




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 		__A   :

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

										__magic_name__						:						int										=parent
										__magic_name__						:						str										=batch_size
										__magic_name__						:						str										=seq_length
										__magic_name__						:						Tuple										=is_training
										__magic_name__						:						List[str]										=use_token_type_ids
										__magic_name__						:						Tuple										=use_input_mask
										__magic_name__						:						Any										=use_labels
										__magic_name__						:						str										=use_mc_token_ids
										__magic_name__						:						List[Any]										=vocab_size
										__magic_name__						:						List[str]										=hidden_size
										__magic_name__						:						Tuple										=num_hidden_layers
										__magic_name__						:						Union[str, Any]										=num_attention_heads
										__magic_name__						:						Tuple										=intermediate_size
										__magic_name__						:						Optional[int]										=hidden_act
										__magic_name__						:						Tuple										=hidden_dropout_prob
										__magic_name__						:						str										=attention_probs_dropout_prob
										__magic_name__						:						Dict										=max_position_embeddings
										__magic_name__						:						Dict										=type_vocab_size
										__magic_name__						:						Any										=type_sequence_label_size
										__magic_name__						:						Optional[Any]										=initializer_range
										__magic_name__						:						Optional[Any]										=num_labels
										__magic_name__						:						Any										=num_choices
										__magic_name__						:						List[str]										=scope
										__magic_name__						:						Optional[Any]										=self.vocab_size - 1

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size			)

										__magic_name__						:						Any										=None
										if self.use_input_mask:
														__magic_name__						:						Optional[int]										=random_attention_mask([self.batch_size, self.seq_length]			)

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

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

										__magic_name__						:						Optional[Any]										=None
										__magic_name__						:						str										=None
										__magic_name__						:						List[str]										=None
										if self.use_labels:
														__magic_name__						:						int										=ids_tensor([self.batch_size]     ,			self.type_sequence_label_size			)
														__magic_name__						:						List[Any]										=ids_tensor([self.batch_size, self.seq_length]     ,			self.num_labels			)
														__magic_name__						:						List[Any]										=ids_tensor([self.batch_size]     ,			self.num_choices			)

										__magic_name__						:						Optional[int]										=self.get_config()

										__magic_name__						:						List[Any]										=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      A__    (		self :List[Any]			):
										'''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      A__    (		self :int     ,			__snake_case :Dict     ,			__snake_case :Optional[Any]     ,			__snake_case :Tuple     ,			__snake_case :Dict     ,			__snake_case :Dict     ,			*__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=CTRLModel(config=__snake_case			)
										model.to(__snake_case			)
										model.eval()

										model(__snake_case     ,			token_type_ids=__snake_case     ,			head_mask=__snake_case			)
										model(__snake_case     ,			token_type_ids=__snake_case			)
										__magic_name__						:						int										=model(__snake_case			)
										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      A__    (		self :Dict     ,			__snake_case :Tuple     ,			__snake_case :str     ,			__snake_case :Dict     ,			__snake_case :Tuple     ,			__snake_case :List[Any]     ,			*__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						str										=CTRLLMHeadModel(__snake_case			)
										model.to(__snake_case			)
										model.eval()

										__magic_name__						:						Any										=model(__snake_case     ,			token_type_ids=__snake_case     ,			labels=__snake_case			)
										self.parent.assertEqual(result.loss.shape     ,			()			)
										self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.seq_length, self.vocab_size)			)

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.prepare_config_and_inputs()

										(
										    (
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			(
										    __magic_name__
										)					,			
										)						:						Any										=config_and_inputs

										__magic_name__						:						str										={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """head_mask""": head_mask}

										return config, inputs_dict


						def      A__    (		self :Union[str, Any]     ,			__snake_case :Union[str, Any]     ,			__snake_case :str     ,			__snake_case :Optional[int]     ,			__snake_case :str     ,			*__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.num_labels
										__magic_name__						:						str										=CTRLForSequenceClassification(__snake_case			)
										model.to(__snake_case			)
										model.eval()
										__magic_name__						:						str										=ids_tensor([self.batch_size]     ,			self.type_sequence_label_size			)
										__magic_name__						:						Any										=model(__snake_case     ,			token_type_ids=__snake_case     ,			labels=__snake_case			)
										self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.num_labels)			)




@require_torch
class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
						UpperCamelCase      =       (CTRLLMHeadModel,) if is_torch_available() else ()
						UpperCamelCase      =       (
						    {
						        """feature-extraction""": CTRLModel,
						        """text-classification""": CTRLForSequenceClassification,
						        """text-generation""": CTRLLMHeadModel,
						        """zero-shot""": CTRLForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCamelCase      =       True
						UpperCamelCase      =       False
						UpperCamelCase      =       False

						def      A__    (		self :Any     ,			__snake_case :List[Any]     ,			__snake_case :List[str]     ,			__snake_case :List[Any]     ,			__snake_case :List[str]     ,			__snake_case :int			):
										'''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      A__    (		self :str			):
										'''simple docstring'''

										__magic_name__						:						Any										=CTRLModelTester(self			)
										__magic_name__						:						Optional[int]										=ConfigTester(self     ,			config_class=__snake_case     ,			n_embd=37			)

						def      A__    (		self :Any			):
										'''simple docstring'''

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

						def      A__    (		self :Any			):
										'''simple docstring'''

										self.config_tester.run_common_tests()

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_ctrl_model(*__snake_case			)

						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*__snake_case			)

						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										pass

						@slow
						def      A__    (		self :Any			):
										'''simple docstring'''

										for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__magic_name__						:						Tuple										=CTRLModel.from_pretrained(__snake_case			)
														self.assertIsNotNone(__snake_case			)


						@unittest.skip("""The model doesn't support left padding"""			)  # and it's not used enough to be worth fixing :)
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										pass




@require_torch
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Dict			):
										'''simple docstring'''

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


						@slow
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						int										=CTRLLMHeadModel.from_pretrained("""ctrl"""			)
										model.to(__snake_case			)
										__magic_name__						:						Optional[Any]										=torch.tensor(
										    [[1_18_59, 0, 16_11, 8]]     ,			dtype=torch.long     ,			device=__snake_case			)  # Legal the president is
										__magic_name__						:						Optional[int]										=[
										    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

										__magic_name__						:						str										=model.generate(__snake_case     ,			do_sample=__snake_case			)
										self.assertListEqual(output_ids[0].tolist()     ,			__snake_case			)










'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Optional[int]			,							lowercase_       :   NestedDataStructureLike[PathLike]			,							lowercase_       :   Optional[NamedSplit] = None			,							lowercase_       :   Optional[Features] = None			,							lowercase_       :   str = None			,							lowercase_       :   bool = False			,							lowercase_       :   bool = False			,							lowercase_       :   Optional[str] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   Optional[Any]			,							):
									super().__init__(
									    lowercase_			,							split=lowercase_			,							features=lowercase_			,							cache_dir=lowercase_			,							keep_in_memory=lowercase_			,							streaming=lowercase_			,							num_proc=lowercase_			,							**lowercase_			,							)
									snake_case_		     =							field
									snake_case_		     =							path_or_paths if isinstance(lowercase_			,							lowercase_   ) else {self.split: path_or_paths}
									snake_case_		     =							Json(
									    cache_dir=lowercase_			,							data_files=lowercase_			,							features=lowercase_			,							field=lowercase_			,							**lowercase_			,							)


				def     A_					(						self       :   Optional[Any]   ):
									# Build iterable dataset
									if self.streaming:
														snake_case_		     =							self.builder.as_streaming_dataset(split=self.split   )
									# Build regular (map-style) dataset
									else:
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None

														self.builder.download_and_prepare(
														    download_config=lowercase_			,							download_mode=lowercase_			,							verification_mode=lowercase_			,							base_path=lowercase_			,							num_proc=self.num_proc			,							)
														snake_case_		     =							self.builder.as_dataset(
														    split=self.split			,							verification_mode=lowercase_			,							in_memory=self.keep_in_memory   )
									return dataset







class 		a							:


				def __init__(						self       :   Dict			,							lowercase_       :   Dataset			,							lowercase_       :   Union[PathLike, BinaryIO]			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   List[str]			,							):
									if num_proc is not None and num_proc <= 0:
														raise ValueError(F"num_proc {num_proc} must be an integer > 0."   )

									snake_case_		     =							dataset
									snake_case_		     =							path_or_buf
									snake_case_		     =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									snake_case_		     =							num_proc
									snake_case_		     =							'''utf-8'''
									snake_case_		     =							to_json_kwargs


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.to_json_kwargs.pop('''path_or_buf'''			,							lowercase_   )
									snake_case_		     =							self.to_json_kwargs.pop('''orient'''			,							'''records'''   )
									snake_case_		     =							self.to_json_kwargs.pop('''lines'''			,							True if orient == '''records''' else False   )
									snake_case_		     =							self.to_json_kwargs.pop('''index'''			,							False if orient in ['''split''', '''table'''] else True   )
									snake_case_		     =							self.to_json_kwargs.pop('''compression'''			,							lowercase_   )

									if compression not in [None, "infer", "gzip", "bz2", "xz"]:
														raise NotImplementedError(F"`datasets` currently does not support {compression} compression"   )

									if isinstance(self.path_or_buf			,							(str, bytes, os.PathLike)   ):
														with fsspec.open(self.path_or_buf			,							'''wb'''			,							compression=lowercase_   ) as buffer:
																			snake_case_		     =							self._write(file_obj=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									else:
														if compression:
																			raise NotImplementedError(
																			    F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
																			    ''' was passed. Please provide a local path instead.'''   )
														snake_case_		     =							self._write(
														    file_obj=self.path_or_buf			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									return written


				def     A_					(						self       :   List[str]			,							lowercase_       :   Dict   ):
									snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							args

									snake_case_		     =							query_table(
									    table=self.dataset.data			,							key=slice(lowercase_			,							offset + self.batch_size   )			,							indices=self.dataset._indices			,							)
									snake_case_		     =							batch.to_pandas().to_json(
									    path_or_buf=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**lowercase_   )
									if not json_str.endswith('''\n'''   ):
														json_str += "\n"
									return json_str.encode(self.encoding   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   BinaryIO			,							lowercase_       :   List[Any]			,							lowercase_       :   str			,							lowercase_       :   int			,							**lowercase_       :   Optional[int]			,							):
									snake_case_		     =							0

									if self.num_proc is None or self.num_proc == 1:
														for offset in logging.tqdm(
														    range(0			,							len(self.dataset   )			,							self.batch_size   )			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																			snake_case_		     =							self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
																			written += file_obj.write(lowercase_   )
									else:
														snake_case_     ,snake_case_		     =							len(self.dataset   ), self.batch_size
														with multiprocessing.Pool(self.num_proc   ) as pool:
																			for json_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_json			,							[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,							lowercase_			,							lowercase_   )]			,							)			,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																								written += file_obj.write(lowercase_   )

									return written

