				    48,
				    20_740,
				    246_533,
				    246_533,
				    19,
				    30,
				    5,
				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

				UpperCamelCase          =					model.generate(A_    ,				do_sample=A_	)
				self.assertListEqual(output_ids[0].tolist()    ,				A_	)


import logging
import os

from .state import PartialState


class     lowercase (		logging.LoggerAdapter       ):



		@staticmethod
		def __UpperCamelCase			(			A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					PartialState()
				return not main_process_only or (main_process_only and state.is_main_process)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				*A_    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				if PartialState._shared_state == {}:
						raise RuntimeError(
						    'You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'	)
				UpperCamelCase          =					kwargs.pop('main_process_only'    ,				A_	)
				UpperCamelCase          =					kwargs.pop('in_order'    ,				A_	)

				if self.isEnabledFor(A_	):
						if self._should_log(A_	):
								UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
								self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)

						elif in_order:
								UpperCamelCase          =					PartialState()
								for i in range(state.num_processes	):
										if i == state.process_index:
												UpperCamelCase     ,  UpperCamelCase          =					self.process(A_    ,				A_	)
												self.logger.log(A_    ,				A_    ,				*A_    ,				**A_	)
										state.wait_for_everyone()






def 				A	( lowercase   ,	lowercase = None			)       ->  Dict:
		'''simple docstring'''

		if log_level is None:
				UpperCamelCase          =					os.environ.get('ACCELERATE_LOG_LEVEL'   ,	lowercase			)
		UpperCamelCase          =					logging.getLogger(lowercase			)
		if log_level is not None:
				logger.setLevel(log_level.upper()			)
				logger.root.setLevel(log_level.upper()			)
		return MultiProcessAdapter(lowercase   ,	{}			)
