






import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Dict		,     __A:   Optional[Any]		,     __A:   str=sys.maxsize   )    ->				str:
												_A								= "bilinear"
												_A								= max_size
												_A								= short_edge_length



							def __call__(  self:   Union[str, Any]		,     __A:   List[str]   )    ->				Union[str, Any]:
												_A								= []
												for img in imgs:
																	_A								= img.shape[:2]
																	# later: provide list and randomly choose index for resize
																	_A								= np.random.randint(self.short_edge_length[0]		,     self.short_edge_length[1] + 1   )
																	if size == 0:
																						return img
																	_A								= size * 1.0 / min(lowerCAmelCase__		,     lowerCAmelCase__   )
																	if h < w:
																						_A								= size, scale * w
																	else:
																						_A								= scale * h, size
																	if max(lowerCAmelCase__		,     lowerCAmelCase__   ) > self.max_size:
																						_A								= self.max_size * 1.0 / max(lowerCAmelCase__		,     lowerCAmelCase__   )
																						_A								= newh * scale
																						_A								= neww * scale
																	_A								= int(neww + 0.5   )
																	_A								= int(newh + 0.5   )

																	if img.dtype == np.uinta:
																						_A								= Image.fromarray(lowerCAmelCase__   )
																						_A								= pil_image.resize((neww, newh)		,     PILImageResampling.BILINEAR   )
																						_A								= np.asarray(lowerCAmelCase__   )
																	else:
																						_A								= img.permute(2		,     0		,     1   ).unsqueeze(0   )  # 3, 0, 1)  # hw(c) -> nchw
																						_A								= nn.functional.interpolate(
																						    lowerCAmelCase__		,     (newh, neww)		,     mode=self.interp_method		,     align_corners=lowerCAmelCase__   ).squeeze(0   )
																	img_augs.append(lowerCAmelCase__   )

												return img_augs





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Optional[int]		,     __A:   Union[str, Any]   )    ->				Optional[Any]:
												_A								= ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		,     cfg.INPUT.MAX_SIZE_TEST   )
												_A								= cfg.INPUT.FORMAT
												_A								= cfg.SIZE_DIVISIBILITY
												_A								= cfg.PAD_VALUE
												_A								= cfg.INPUT.MAX_SIZE_TEST
												_A								= cfg.MODEL.DEVICE
												_A								= torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )		,     1		,     1   )
												_A								= torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )		,     1		,     1   )
												_A								= lambda __A   : (x - self.pixel_mean) / self.pixel_std

							def       __A (  self:   int		,     __A:   Tuple   )    ->				str:
												_A								= tuple(max(lowerCAmelCase__   ) for s in zip(*[img.shape for img in images]   )   )
												_A								= [im.shape[-2:] for im in images]
												_A								= [
												    nn.functional.pad(
												        lowerCAmelCase__		,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,     value=self.pad_value		,     )
												    for size, im in zip(lowerCAmelCase__		,     lowerCAmelCase__   )
												]

												return torch.stack(lowerCAmelCase__   ), torch.tensor(lowerCAmelCase__   )



							def __call__(  self:   Union[str, Any]		,     __A:   Union[str, Any]		,     __A:   Any=False   )    ->				List[Any]:
												with torch.no_grad():
																	if not isinstance(lowerCAmelCase__		,     lowerCAmelCase__   ):
																						_A								= [images]
																	if single_image:
																						assert len(lowerCAmelCase__   ) == 1
																	for i in range(len(lowerCAmelCase__   )   ):
																						if isinstance(images[i]		,     torch.Tensor   ):
																											images.insert(lowerCAmelCase__		,     images.pop(lowerCAmelCase__   ).to(self.device   ).float()   )
																						elif not isinstance(images[i]		,     torch.Tensor   ):
																											images.insert(
																											    lowerCAmelCase__		,     torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase__   )		,     input_format=self.input_format   )   )
																											    .to(self.device   )
																											    .float()		,     )
            # resize smallest edge
																	_A								= torch.tensor([im.shape[:2] for im in images]   )
																	_A								= self.aug(lowerCAmelCase__   )
																	# transpose images and convert to torch tensors
																	# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																	# now normalize before pad to avoid useless arithmetic
																	_A								= [self.normalizer(lowerCAmelCase__   ) for x in images]
																	# now pad them to do the following operations
																	_A								= self.pad(lowerCAmelCase__   )
																	# Normalize

																	if self.size_divisibility > 0:
																						raise NotImplementedError()
																	# pad
																	_A								= torch.true_divide(lowerCAmelCase__		,     lowerCAmelCase__   )
																	if single_image:
																						return images[0], sizes[0], scales_yx[0]
																	else:
																						return images, sizes, scales_yx




def      __A					(     _lowercase     :   Optional[Any]					,			_lowercase     :   Any ):

					'''simple docstring'''







					boxes[:, 0::2] *= scale_yx[:, 1]
					boxes[:, 1::2] *= scale_yx[:, 0]
					return boxes




def      __A					(     _lowercase     :   Optional[Any]					,			_lowercase     :   Tuple[int, int] ):

					'''simple docstring'''







					assert torch.isfinite(_lowercase ).all(), "Box tensor contains infinite or NaN!"
					_A								= box_size
					tensor[:, 0].clamp_(min=0					,			max=_lowercase )
					tensor[:, 1].clamp_(min=0					,			max=_lowercase )
					tensor[:, 2].clamp_(min=0					,			max=_lowercase )
					tensor[:, 3].clamp_(min=0					,			max=_lowercase )











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 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Tuple		,     __A:   Any		,     __A:   List[Any]=14		,     __A:   Dict=7		,     __A:   List[str]=True		,     __A:   Tuple=True		,     __A:   Union[str, Any]=True		,     __A:   List[Any]=True		,     __A:   Optional[int]=True		,     __A:   Tuple=99		,     __A:   Optional[Any]=32		,     __A:   List[str]=5		,     __A:   Dict=4		,     __A:   str=37		,     __A:   Dict="gelu"		,     __A:   List[str]=0.1		,     __A:   str=0.1		,     __A:   Any=5_12		,     __A:   Union[str, Any]=16		,     __A:   List[Any]=2		,     __A:   Tuple=0.02		,     __A:   Tuple=3		,     __A:   Union[str, Any]=4		,     __A:   Any=None		,     )    ->				Optional[Any]:
												_A								= parent
												_A								= batch_size
												_A								= seq_length
												_A								= is_training
												_A								= use_token_type_ids
												_A								= use_input_mask
												_A								= use_labels
												_A								= use_mc_token_ids
												_A								= vocab_size
												_A								= hidden_size
												_A								= num_hidden_layers
												_A								= num_attention_heads
												_A								= intermediate_size
												_A								= hidden_act
												_A								= hidden_dropout_prob
												_A								= attention_probs_dropout_prob
												_A								= max_position_embeddings
												_A								= type_vocab_size
												_A								= type_sequence_label_size
												_A								= initializer_range
												_A								= num_labels
												_A								= num_choices
												_A								= scope
												_A								= self.vocab_size - 1

							def       __A (  self:   Optional[int]   )    ->				Union[str, Any]:
												_A								= ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size   )

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

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

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

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

												_A								= self.get_config()

												_A								= 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:   Optional[int]   )    ->				List[Any]:
												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:   Union[str, Any]		,     __A:   Union[str, Any]		,     __A:   Dict		,     __A:   Optional[int]		,     __A:   List[str]		,     __A:   List[str]		,     *__A:   Optional[int]   )    ->				Optional[Any]:
												_A								= CTRLModel(config=__A   )
												model.to(__A   )
												model.eval()

												model(__A		,     token_type_ids=__A		,     head_mask=__A   )
												model(__A		,     token_type_ids=__A   )
												_A								= 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       __A (  self:   Optional[Any]		,     __A:   List[str]		,     __A:   Dict		,     __A:   List[Any]		,     __A:   List[Any]		,     __A:   Any		,     *__A:   Any   )    ->				str:
												_A								= CTRLLMHeadModel(__A   )
												model.to(__A   )
												model.eval()

												_A								= 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       __A (  self:   Optional[int]   )    ->				Dict:
												_A								= self.prepare_config_and_inputs()

												(
												    (
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,(
												    _A
												)							,
												)								= config_and_inputs

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

												return config, inputs_dict



							def       __A (  self:   List[str]		,     __A:   Dict		,     __A:   Dict		,     __A:   Tuple		,     __A:   List[Any]		,     *__A:   Optional[int]   )    ->				Any:
												_A								= self.num_labels
												_A								= CTRLForSequenceClassification(__A   )
												model.to(__A   )
												model.eval()
												_A								= ids_tensor([self.batch_size]		,     self.type_sequence_label_size   )
												_A								= model(__A		,     token_type_ids=__A		,     labels=__A   )
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)   )





@require_torch
class 						SCREAMING_SNAKE_CASE				( snake_case  ,	snake_case  ,	snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

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

							def       __A (  self:   Any		,     __A:   List[Any]		,     __A:   int		,     __A:   Optional[Any]		,     __A:   Optional[int]		,     __A:   List[Any]   )    ->				List[str]:
												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:   Any   )    ->				Union[str, Any]:
												_A								= CTRLModelTester(self   )
												_A								= ConfigTester(self		,     config_class=__A		,     n_embd=37   )

							def       __A (  self:   Optional[int]   )    ->				List[Any]:
												super().tearDown()
												# clean-up as much as possible GPU memory occupied by PyTorch
												gc.collect()
												torch.cuda.empty_cache()

							def       __A (  self:   Dict   )    ->				Any:
												self.config_tester.run_common_tests()

							def       __A (  self:   str   )    ->				Optional[Any]:
												_A								= self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_ctrl_model(*__A   )

							def       __A (  self:   List[str]   )    ->				Any:
												_A								= 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       __A (  self:   Optional[Any]   )    ->				int:
												pass

							@slow
							def       __A (  self:   Tuple   )    ->				Dict:
												for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_A								= 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       __A (  self:   Any   )    ->				Union[str, Any]:
												pass





@require_torch
class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

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



							@slow
							def       __A (  self:   Any   )    ->				Any:
												_A								= CTRLLMHeadModel.from_pretrained('''ctrl'''   )
												model.to(__A   )
												_A								= torch.tensor(
												    [[1_18_59, 0, 16_11, 8]]		,     dtype=torch.long		,     device=__A   )  # Legal the president is
												_A								= [
												    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

												_A								= model.generate(__A		,     do_sample=__A   )
												self.assertListEqual(output_ids[0].tolist()		,     __A   )



