




import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						RobertaTokenizer
     _a							 =						RobertaTokenizerFast
     _a							 =						True
     _a							 =						{"cls_token": "<s>"}

     def 					a__	(  self				)						->							Dict:
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           _A							:     Any			=						[
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """\u0120""",
               """\u0120l""",
               """\u0120n""",
               """\u0120lo""",
               """\u0120low""",
               """er""",
               """\u0120lowest""",
               """\u0120newer""",
               """\u0120wider""",
               """<unk>""",
           ]
           _A							:     List[str]			=						dict(zip(_a		,     range(len(_a				)				)				)				)
           _A							:     Union[str, Any]			=						["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
           _A							:     Optional[int]			=						{"""unk_token""": """<unk>"""}

           _A							:     Tuple			=						os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""vocab_file"""]				)
           _A							:     int			=						os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""merges_file"""]				)
           with open(self.vocab_file		,     """w"""		,     encoding="""utf-8"""				) as fp:
                 fp.write(json.dumps(_a				) + """\n"""				)
           with open(self.merges_file		,     """w"""		,     encoding="""utf-8"""				) as fp:
                 fp.write("""\n""".join(_a				)				)

     def 					a__	(  self		,     **_a				)						->							List[Any]:
           kwargs.update(self.special_tokens_map				)
           return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **_a				)

     def 					a__	(  self		,     **_a				)						->							str:
           kwargs.update(self.special_tokens_map				)
           return RobertaTokenizerFast.from_pretrained(self.tmpdirname		,     **_a				)

     def 					a__	(  self		,     _a				)						->							Dict:
           _A							:     Optional[int]			=						"""lower newer"""
           _A							:     int			=						"""lower newer"""
           return input_text, output_text

     def 					a__	(  self				)						->							List[str]:
           _A							:     Tuple			=						self.tokenizer_class(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map				)
           _A							:     List[str]			=						"""lower newer"""
           _A							:     str			=						["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
           _A							:     str			=						tokenizer.tokenize(_a				)  # , add_prefix_space=True)
           self.assertListEqual(_a		,     _a				)

           _A							:     Optional[Any]			=						tokens + [tokenizer.unk_token]
           _A							:     Any			=						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)		,     _a				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Optional[int]			=						self.get_tokenizer()

           self.assertListEqual(tokenizer.encode("""Hello world!"""		,     add_special_tokens=_a				)		,     [0, 3_1414, 232, 328, 2]				)
           self.assertListEqual(
               tokenizer.encode("""Hello world! cécé herlolip 418"""		,     add_special_tokens=_a				)		,     [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]		,     )

     @slow
     def 					a__	(  self				)						->							str:
           _A							:     Union[str, Any]			=						self.tokenizer_class.from_pretrained("""roberta-base"""				)

           _A							:     Optional[int]			=						tokenizer.encode("""sequence builders"""		,     add_special_tokens=_a				)
           _A							:     Union[str, Any]			=						tokenizer.encode("""multi-sequence build"""		,     add_special_tokens=_a				)

           _A							:     Optional[Any]			=						tokenizer.encode(
               """sequence builders"""		,     add_special_tokens=_a		,     add_prefix_space=_a				)
           _A							:     List[str]			=						tokenizer.encode(
               """sequence builders"""		,     """multi-sequence build"""		,     add_special_tokens=_a		,     add_prefix_space=_a				)

           _A							:     List[str]			=						tokenizer.build_inputs_with_special_tokens(_a				)
           _A							:     Dict			=						tokenizer.build_inputs_with_special_tokens(_a		,     _a				)

           assert encoded_sentence == encoded_text_from_decode
           assert encoded_pair == encoded_pair_from_decode

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Optional[int]			=						self.get_tokenizer()

           _A							:     List[Any]			=						"""Encode this sequence."""
           _A							:     Any			=						tokenizer.byte_encoder[""" """.encode("""utf-8"""				)[0]]

           # Testing encoder arguments
           _A							:     Dict			=						tokenizer.encode(_a		,     add_special_tokens=_a		,     add_prefix_space=_a				)
           _A							:     Dict			=						tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
           self.assertNotEqual(_a		,     _a				)

           _A							:     int			=						tokenizer.encode(_a		,     add_special_tokens=_a		,     add_prefix_space=_a				)
           _A							:     Tuple			=						tokenizer.convert_ids_to_tokens(encoded[0]				)[0]
           self.assertEqual(_a		,     _a				)

           tokenizer.add_special_tokens({"""bos_token""": """<s>"""}				)
           _A							:     List[Any]			=						tokenizer.encode(_a		,     add_special_tokens=_a				)
           _A							:     int			=						tokenizer.convert_ids_to_tokens(encoded[1]				)[0]
           self.assertNotEqual(_a		,     _a				)

           # Testing spaces after special tokens
           _A							:     Dict			=						"""<mask>"""
           tokenizer.add_special_tokens(
               {"""mask_token""": AddedToken(_a		,     lstrip=_a		,     rstrip=_a				)}				)  # mask token has a left space
           _A							:     Any			=						tokenizer.convert_tokens_to_ids(_a				)

           _A							:     Union[str, Any]			=						"""Encode <mask> sequence"""
           _A							:     Optional[Any]			=						"""Encode <mask>sequence"""

           _A							:     List[Any]			=						tokenizer.encode(_a				)
           _A							:     int			=						encoded.index(_a				)
           _A							:     Any			=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
           self.assertEqual(_a		,     _a				)

           _A							:     Optional[Any]			=						tokenizer.encode(_a				)
           _A							:     Optional[Any]			=						encoded.index(_a				)
           _A							:     List[str]			=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]				)[0]
           self.assertNotEqual(_a		,     _a				)

     def 					a__	(  self				)						->							Optional[int]:
           pass

     def 					a__	(  self				)						->							Tuple:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
                       _A							:     Any			=						self.rust_tokenizer_class.from_pretrained(_a		,     **_a				)
                       _A							:     int			=						self.tokenizer_class.from_pretrained(_a		,     **_a				)
                       _A							:     Union[str, Any]			=						"""A, <mask> AllenNLP sentence."""
                       _A							:     List[Any]			=						tokenizer_r.encode_plus(_a		,     add_special_tokens=_a		,     return_token_type_ids=_a				)
                       _A							:     Optional[int]			=						tokenizer_p.encode_plus(_a		,     add_special_tokens=_a		,     return_token_type_ids=_a				)

                       # token_type_ids should put 0 everywhere
                       self.assertEqual(sum(tokens_r["""token_type_ids"""]				)		,     sum(tokens_p["""token_type_ids"""]				)				)

                       # attention_mask should put 1 everywhere, so sum over length should be 1
                       self.assertEqual(
                           sum(tokens_r["""attention_mask"""]				) / len(tokens_r["""attention_mask"""]				)		,     sum(tokens_p["""attention_mask"""]				) / len(tokens_p["""attention_mask"""]				)		,     )

                       _A							:     str			=						tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]				)
                       _A							:     Optional[Any]			=						tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]				)

                       # Rust correctly handles the space before the mask while python doesnt
                       self.assertSequenceEqual(tokens_p["""input_ids"""]		,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]				)
                       self.assertSequenceEqual(tokens_r["""input_ids"""]		,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]				)

                       self.assertSequenceEqual(
                           _a		,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)
                       self.assertSequenceEqual(
                           _a		,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]				)

     def 					a__	(  self				)						->							Any:
           for trim_offsets, add_prefix_space in itertools.product([True, False]		,     repeat=2				):
                 _A							:     List[Any]			=						self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)

                 _A							:     Union[str, Any]			=						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()				)
                 _A							:     Union[str, Any]			=						json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()				)

                 self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]		,     _a				)

                 self.assertEqual(post_processor_state["""add_prefix_space"""]		,     _a				)
                 self.assertEqual(post_processor_state["""trim_offsets"""]		,     _a				)



     def 					a__	(  self				)						->							Union[str, Any]:
           # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
           # `trim_offsets`
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
                       _A							:     Optional[int]			=						"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                       _A							:     Dict			=						F'''{text_of_1_token} {text_of_1_token}'''

                       _A							:     Tuple			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     Optional[int]			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (len(_a				) + 1, len(_a				) + 1 + len(_a				))		,     )

                       _A							:     List[str]			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     str			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (len(_a				) + 1, len(_a				) + 1 + len(_a				))		,     )

                       _A							:     Dict			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     List[str]			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (len(_a				), len(_a				) + 1 + len(_a				))		,     )

                       _A							:     int			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     List[Any]			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (len(_a				), len(_a				) + 1 + len(_a				))		,     )

                       _A							:     Dict			=						F''' {text}'''

                       # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                       #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                       # )
                       # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                       # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                       # self.assertEqual(
                       #     encoding.offset_mapping[1],
                       #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                       # )

                       _A							:     Optional[Any]			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     Union[str, Any]			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (1, 1 + len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (1 + len(_a				) + 1, 1 + len(_a				) + 1 + len(_a				))		,     )

                       _A							:     Optional[int]			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     int			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (1 + len(_a				), 1 + len(_a				) + 1 + len(_a				))		,     )

                       _A							:     Optional[int]			=						self.rust_tokenizer_class.from_pretrained(
                           _a		,     use_fast=_a		,     add_prefix_space=_a		,     trim_offsets=_a				)
                       _A							:     Optional[Any]			=						tokenizer_r(_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a				)
                       self.assertEqual(encoding.offset_mapping[0]		,     (0, 1 + len(_a				))				)
                       self.assertEqual(
                           encoding.offset_mapping[1]		,     (1 + len(_a				), 1 + len(_a				) + 1 + len(_a				))		,     )








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							,					_A							,					_A=1_4							,					_A=7							,					_A=True							,					_A=True							,					_A=True							,					_A=True							,					_A=True							,					_A=9_9							,					_A=3_2							,					_A=5							,					_A=4							,					_A=3_7							,					_A="gelu"							,					_A=0.1							,					_A=0.1							,					_A=5_1_2							,					_A=1_6							,					_A=2							,					_A=0.02							,					_A=3							,					_A=4							,					_A=None							,					):






				'''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      _lowercase				(    self		):






				'''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      _lowercase				(    self		):






				'''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      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''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      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''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      _lowercase				(    self		):






				'''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      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''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 	A_       (a_     ,							a_     ,							a_     ,							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      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A		):






				'''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      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLModelTester(self		)
				UpperCAmelCase   		=				ConfigTester(self							,					config_class=_A							,					n_embd=3_7		)
	def      _lowercase				(    self		):






				'''simple docstring'''







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






				'''simple docstring'''







				self.config_tester.run_common_tests()
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*_A		)
	def      _lowercase				(    self		):






				'''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      _lowercase				(    self		):






				'''simple docstring'''







				pass
	@slow
	def      _lowercase				(    self		):






				'''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      _lowercase				(    self		):






				'''simple docstring'''







				pass

@require_torch
class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







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







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLLMHeadModel.from_pretrained('''ctrl'''		)
				model.to(_A		)
				UpperCAmelCase   		=				torch.tensor(
				    [[1_1_8_5_9, 0, 1_6_1_1, 8]]							,					dtype=torch.long							,					device=_A		)  # Legal the president is
				UpperCAmelCase   		=				[
				    1_1_8_5_9,
				    0,
				    1_6_1_1,
				    8,
				    5,
				    1_5_0,
				    2_6_4_4_9,
				    2,
				    1_9,
				    3_4_8,
				    4_6_9,
				    3,
				    2_5_9_5,
				    4_8,
				    2_0_7_4_0,
				    2_4_6_5_3_3,
				    2_4_6_5_3_3,
				    1_9,
				    3_0,
				    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		)



