


"""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 ChineseCLIPImageProcessor


class       SCREAMING_SNAKE_CASE__				(		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__=[0.48145466, 0.4578275, 0.40821073]		,     snake_case__=[0.26862954, 0.26130258, 0.27577711]		,     snake_case__=True		,     ):






         """simple docstring"""





         lowerCAmelCase     :     str								=							size if size is not None else {"height": 224, "width": 224}
         lowerCAmelCase     :     str								=							crop_size if crop_size is not None else {"height": 18, "width": 18}
         lowerCAmelCase     :     Union[str, Any]								=							parent
         lowerCAmelCase     :     List[Any]								=							batch_size
         lowerCAmelCase     :     Union[str, Any]								=							num_channels
         lowerCAmelCase     :     Tuple								=							image_size
         lowerCAmelCase     :     Dict								=							min_resolution
         lowerCAmelCase     :     Optional[Any]								=							max_resolution
         lowerCAmelCase     :     int								=							do_resize
         lowerCAmelCase     :     Any								=							size
         lowerCAmelCase     :     Any								=							do_center_crop
         lowerCAmelCase     :     Tuple								=							crop_size
         lowerCAmelCase     :     List[str]								=							do_normalize
         lowerCAmelCase     :     Any								=							image_mean
         lowerCAmelCase     :     Any								=							image_std
         lowerCAmelCase     :     Tuple								=							do_convert_rgb

       def        lowercase__							(						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        lowercase__							(						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:
           lowerCAmelCase     :     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:
           lowerCAmelCase     :     List[str]								=							[]
           for i in range(self.batch_size						):
             lowerCAmelCase						,  lowerCAmelCase     :     Optional[Any]								=							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
           lowerCAmelCase     :     int								=							[Image.fromarray(np.moveaxis(snake_case__		,     0		,     -1						)						) for x in image_inputs]

         if torchify:
           lowerCAmelCase     :     Optional[int]								=							[torch.from_numpy(snake_case__						) for x in image_inputs]

         return image_inputs


@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__				(		lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				int   =ChineseCLIPImageProcessor if is_vision_available() else None

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							ChineseCLIPImageProcessingTester(self		,     do_center_crop=snake_case__						)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							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        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size		,     {"height": 224, "width": 224}						)
         self.assertEqual(image_processor.crop_size		,     {"height": 18, "width": 18}						)

         lowerCAmelCase     :     str								=							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        lowercase__							(						self						):






         """simple docstring"""





         pass

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         lowerCAmelCase     :     Tuple								=							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
         lowerCAmelCase     :     Optional[int]								=							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
         lowerCAmelCase     :     Tuple								=							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        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         lowerCAmelCase     :     Any								=							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
         lowerCAmelCase     :     Optional[int]								=							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
         lowerCAmelCase     :     Tuple								=							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        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         lowerCAmelCase     :     int								=							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
         lowerCAmelCase     :     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
         lowerCAmelCase     :     List[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       SCREAMING_SNAKE_CASE__				(		lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				Optional[int]   =ChineseCLIPImageProcessor if is_vision_available() else None

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							ChineseCLIPImageProcessingTester(self		,     num_channels=4		,     do_center_crop=snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							3

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							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        lowercase__							(						self						):






         """simple docstring"""





         pass







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         lowerCAmelCase     :     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
         lowerCAmelCase     :     Tuple								=							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
         lowerCAmelCase     :     Dict								=							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"],
             )		,     )









"""simple docstring"""







from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
     from fairscale.optim import OSS


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__=None		,     snake_case__=None		,     *snake_case__		,     **snake_case__						):






         """simple docstring"""





         super().__init__(*snake_case__		,     **snake_case__						)

         if config is None:
           assert isinstance(self.model		,     snake_case__						), (
               "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
               f""" {self.model.__class__}"""
           )
           lowerCAmelCase     :     Optional[int]								=							self.model.config
         else:
           lowerCAmelCase     :     List[str]								=							config

         lowerCAmelCase     :     Any								=							data_args
         lowerCAmelCase     :     Tuple								=							self.config.tgt_vocab_size if isinstance(self.config		,     snake_case__						) else self.config.vocab_size

         if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
           assert self.config.pad_token_id is not None, (
               "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
               " calculation or doing label smoothing."
           )

         if self.config.pad_token_id is None and self.config.eos_token_id is not None:
           logger.warning(
               f"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
               " padding.."						)

         if self.args.label_smoothing == 0:
           lowerCAmelCase     :     int								=							torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id						)
         else:
           # dynamically import label_smoothed_nll_loss
           from utils import label_smoothed_nll_loss

           lowerCAmelCase     :     Tuple								=							label_smoothed_nll_loss

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if self.optimizer is None:
           lowerCAmelCase     :     Optional[int]								=							["bias", "LayerNorm.weight"]
           lowerCAmelCase     :     str								=							[
               {
                   "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay						)],
                   "weight_decay": self.args.weight_decay,
               },
               {
                   "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay						)],
                   "weight_decay": 0.0,
               },
           ]
           lowerCAmelCase     :     Union[str, Any]								=							Adafactor if self.args.adafactor else AdamW
           if self.args.adafactor:
             lowerCAmelCase     :     Dict								=							Adafactor
             lowerCAmelCase     :     Optional[int]								=							{"scale_parameter": False, "relative_step": False}
           else:
             lowerCAmelCase     :     int								=							AdamW
             lowerCAmelCase     :     int								=							{
                 "betas": (self.args.adam_betaa, self.args.adam_betaa),
                 "eps": self.args.adam_epsilon,
             }
           lowerCAmelCase     :     Any								=							self.args.learning_rate
           if self.sharded_ddp:
             lowerCAmelCase     :     int								=							OSS(
                 params=snake_case__		,     optim=snake_case__		,     **snake_case__		,     )
           else:
             lowerCAmelCase     :     Any								=							optimizer_cls(snake_case__		,     **snake_case__						)

         if self.lr_scheduler is None:
           lowerCAmelCase     :     Tuple								=							self._get_lr_scheduler(snake_case__						)
         else:  # ignoring --lr_scheduler
           logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."						)

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							arg_to_scheduler[self.args.lr_scheduler]
         if self.args.lr_scheduler == "constant":
           lowerCAmelCase     :     Tuple								=							schedule_func(self.optimizer						)
         elif self.args.lr_scheduler == "constant_w_warmup":
           lowerCAmelCase     :     Any								=							schedule_func(self.optimizer		,     num_warmup_steps=self.args.warmup_steps						)
         else:
           lowerCAmelCase     :     str								=							schedule_func(
               self.optimizer		,     num_warmup_steps=self.args.warmup_steps		,     num_training_steps=snake_case__						)
         return scheduler

       def        lowercase__							(						self						):






         """simple docstring"""





         if isinstance(self.train_dataset		,     torch.utils.data.IterableDataset						):
           return None
         elif is_torch_tpu_available():
           return get_tpu_sampler(self.train_dataset						)
         else:
           if self.args.sortish_sampler:
             self.train_dataset.make_sortish_sampler(
                 self.args.per_device_train_batch_size		,     distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)		,     )

           return (
               RandomSampler(self.train_dataset						)
               if self.args.local_rank == -1
               else DistributedSampler(self.train_dataset						)
           )

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if self.args.label_smoothing == 0:
           if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
             # force training to ignore pad token
             lowerCAmelCase     :     Dict								=							model(**snake_case__		,     use_cache=snake_case__						)[0]
             lowerCAmelCase     :     List[Any]								=							self.loss_fn(logits.view(-1		,     logits.shape[-1]						)		,     labels.view(-1						)						)
           else:
             # compute usual loss via models
             lowerCAmelCase						,  lowerCAmelCase     :     str								=							model(**snake_case__		,     labels=snake_case__		,     use_cache=snake_case__						)[:2]
         else:
           # compute label smoothed loss
           lowerCAmelCase     :     int								=							model(**snake_case__		,     use_cache=snake_case__						)[0]
           lowerCAmelCase     :     List[Any]								=							torch.nn.functional.log_softmax(snake_case__		,     dim=-1						)
           lowerCAmelCase						,  lowerCAmelCase     :     str								=							self.loss_fn(snake_case__		,     snake_case__		,     self.args.label_smoothing		,     ignore_index=self.config.pad_token_id						)
         return loss, logits

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							inputs.pop("labels"						)
         lowerCAmelCase						,  lowerCAmelCase     :     str								=							self._compute_loss(snake_case__		,     snake_case__		,     snake_case__						)
         return loss

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__ = None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							self._prepare_inputs(snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							{
             "max_length": self.data_args.val_max_target_length
             if self.data_args is not None
             else self.config.max_length,
             "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
         }

         if self.args.predict_with_generate and not self.args.prediction_loss_only:
           lowerCAmelCase     :     Dict								=							self.model.generate(
               inputs["input_ids"]		,     attention_mask=inputs["attention_mask"]		,     **snake_case__		,     )
           # in case the batch is shorter than max length, the output should be padded
           if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
             lowerCAmelCase     :     Dict								=							self._pad_tensors_to_max_len(snake_case__		,     gen_kwargs["max_length"]						)

         lowerCAmelCase     :     Optional[Any]								=							inputs.pop("labels"						)
         with torch.no_grad():
           # compute loss on predict data
           lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							self._compute_loss(snake_case__		,     snake_case__		,     snake_case__						)

         lowerCAmelCase     :     List[str]								=							loss.mean().detach()
         if self.args.prediction_loss_only:
           return (loss, None, None)

         lowerCAmelCase     :     int								=							generated_tokens if self.args.predict_with_generate else logits

         if labels.shape[-1] < gen_kwargs["max_length"]:
           lowerCAmelCase     :     Optional[int]								=							self._pad_tensors_to_max_len(snake_case__		,     gen_kwargs["max_length"]						)

         return (loss, logits, labels)







       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

         if pad_token_id is None:
           raise ValueError(
               "Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
               f""" padded to `max_length`={max_length}"""						)

         lowerCAmelCase     :     Optional[Any]								=							pad_token_id * torch.ones(
             (tensor.shape[0], max_length)		,     dtype=tensor.dtype		,     device=tensor.device						)
         lowerCAmelCase     :     int								=							tensor
         return padded_tensor





