






"""simple docstring"""







import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa


class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]




						def __init__(   self       ,  snake_case__			):
												'''simple docstring'''
												assert Path(hparams.data_dir			).exists()
												lowercase__				:      Union[str, Any]=       Path(hparams.output_dir			)
												self.output_dir.mkdir(exist_ok=snake_case__			)

												lowercase__				:      List[str]=       self.output_dir.joinpath("student"			)

												lowercase__				:      Union[str, Any]=       str(snake_case__			)  # Tell lightning we are training the student
												lowercase__				:      Optional[Any]=       AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher			).eval()
												use_task_specific_params(snake_case__       ,  hparams.task			)  # We copy good generation parameters to student by default
												if hparams.student is not None:
																		lowercase__				:      Any=       AutoModelForSeqaSeqLM.from_pretrained(hparams.student			)
																		use_task_specific_params(snake_case__       ,  hparams.task			)
																		lowercase__,	lowercase__				:      Optional[Any]=       None, None
												else:
																		lowercase__,	lowercase__,	lowercase__				:      Optional[Any]=       create_student_by_copying_alternating_layers(
																		    snake_case__       ,  e=hparams.student_encoder_layers       ,  d=hparams.student_decoder_layers       ,  save_path=snake_case__			)

												if hparams.length_penalty != -1:
																		lowercase__				:      Dict=       hparams.length_penalty
												lowercase__				:      int=       hparams.teacher  # Use teacher's tokenizer
												super().__init__(snake_case__       ,  model=snake_case__       ,  config=student.config			)
												assert student.config.model_type == teacher.config.model_type, (
												    F'''teacher, student model types should be the same, got {student.config.model_type} !='''
												    F''' {teacher.config.model_type}'''
												)

												if student.config.model_type == "t5":
																		lowercase__				:      str=       len(student.get_encoder().block			)
																		lowercase__				:      Optional[int]=       len(student.get_decoder().block			)
																		lowercase__				:      List[str]=       len(teacher.get_encoder().block			)
																		lowercase__				:      Union[str, Any]=       len(teacher.get_decoder().block			)
												else:
																		lowercase__				:      Dict=       student.config.encoder_layers
																		lowercase__				:      str=       student.config.decoder_layers
																		lowercase__				:      Optional[Any]=       teacher.config.encoder_layers
																		lowercase__				:      Dict=       teacher.config.decoder_layers

												lowercase__				:      Dict=       not (hparams.student is None or hparams.teacher == hparams.student)
												lowercase__				:      List[Any]=       (not self.different_base_models) and hparams.alpha_hid > 0
												lowercase__				:      Optional[Any]=       self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
												# self.different_encoder determines whether we need to run the teacher encoder
												lowercase__				:      Union[str, Any]=       teacher
												freeze_params(self.teacher			)

												if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
																		try:
																								del self.teacher.model.encoder
																		except AttributeError:  # T5
																								del self.teacher.encoder

												if e_layer_ids is None:
																		lowercase__				:      Optional[Any]=       list(range(snake_case__			)			)
												if d_layer_ids is None:
																		lowercase__				:      Optional[int]=       list(range(snake_case__			)			)

												lowercase__,	lowercase__				:      str=       e_layer_ids, d_layer_ids  # type: List[int], List[int]

												if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
																		if hparams.supervise_forward:
																								lowercase__				:      List[str]=       get_layers_to_supervise(
																								    n_student=len(self.e_layer_ids			)       ,  n_teacher=snake_case__			)
																								lowercase__				:      int=       get_layers_to_supervise(
																								    n_student=len(self.d_layer_ids			)       ,  n_teacher=snake_case__			)
																		else:  # student layer should emulate hidden states of the teacher layer it was copied from
																								lowercase__				:      Dict=       self.e_layer_ids
																								lowercase__				:      Optional[Any]=       self.d_layer_ids
												else:
																		lowercase__				:      Any=       None
																		lowercase__				:      Optional[int]=       None

												lowercase__				:      List[str]=       nn.KLDivLoss(reduction="batchmean"			)
												lowercase__				:      Tuple=       2.0
												lowercase__				:      int=       hparams.alpha_mlm
												lowercase__				:      List[Any]=       hparams.alpha_ce
												lowercase__				:      Optional[int]=       hparams.alpha_hid
												gc.collect()
												torch.cuda.empty_cache()




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Dict=       mask[:, :, None].expand_as(snake_case__			)
												lowercase__				:      Tuple=       s_logits.size(-1			)
												lowercase__				:      List[str]=       torch.masked_select(snake_case__       ,  snake_case__			)  # (bs * seq_length * voc_size) modulo the 1s in mask
												lowercase__				:      int=       torch.masked_select(snake_case__       ,  snake_case__			)  # (bs * seq_length * voc_size) modulo the 1s in mask
												lowercase__				:      Optional[int]=       s_logits_slct.view(-1       ,  snake_case__			)  # (bs * seq_length, voc_size) modulo the 1s in mask
												lowercase__				:      Any=       t_logits_slct.view(-1       ,  snake_case__			)  # (bs * seq_length, voc_size) modulo the 1s in mask
												assert t_logits_slct.size() == s_logits_slct.size()
												lowercase__				:      Optional[int]=       (
												    self.ce_loss_fct(
												        nn.functional.log_softmax(s_logits_slct / self.temperature       ,  dim=-1			)       ,  nn.functional.softmax(t_logits_slct / self.temperature       ,  dim=-1			)       ,  )
												    * (self.temperature) ** 2
												)
												return loss_ce




						@staticmethod
						def 		UpperCAmelCase_       (   snake_case__       ,  snake_case__			):
												'''simple docstring'''
												SummarizationModule.add_model_specific_args(snake_case__       ,  snake_case__			)
												add_distill_args(snake_case__			)
												return parser




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Tuple=       self.tokenizer.pad_token_id
												lowercase__,	lowercase__,	lowercase__				:      Tuple=       batch["input_ids"], batch["attention_mask"], batch["labels"]
												if isinstance(self.model       ,  snake_case__			):
																		lowercase__				:      Optional[Any]=       self.model._shift_right(snake_case__			)
												else:
																		lowercase__				:      Optional[int]=       shift_tokens_right(snake_case__       ,  snake_case__			)

												# noinspection PyCallingNonCallable
												lowercase__				:      Any=       self(
												    snake_case__       ,  attention_mask=snake_case__       ,  decoder_input_ids=snake_case__       ,  output_hidden_states=self.do_calc_hidden_loss       ,  output_attentions=snake_case__       ,  use_cache=snake_case__       ,  )
												lowercase__				:      Optional[Any]=       student_outputs["logits"]

												# Same cross entropy vs. label smoothing logic as finetune.py
												assert lm_logits.shape[-1] == self.model.config.vocab_size
												if self.hparams.label_smoothing == 0:
																		# Same behavior as modeling_bart.py, besides ignoring pad_token_id
																		lowercase__				:      str=       nn.CrossEntropyLoss(ignore_index=snake_case__			)
																		lowercase__				:      Tuple=       loss_fct(lm_logits.view(-1       ,  lm_logits.shape[-1]			)       ,  labels.view(-1			)			)
												else:
																		lowercase__				:      int=       nn.functional.log_softmax(snake_case__       ,  dim=-1			)
																		lowercase__,	lowercase__				:      Union[str, Any]=       label_smoothed_nll_loss(
																		    snake_case__       ,  snake_case__       ,  self.hparams.label_smoothing       ,  ignore_index=snake_case__			)

												def zero_tensor():
																		return torch.tensor(0.0			).type_as(snake_case__			)

												lowercase__				:      Union[str, Any]=       student_outputs[
												    "encoder_last_hidden_state"
												]  # use this unless self.different_base_models
												lowercase__,	lowercase__				:      List[str]=       zero_tensor(), zero_tensor()
												if self.different_encoder:  # compute encoder hidden state loss
																		lowercase__				:      Any=       self.teacher.get_encoder()(
																		    snake_case__       ,  attention_mask=snake_case__       ,  output_hidden_states=self.do_calc_hidden_loss       ,  )
																		if self.different_base_models:
																								lowercase__				:      Optional[int]=       all_teacher_encoder_outputs["last_hidden_state"]
																		elif self.do_calc_hidden_loss:
																								lowercase__				:      Tuple=       self.calc_hidden_loss(
																								    snake_case__       ,  student_outputs["encoder_hidden_states"]       ,  all_teacher_encoder_outputs["hidden_states"]       ,  self.e_matches       ,  normalize_hidden=self.hparams.normalize_hidden       ,  )

												lowercase__				:      List[str]=       self.teacher(
												    snake_case__       ,  attention_mask=snake_case__       ,  encoder_outputs=(teacher_enc_outputs,)       ,  decoder_input_ids=snake_case__       ,  output_hidden_states=self.do_calc_hidden_loss       ,  use_cache=snake_case__       ,  )
												lowercase__				:      int=       decoder_input_ids.ne(snake_case__			)
												lowercase__				:      Tuple=       self.calc_ce_loss(snake_case__       ,  snake_case__       ,  teacher_outputs["logits"]			)
												if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
																		lowercase__				:      str=       self.calc_hidden_loss(
																		    snake_case__       ,  student_outputs["decoder_hidden_states"]       ,  teacher_outputs["decoder_hidden_states"]       ,  self.d_matches       ,  normalize_hidden=self.hparams.normalize_hidden       ,  )

												lowercase__				:      str=       (
												    self.alpha_ce * loss_ce
												    + self.alpha_mlm * student_lm_loss
												    + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
												)
												return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec




						@staticmethod
						def 		UpperCAmelCase_       (   snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Any=       "expected list or tuple for hidden_states, got tensor of shape: "
												assert not isinstance(snake_case__       ,  torch.Tensor			), F'''{msg}{hidden_states.shape}'''
												assert not isinstance(snake_case__       ,  torch.Tensor			), F'''{msg}{hidden_states_T.shape}'''
												lowercase__				:      str=       attention_mask.to(hidden_states[0]			)
												lowercase__				:      Optional[Any]=       mask.sum() * hidden_states[0].size(-1			)
												lowercase__				:      List[str]=       torch.stack([hidden_states[i] for i in range(len(snake_case__			)			)]			)
												lowercase__				:      Dict=       torch.stack([hidden_states_T[j] for j in matches]			)
												assert student_states.shape == teacher_states.shape, F'''{student_states.shape} != {teacher_states.shape}'''
												if normalize_hidden:
																		lowercase__				:      str=       nn.functional.layer_norm(snake_case__       ,  student_states.shape[1:]			)
																		lowercase__				:      List[Any]=       nn.functional.layer_norm(snake_case__       ,  teacher_states.shape[1:]			)
												lowercase__				:      List[Any]=       nn.functional.mse_loss(snake_case__       ,  snake_case__       ,  reduction="none"			)
												lowercase__				:      Union[str, Any]=       (mse * mask.unsqueeze(0			).unsqueeze(-1			)).sum() / valid_count
												return masked_mse





def 						lowercase__(A						)				->Any:




						"""simple docstring"""



						parser.add_argument("--teacher"					,		type=A						)
						parser.add_argument("--alpha_ce"					,		default=0.8					,		type=A						)
						parser.add_argument("--alpha_mlm"					,		default=0.2					,		type=A						)
						parser.add_argument("--alpha_hid"					,		default=0.0					,		type=A					,		required=A						)
						parser.add_argument("--student"					,		type=A					,		required=A						)
						parser.add_argument("--student_decoder_layers"					,		default=12					,		type=A					,		required=A						)
						parser.add_argument("--student_encoder_layers"					,		default=12					,		type=A					,		required=A						)
						parser.add_argument("--no_teacher"					,		action="store_true"					,		default=A						)
						parser.add_argument("--length_penalty"					,		type=A					,		default=-1						)
						parser.add_argument("--supervise_forward"					,		action="store_true"					,		default=A						)
						parser.add_argument("--normalize_hidden"					,		action="store_true"					,		default=A						)


class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					"translation"
						__lowerCamelCase				       =					["bleu"]
						__lowerCamelCase				       =					"bleu"




						def __init__(   self       ,  snake_case__       ,  **snake_case__			):
												'''simple docstring'''
												super().__init__(snake_case__       ,  **snake_case__			)
												assert hparams.src_lang is not None
												assert hparams.tgt_lang is not None
												lowercase__				:      Any=       hparams.src_lang
												lowercase__				:      List[str]=       hparams.tgt_lang
												if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer       ,  snake_case__			):
																		lowercase__				:      int=       self.tokenizer.lang_code_to_id[hparams.tgt_lang]




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												return calculate_bleu(snake_case__       ,  snake_case__			)




						@staticmethod
						def 		UpperCAmelCase_       (   snake_case__       ,  snake_case__			):
												'''simple docstring'''
												TranslationModule.add_model_specific_args(snake_case__       ,  snake_case__			)
												add_distill_args(snake_case__			)
												return parser





def 						lowercase__(A						)				->Tuple:




						"""simple docstring"""



						if args.no_teacher:
												lowercase__				:      Tuple=       TranslationModule if "translation" in args.task else SummarizationModule
						else:  # DISTILL WITH TEACHER
												lowercase__				:      Any=       TranslationDistiller if "translation" in args.task else SummarizationDistiller
						lowercase__				:      str=       module_cls.__name__
						print(f'''using module {args.setup_cls}'''						)
						lowercase__				:      str=       module_cls(A						)
						return model





def 						lowercase__(A						)				->Dict:




						"""simple docstring"""



						Path(args.output_dir						).mkdir(exist_ok=A						)
						check_output_dir(A					,		expected_items=3						)

						lowercase__				:      Dict=       create_module(A						)
						return ft_main(A					,		model=A						)


if __name__ == "__main__":
			a :				int      					=		argparse.ArgumentParser()
			a :				Dict      					=		pl.Trainer.add_argparse_args(parser)
			a :				int      					=		SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
			a :				List[str]      					=		parser.parse_args()

			distill_main(args)














"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import CLIPImageProcessor


class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__=7       ,  snake_case__=3       ,  snake_case__=18       ,  snake_case__=30       ,  snake_case__=400       ,  snake_case__=True       ,  snake_case__=None       ,  snake_case__=True       ,  snake_case__=None       ,  snake_case__=True       ,  snake_case__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,  snake_case__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,  snake_case__=True       ,  ):
												'''simple docstring'''
												lowercase__				:      int=       size if size is not None else {"shortest_edge": 20}
												lowercase__				:      int=       crop_size if crop_size is not None else {"height": 18, "width": 18}
												lowercase__				:      Union[str, Any]=       parent
												lowercase__				:      Optional[Any]=       batch_size
												lowercase__				:      List[str]=       num_channels
												lowercase__				:      str=       image_size
												lowercase__				:      Optional[int]=       min_resolution
												lowercase__				:      Union[str, Any]=       max_resolution
												lowercase__				:      List[Any]=       do_resize
												lowercase__				:      Any=       size
												lowercase__				:      Optional[Any]=       do_center_crop
												lowercase__				:      List[str]=       crop_size
												lowercase__				:      List[Any]=       do_normalize
												lowercase__				:      Optional[Any]=       image_mean
												lowercase__				:      Dict=       image_std
												lowercase__				:      Dict=       do_convert_rgb




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_convert_rgb": self.do_convert_rgb,
												}




						def 		UpperCAmelCase_       (   self       ,  snake_case__=False       ,  snake_case__=False       ,  snake_case__=False			):
												'''simple docstring'''
												assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

												if equal_resolution:
																		lowercase__				:      Optional[Any]=       []
																		for i in range(self.batch_size			):
																								image_inputs.append(
																								    np.random.randint(
																								        255       ,  size=(self.num_channels, self.max_resolution, self.max_resolution)       ,  dtype=np.uinta			)			)
												else:
																		lowercase__				:      Optional[Any]=       []
																		for i in range(self.batch_size			):
																								lowercase__,	lowercase__				:      str=       np.random.choice(np.arange(self.min_resolution       ,  self.max_resolution			)       ,  2			)
																								image_inputs.append(np.random.randint(255       ,  size=(self.num_channels, width, height)       ,  dtype=np.uinta			)			)

												if not numpify and not torchify:
																		# PIL expects the channel dimension as last dimension
																		lowercase__				:      Dict=       [Image.fromarray(np.moveaxis(snake_case__       ,  0       ,  -1			)			) for x in image_inputs]

												if torchify:
																		lowercase__				:      Tuple=       [torch.from_numpy(snake_case__			) for x in image_inputs]

												return image_inputs


@require_torch
@require_vision
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					CLIPImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[Any]=       CLIPImageProcessingTester(self			)




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Dict=       self.image_processing_class(**self.image_processor_dict			)
												self.assertTrue(hasattr(snake_case__       ,  "do_resize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "size"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_normalize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_mean"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_std"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_convert_rgb"			)			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       self.image_processing_class.from_dict(self.image_processor_dict			)
												self.assertEqual(image_processor.size       ,  {"shortest_edge": 20}			)
												self.assertEqual(image_processor.crop_size       ,  {"height": 18, "width": 18}			)

												lowercase__				:      Dict=       self.image_processing_class.from_dict(self.image_processor_dict       ,  size=42       ,  crop_size=84			)
												self.assertEqual(image_processor.size       ,  {"shortest_edge": 42}			)
												self.assertEqual(image_processor.crop_size       ,  {"height": 84, "width": 84}			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      int=       self.image_processing_class(**self.image_processor_dict			)
												# create random PIL images
												lowercase__				:      Optional[Any]=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  Image.Image			)

												# Test not batched input
												lowercase__				:      List[str]=       image_processing(image_inputs[0]       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      Dict=       image_processing(snake_case__       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      Any=       self.image_processing_class(**self.image_processor_dict			)
												# create random numpy tensors
												lowercase__				:      Tuple=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__       ,  numpify=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  np.ndarray			)

												# Test not batched input
												lowercase__				:      Dict=       image_processing(image_inputs[0]       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      int=       image_processing(snake_case__       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      Optional[Any]=       self.image_processing_class(**self.image_processor_dict			)
												# create random PyTorch tensors
												lowercase__				:      Union[str, Any]=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__       ,  torchify=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  torch.Tensor			)

												# Test not batched input
												lowercase__				:      Any=       image_processing(image_inputs[0]       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      Union[str, Any]=       image_processing(snake_case__       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )


@require_torch
@require_vision
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					CLIPImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       CLIPImageProcessingTester(self       ,  num_channels=4			)
												lowercase__				:      Optional[Any]=       3




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       self.image_processing_class(**self.image_processor_dict			)
												self.assertTrue(hasattr(snake_case__       ,  "do_resize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "size"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_normalize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_mean"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_std"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_convert_rgb"			)			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      int=       self.image_processing_class(**self.image_processor_dict			)
												# create random PIL images
												lowercase__				:      int=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  Image.Image			)

												# Test not batched input
												lowercase__				:      Optional[int]=       image_processing(image_inputs[0]       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        1,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      Any=       image_processing(snake_case__       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )






