





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				:   Tuple						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   Any						       =						{
    '''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        __magic_name__	(				_UpperCamelCase				):





     """simple docstring"""



     def __init__(				self							:List[Any]      ,   snake_case							:List[str]=None      ,   snake_case							:Union[str, Any]=None      ,   *snake_case							:Dict      ,   **snake_case							:Optional[int]	):





        '''simple docstring'''




        super().__init__(*a_      ,   **a_	)

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

        A_       :					Optional[Any]        =      data_args
        A_       :					str        =      self.config.tgt_vocab_size if isinstance(self.config      ,   a_	) 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:
           A_       :					Dict        =      torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id	)
        else:
           # dynamically import label_smoothed_nll_loss
           from utils import label_smoothed_nll_loss

           A_       :					Tuple        =      label_smoothed_nll_loss



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:int	):





        '''simple docstring'''




        if self.optimizer is None:
           A_       :					Dict        =      ["bias", "LayerNorm.weight"]
           A_       :					Tuple        =      [
               {
                   "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,
               },
           ]
           A_       :					Optional[int]        =      Adafactor if self.args.adafactor else AdamW
           if self.args.adafactor:
              A_       :					Any        =      Adafactor
              A_       :					List[Any]        =      {"scale_parameter": False, "relative_step": False}
           else:
              A_       :					Any        =      AdamW
              A_       :					Dict        =      {
                  "betas": (self.args.adam_betaa, self.args.adam_betaa),
                  "eps": self.args.adam_epsilon,
              }
           A_       :					List[str]        =      self.args.learning_rate
           if self.sharded_ddp:
              A_       :					Optional[Any]        =      OSS(
                  params=a_      ,   optim=a_      ,   **a_      ,   )
           else:
              A_       :					Any        =      optimizer_cls(a_      ,   **a_	)

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



     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:str	):





        '''simple docstring'''




        A_       :					Optional[int]        =      arg_to_scheduler[self.args.lr_scheduler]
        if self.args.lr_scheduler == "constant":
           A_       :					Dict        =      schedule_func(self.optimizer	)
        elif self.args.lr_scheduler == "constant_w_warmup":
           A_       :					Union[str, Any]        =      schedule_func(self.optimizer      ,   num_warmup_steps=self.args.warmup_steps	)
        else:
           A_       :					List[Any]        =      schedule_func(
               self.optimizer      ,   num_warmup_steps=self.args.warmup_steps      ,   num_training_steps=a_	)
        return scheduler



     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''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     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:Tuple      ,   snake_case							:Optional[int]      ,   snake_case							:Optional[Any]	):





        '''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
              A_       :					Optional[int]        =      model(**a_      ,   use_cache=a_	)[0]
              A_       :					List[str]        =      self.loss_fn(logits.view(-1      ,   logits.shape[-1]	)      ,   labels.view(-1	)	)
           else:
              # compute usual loss via models
              A_       :					Tuple        =      model(**a_      ,   labels=a_      ,   use_cache=a_	)[:2]
        else:
           # compute label smoothed loss
           A_       :					Dict        =      model(**a_      ,   use_cache=a_	)[0]
           A_       :					int        =      torch.nn.functional.log_softmax(a_      ,   dim=-1	)
           A_       :					Optional[Any]        =      self.loss_fn(a_      ,   a_      ,   self.args.label_smoothing      ,   ignore_index=self.config.pad_token_id	)
        return loss, logits



     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:Dict      ,   snake_case							:str	):





        '''simple docstring'''




        A_       :					List[str]        =      inputs.pop("labels"	)
        A_       :					Tuple        =      self._compute_loss(a_      ,   a_      ,   a_	)
        return loss



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]      ,   snake_case							:nn.Module      ,   snake_case							:Dict[str, Union[torch.Tensor, Any]]      ,   snake_case							:bool      ,   snake_case							:Optional[List[str]] = None      ,   ):





        '''simple docstring'''




        A_       :					str        =      self._prepare_inputs(a_	)

        A_       :					List[str]        =      {
            "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:
           A_       :					Optional[int]        =      self.model.generate(
               inputs["input_ids"]      ,   attention_mask=inputs["attention_mask"]      ,   **a_      ,   )
           # in case the batch is shorter than max length, the output should be padded
           if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
              A_       :					str        =      self._pad_tensors_to_max_len(a_      ,   gen_kwargs["max_length"]	)

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

        A_       :					Tuple        =      loss.mean().detach()
        if self.args.prediction_loss_only:
           return (loss, None, None)

        A_       :					Any        =      generated_tokens if self.args.predict_with_generate else logits

        if labels.shape[-1] < gen_kwargs["max_length"]:
           A_       :					Tuple        =      self._pad_tensors_to_max_len(a_      ,   gen_kwargs["max_length"]	)

        return (loss, logits, labels)





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:Optional[int]      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					Dict        =      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}"	)

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







"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor



@require_torch
@require_vision
class 					__UpperCAmelCase							(						unittest.TestCase  ):
				__lowerCamelCase				:	int				     =				ViTImageProcessor if is_vision_available() else None
				@property
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			(3, 32, 1_28)
							a__  :	Any		 =			tempfile.mkdtemp()

							# fmt: off
							a__  :	str		 =			["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
							# fmt: on
							a__  :	List[Any]		 =			dict(zip(a_	, range(len(a_    )    )    )    )

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

							a__  :	Any		 =			{
							    "do_normalize": False,
							    "do_resize": True,
							    "image_processor_type": "ViTImageProcessor",
							    "resample": 3,
							    "size": {"height": 32, "width": 1_28},
							}
							a__  :	Tuple		 =			os.path.join(self.tmpdirname	, a_    )
							with open(self.image_processor_file	, "w"	, encoding="utf-8"    ) as fp:
										json.dump(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[str]	, **a_    :			List[str]    )	->	Union[str, Any]:



							'''simple docstring'''



							return MgpstrTokenizer.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			List[Any]	, **a_    :			Union[str, Any]    )	->	Dict:



							'''simple docstring'''



							return ViTImageProcessor.from_pretrained(self.tmpdirname	, **a_    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	int:



							'''simple docstring'''



							shutil.rmtree(self.tmpdirname    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Any:



							'''simple docstring'''



							a__  :	Any		 =			np.random.randint(2_55	, size=(3, 30, 4_00)	, dtype=np.uinta    )

							a__  :	List[str]		 =			Image.fromarray(np.moveaxis(a_	, 0	, -1    )    )

							return image_input
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.get_tokenizer()
							a__  :	Any		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )
							a__  :	Optional[Any]		 =			MgpstrProcessor.from_pretrained(self.tmpdirname	, use_fast=a_    )

							self.assertEqual(processor.char_tokenizer.get_vocab()	, tokenizer.get_vocab()    )
							self.assertIsInstance(processor.char_tokenizer	, a_    )

							self.assertEqual(processor.image_processor.to_json_string()	, image_processor.to_json_string()    )
							self.assertIsInstance(processor.image_processor	, a_    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	str:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_tokenizer()
							a__  :	Tuple		 =			self.get_image_processor()

							a__  :	Optional[int]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )
							processor.save_pretrained(self.tmpdirname    )

							a__  :	Union[str, Any]		 =			self.get_tokenizer(bos_token="(BOS)"	, eos_token="(EOS)"    )
							a__  :	Any		 =			self.get_image_processor(do_normalize=a_	, padding_value=1.0    )

							a__  :	Dict		 =			MgpstrProcessor.from_pretrained(
							    self.tmpdirname	, bos_token="(BOS)"	, eos_token="(EOS)"	, do_normalize=a_	, padding_value=1.0    )

							self.assertEqual(processor.char_tokenizer.get_vocab()	, tokenizer_add_kwargs.get_vocab()    )
							self.assertIsInstance(processor.char_tokenizer	, a_    )

							self.assertEqual(processor.image_processor.to_json_string()	, image_processor_add_kwargs.to_json_string()    )
							self.assertIsInstance(processor.image_processor	, a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Dict:



							'''simple docstring'''



							a__  :	str		 =			self.get_image_processor()
							a__  :	Union[str, Any]		 =			self.get_tokenizer()

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			self.prepare_image_inputs()

							a__  :	List[Any]		 =			image_processor(a_	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			processor(images=a_	, return_tensors="np"    )

							for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum()	, input_processor[key].sum()	, delta=1E-2    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	int:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.get_image_processor()
							a__  :	List[Any]		 =			self.get_tokenizer()

							a__  :	int		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			"test"

							a__  :	Any		 =			processor(text=a_    )

							a__  :	Tuple		 =			tokenizer(a_    )
							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]	, encoded_processor[key]    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.get_image_processor()
							a__  :	List[str]		 =			self.get_tokenizer()

							a__  :	List[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			"test"
							a__  :	str		 =			self.prepare_image_inputs()

							a__  :	Any		 =			processor(text=a_	, images=a_    )

							self.assertListEqual(list(inputs.keys()    )	, ["pixel_values", "labels"]    )

							# test if it raises when no input is passed
							with pytest.raises(a_    ):
										processor()
				def       UpperCAmelCase      (		self    :			List[str]    )	->	int:



							'''simple docstring'''



							a__  :	Any		 =			self.get_image_processor()
							a__  :	Tuple		 =			self.get_tokenizer()

							a__  :	Dict		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	Optional[Any]		 =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a__  :	Any		 =			processor.char_decode(a_    )
							a__  :	str		 =			tokenizer.batch_decode(a_    )
							a__  :	Union[str, Any]		 =			[seq.replace(" "	, ""    ) for seq in decoded_tok]

							self.assertListEqual(a_	, a_    )
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[str]		 =			self.get_image_processor()
							a__  :	Any		 =			self.get_tokenizer()

							a__  :	Any		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	str		 =			None
							a__  :	Optional[Any]		 =			self.prepare_image_inputs()

							a__  :	Optional[int]		 =			processor(text=a_	, images=a_    )

							self.assertListEqual(list(inputs.keys()    )	, processor.model_input_names    )






				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	List[Any]		 =			self.get_image_processor()
							a__  :	List[str]		 =			self.get_tokenizer()

							a__  :	Optional[Any]		 =			MgpstrProcessor(tokenizer=a_	, image_processor=a_    )

							a__  :	List[str]		 =			torch.randn(1	, 27	, 38    )
							a__  :	Tuple		 =			torch.randn(1	, 27	, 5_02_57    )
							a__  :	List[Any]		 =			torch.randn(1	, 27	, 3_05_22    )

							a__  :	Optional[Any]		 =			processor.batch_decode([char_input, bpe_input, wp_input]    )

							self.assertListEqual(list(results.keys()    )	, ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]    )