





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A				:    Dict       =   logging.get_logger(__name__)

class 	A_       (a_			):
	UpperCAmelCase__					=			['''pixel_values''']
	def __init__(    self							,					_A = True							,					_A = None							,					_A = PILImageResampling.BICUBIC							,					_A = True							,					_A = True							,					_A = 1 / 2_5_5							,					_A = None							,					_A = True							,					_A = None							,					_A = None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(**_A		)
				UpperCAmelCase   		=				size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
				UpperCAmelCase   		=				get_size_dict(_A		)
				UpperCAmelCase   		=				crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
				UpperCAmelCase   		=				get_size_dict(_A							,					default_to_square=_A							,					param_name='''crop_size'''		)

				UpperCAmelCase   		=				do_resize
				UpperCAmelCase   		=				do_rescale
				UpperCAmelCase   		=				do_normalize
				UpperCAmelCase   		=				do_center_crop
				UpperCAmelCase   		=				crop_size
				UpperCAmelCase   		=				size
				UpperCAmelCase   		=				resample
				UpperCAmelCase   		=				rescale_factor
				UpperCAmelCase   		=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				UpperCAmelCase   		=				image_std if image_std is not None else IMAGENET_DEFAULT_STD
	def      _lowercase				(    self							,					_A							,					_A							,					_A = PILImageResampling.BILINEAR							,					_A = None							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				get_size_dict(_A		)

				if "shortest_edge" in size:
							UpperCAmelCase   		=				get_resize_output_image_size(_A							,					size=size['''shortest_edge''']							,					default_to_square=_A		)
							# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
				elif "height" in size and "width" in size:
							UpperCAmelCase   		=				(size['''height'''], size['''width'''])
				else:
							raise ValueError(F"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""		)
				return resize(_A							,					size=_A							,					resample=_A							,					data_format=_A							,					**_A		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A = None							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				get_size_dict(_A		)
				if "height" not in size or "width" not in size:
							raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""		)
				return center_crop(_A							,					size=(size['''height'''], size['''width'''])							,					data_format=_A							,					**_A		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A = None							,					**_A		):






				'''simple docstring'''







				return rescale(_A							,					scale=_A							,					data_format=_A							,					**_A		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A = None							,					**_A							,					):






				'''simple docstring'''







				return normalize(_A							,					mean=_A							,					std=_A							,					data_format=_A							,					**_A		)







	def      _lowercase				(    self							,					_A							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = None							,					_A = ChannelDimension.FIRST							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				do_resize if do_resize is not None else self.do_resize
				UpperCAmelCase   		=				do_rescale if do_rescale is not None else self.do_rescale
				UpperCAmelCase   		=				do_normalize if do_normalize is not None else self.do_normalize
				UpperCAmelCase   		=				do_center_crop if do_center_crop is not None else self.do_center_crop
				UpperCAmelCase   		=				crop_size if crop_size is not None else self.crop_size
				UpperCAmelCase   		=				get_size_dict(_A							,					param_name='''crop_size'''							,					default_to_square=_A		)
				UpperCAmelCase   		=				resample if resample is not None else self.resample
				UpperCAmelCase   		=				rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCAmelCase   		=				image_mean if image_mean is not None else self.image_mean
				UpperCAmelCase   		=				image_std if image_std is not None else self.image_std

				UpperCAmelCase   		=				size if size is not None else self.size
				UpperCAmelCase   		=				get_size_dict(_A		)

				if not is_batched(_A		):
							UpperCAmelCase   		=				[images]

				if not valid_images(_A		):
							raise ValueError(
							    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
							    '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

				if do_resize and size is None:
							raise ValueError('''Size must be specified if do_resize is True.'''		)

				if do_center_crop and crop_size is None:
							raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

				if do_rescale and rescale_factor is None:
							raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

				# All transformations expect numpy arrays.
				UpperCAmelCase   		=				[to_numpy_array(_A		) for image in images]

				if do_resize:
							UpperCAmelCase   		=				[self.resize(image=_A							,					size=_A							,					resample=_A		) for image in images]

				if do_center_crop:
							UpperCAmelCase   		=				[self.center_crop(image=_A							,					size=_A		) for image in images]

				if do_rescale:
							UpperCAmelCase   		=				[self.rescale(image=_A							,					scale=_A		) for image in images]

				if do_normalize:
							UpperCAmelCase   		=				[self.normalize(image=_A							,					mean=_A							,					std=_A		) for image in images]

				UpperCAmelCase   		=				[to_channel_dimension_format(_A							,					_A		) for image in images]

				UpperCAmelCase   		=				{'''pixel_values''': images}
				return BatchFeature(data=_A							,					tensor_type=_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		)



