

import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


A_		:			Any										=   getLogger(__name__)


A_		:			List[Any]										=   'cuda' if torch.cuda.is_available() else 'cpu'


def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE = 8       ,   SCREAMING_SNAKE_CASE = DEFAULT_DEVICE       ,   SCREAMING_SNAKE_CASE=False       ,   SCREAMING_SNAKE_CASE="summarization"       ,   SCREAMING_SNAKE_CASE=None       ,   **SCREAMING_SNAKE_CASE       ,   )      ->     Dict:






    '''simple docstring'''
    __UpperCAmelCase									=			Path(SCREAMING_SNAKE_CASE      ).open('''w'''       ,   encoding='''utf-8'''      )
    __UpperCAmelCase									=			str(SCREAMING_SNAKE_CASE      )
    __UpperCAmelCase									=			AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE      ).to(SCREAMING_SNAKE_CASE      )
    if fpaa:
        __UpperCAmelCase									=			model.half()

    __UpperCAmelCase									=			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE      )
    logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''      )  # if this is wrong, check config.model_type.

    __UpperCAmelCase									=			time.time()
    # update config with task specific params
    use_task_specific_params(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
    if prefix is None:
        __UpperCAmelCase									=			prefix or getattr(model.config       ,   '''prefix'''       ,   ''''''      ) or ''''''
    for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      )      ):
        __UpperCAmelCase									=			[prefix + text for text in examples_chunk]
        __UpperCAmelCase									=			tokenizer(SCREAMING_SNAKE_CASE       ,   return_tensors='''pt'''       ,   truncation=SCREAMING_SNAKE_CASE       ,   padding='''longest'''      ).to(SCREAMING_SNAKE_CASE      )
        __UpperCAmelCase									=			model.generate(
            input_ids=batch.input_ids       ,   attention_mask=batch.attention_mask       ,   **SCREAMING_SNAKE_CASE       ,   )
        __UpperCAmelCase									=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE       ,   skip_special_tokens=SCREAMING_SNAKE_CASE       ,   clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE      )
        for hypothesis in dec:
            fout.write(hypothesis + '''\n'''      )
            fout.flush()
    fout.close()
    __UpperCAmelCase									=			int(time.time() - start_time      )  # seconds
    __UpperCAmelCase									=			len(SCREAMING_SNAKE_CASE      )
    return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs       ,   4      )}


def       __a       (   )      ->     List[str]:






    '''simple docstring'''
    return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''      )


def       __a       (   SCREAMING_SNAKE_CASE=True      )      ->     List[str]:






    '''simple docstring'''
    __UpperCAmelCase									=			argparse.ArgumentParser()
    parser.add_argument('''model_name'''       ,   type=SCREAMING_SNAKE_CASE       ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''      )
    parser.add_argument('''input_path'''       ,   type=SCREAMING_SNAKE_CASE       ,   help='''like cnn_dm/test.source'''      )
    parser.add_argument('''save_path'''       ,   type=SCREAMING_SNAKE_CASE       ,   help='''where to save summaries'''      )
    parser.add_argument('''--reference_path'''       ,   type=SCREAMING_SNAKE_CASE       ,   required=SCREAMING_SNAKE_CASE       ,   help='''like cnn_dm/test.target'''      )
    parser.add_argument('''--score_path'''       ,   type=SCREAMING_SNAKE_CASE       ,   required=SCREAMING_SNAKE_CASE       ,   default='''metrics.json'''       ,   help='''where to save metrics'''      )
    parser.add_argument('''--device'''       ,   type=SCREAMING_SNAKE_CASE       ,   required=SCREAMING_SNAKE_CASE       ,   default=SCREAMING_SNAKE_CASE       ,   help='''cuda, cuda:1, cpu etc.'''      )
    parser.add_argument(
        '''--prefix'''       ,   type=SCREAMING_SNAKE_CASE       ,   required=SCREAMING_SNAKE_CASE       ,   default=SCREAMING_SNAKE_CASE       ,   help='''will be added to the begininng of src examples'''      )
    parser.add_argument('''--task'''       ,   type=SCREAMING_SNAKE_CASE       ,   default='''summarization'''       ,   help='''used for task_specific_params + metrics'''      )
    parser.add_argument('''--bs'''       ,   type=SCREAMING_SNAKE_CASE       ,   default=8       ,   required=SCREAMING_SNAKE_CASE       ,   help='''batch size'''      )
    parser.add_argument(
        '''--n_obs'''       ,   type=SCREAMING_SNAKE_CASE       ,   default=-1       ,   required=SCREAMING_SNAKE_CASE       ,   help='''How many observations. Defaults to all.'''      )
    parser.add_argument('''--fp16'''       ,   action='''store_true'''      )
    parser.add_argument('''--dump-args'''       ,   action='''store_true'''       ,   help='''print the custom hparams with the results'''      )
    parser.add_argument(
        '''--info'''       ,   nargs='''?'''       ,   type=SCREAMING_SNAKE_CASE       ,   const=datetime_now()       ,   help=(
            '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
            ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
        )       ,   )
    # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
    __UpperCAmelCase					,     __UpperCAmelCase									=			parser.parse_known_args()
    __UpperCAmelCase									=			parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE      )
    if parsed_args and verbose:
        print(f'''parsed the following generate kwargs: {parsed_args}'''      )
    with open(args.input_path      ) as f:
        __UpperCAmelCase									=			[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in f.readlines()]
    if args.n_obs > 0:
        __UpperCAmelCase									=			examples[: args.n_obs]
    Path(args.save_path      ).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE      )
    if args.reference_path is None and Path(args.score_path      ).exists():
        warnings.warn(f'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''      )
    __UpperCAmelCase									=			generate_summaries_or_translations(
        SCREAMING_SNAKE_CASE       ,   args.save_path       ,   args.model_name       ,   batch_size=args.bs       ,   device=args.device       ,   fpaa=args.fpaa       ,   task=args.task       ,   prefix=args.prefix       ,   **SCREAMING_SNAKE_CASE       ,   )

    if args.reference_path is None:
        return {}

    # Compute scores
    __UpperCAmelCase									=			calculate_bleu if '''translation''' in args.task else calculate_rouge
    __UpperCAmelCase									=			[x.rstrip() for x in open(args.save_path      ).readlines()]
    __UpperCAmelCase									=			[x.rstrip() for x in open(args.reference_path      ).readlines()][: len(SCREAMING_SNAKE_CASE      )]
    __UpperCAmelCase									=			score_fn(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
    scores.update(SCREAMING_SNAKE_CASE      )

    if args.dump_args:
        scores.update(SCREAMING_SNAKE_CASE      )
    if args.info:
        __UpperCAmelCase									=			args.info

    if verbose:
        print(SCREAMING_SNAKE_CASE      )

    if args.score_path is not None:
        json.dump(SCREAMING_SNAKE_CASE       ,   open(args.score_path       ,   '''w'''      )      )

    return scores


if __name__ == "__main__":
       # Usage for MT:
       # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
       run_generate(verbose=True)







import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import DonutSwinModel
       from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST
class 					A_   :


    '''simple docstring'''






    def __init__(self     ,      lowercase__     ,      lowercase__=13     ,      lowercase__=32     ,      lowercase__=2     ,      lowercase__=3     ,      lowercase__=16     ,      lowercase__=[1, 2, 1]     ,      lowercase__=[2, 2, 4]     ,      lowercase__=2     ,      lowercase__=2.0     ,      lowercase__=True     ,      lowercase__=0.0     ,      lowercase__=0.0     ,      lowercase__=0.1     ,      lowercase__="gelu"     ,      lowercase__=False     ,      lowercase__=True     ,      lowercase__=0.02     ,      lowercase__=1E-5     ,      lowercase__=True     ,      lowercase__=None     ,      lowercase__=True     ,      lowercase__=10     ,      lowercase__=8     ,      )     ->      str:
        __UpperCAmelCase									=			parent
        __UpperCAmelCase									=			batch_size
        __UpperCAmelCase									=			image_size
        __UpperCAmelCase									=			patch_size
        __UpperCAmelCase									=			num_channels
        __UpperCAmelCase									=			embed_dim
        __UpperCAmelCase									=			depths
        __UpperCAmelCase									=			num_heads
        __UpperCAmelCase									=			window_size
        __UpperCAmelCase									=			mlp_ratio
        __UpperCAmelCase									=			qkv_bias
        __UpperCAmelCase									=			hidden_dropout_prob
        __UpperCAmelCase									=			attention_probs_dropout_prob
        __UpperCAmelCase									=			drop_path_rate
        __UpperCAmelCase									=			hidden_act
        __UpperCAmelCase									=			use_absolute_embeddings
        __UpperCAmelCase									=			patch_norm
        __UpperCAmelCase									=			layer_norm_eps
        __UpperCAmelCase									=			initializer_range
        __UpperCAmelCase									=			is_training
        __UpperCAmelCase									=			scope
        __UpperCAmelCase									=			use_labels
        __UpperCAmelCase									=			type_sequence_label_size
        __UpperCAmelCase									=			encoder_stride


    def      lowerCAmelCase_						(self  )     ->      Any:
        __UpperCAmelCase									=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        __UpperCAmelCase									=			None
        if self.use_labels:
            __UpperCAmelCase									=			ids_tensor([self.batch_size]     ,      self.type_sequence_label_size  )

        __UpperCAmelCase									=			self.get_config()

        return config, pixel_values, labels


    def      lowerCAmelCase_						(self  )     ->      Tuple:
        return DonutSwinConfig(
            image_size=self.image_size     ,      patch_size=self.patch_size     ,      num_channels=self.num_channels     ,      embed_dim=self.embed_dim     ,      depths=self.depths     ,      num_heads=self.num_heads     ,      window_size=self.window_size     ,      mlp_ratio=self.mlp_ratio     ,      qkv_bias=self.qkv_bias     ,      hidden_dropout_prob=self.hidden_dropout_prob     ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,      drop_path_rate=self.drop_path_rate     ,      hidden_act=self.hidden_act     ,      use_absolute_embeddings=self.use_absolute_embeddings     ,      path_norm=self.patch_norm     ,      layer_norm_eps=self.layer_norm_eps     ,      initializer_range=self.initializer_range     ,      encoder_stride=self.encoder_stride     ,      )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[str]:
        __UpperCAmelCase									=			DonutSwinModel(config=lowercase__  )
        model.to(lowercase__  )
        model.eval()
        __UpperCAmelCase									=			model(lowercase__  )

        __UpperCAmelCase									=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
        __UpperCAmelCase									=			int(config.embed_dim * 2 ** (len(config.depths  ) - 1)  )

        self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, expected_seq_len, expected_dim)  )






    def      lowerCAmelCase_						(self  )     ->      str:
        __UpperCAmelCase									=			self.prepare_config_and_inputs()
        (
            (
            __UpperCAmelCase
        )					,     (
            __UpperCAmelCase
        )					,     (
            __UpperCAmelCase
        )					,     
        )									=			config_and_inputs
        __UpperCAmelCase									=			{'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
class 					A_   (      _a ,     _a ,     unittest.TestCase						):


    '''simple docstring'''






    a__     							=       (DonutSwinModel,) if is_torch_available() else ()
    a__     							=       {"feature-extraction": DonutSwinModel} if is_torch_available() else {}
    a__     							=       True

    a__     							=       False
    a__     							=       False
    a__     							=       False


    def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
        __UpperCAmelCase									=			DonutSwinModelTester(self  )
        __UpperCAmelCase									=			ConfigTester(self     ,      config_class=lowercase__     ,      embed_dim=37  )


    def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()


    def      lowerCAmelCase_						(self  )     ->      Optional[int]:
        return


    def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
        __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      List[Any]:
        # DonutSwin does not use inputs_embeds
        pass


    def      lowerCAmelCase_						(self  )     ->      Union[str, Any]:
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCAmelCase									=			model_class(lowercase__  )
            self.assertIsInstance(model.get_input_embeddings()     ,      (nn.Module)  )
            __UpperCAmelCase									=			model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(lowercase__     ,      nn.Linear  )  )


    def      lowerCAmelCase_						(self  )     ->      Tuple:
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCAmelCase									=			model_class(lowercase__  )
            __UpperCAmelCase									=			inspect.signature(model.forward  )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __UpperCAmelCase									=			[*signature.parameters.keys()]

            __UpperCAmelCase									=			['''pixel_values''']
            self.assertListEqual(arg_names[:1]     ,      lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCAmelCase									=			True

        for model_class in self.all_model_classes:
            __UpperCAmelCase									=			True
            __UpperCAmelCase									=			False
            __UpperCAmelCase									=			True
            __UpperCAmelCase									=			model_class(lowercase__  )
            model.to(lowercase__  )
            model.eval()
            with torch.no_grad():
                __UpperCAmelCase									=			model(**self._prepare_for_class(lowercase__     ,      lowercase__  )  )
            __UpperCAmelCase									=			outputs.attentions
            __UpperCAmelCase									=			len(self.model_tester.depths  )
            self.assertEqual(len(lowercase__  )     ,      lowercase__  )

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            __UpperCAmelCase									=			True
            __UpperCAmelCase									=			config.window_size**2
            __UpperCAmelCase									=			model_class(lowercase__  )
            model.to(lowercase__  )
            model.eval()
            with torch.no_grad():
                __UpperCAmelCase									=			model(**self._prepare_for_class(lowercase__     ,      lowercase__  )  )
            __UpperCAmelCase									=			outputs.attentions
            self.assertEqual(len(lowercase__  )     ,      lowercase__  )

            self.assertListEqual(
                list(attentions[0].shape[-3:]  )     ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,      )
            __UpperCAmelCase									=			len(lowercase__  )

            # Check attention is always last and order is fine
            __UpperCAmelCase									=			True
            __UpperCAmelCase									=			True
            __UpperCAmelCase									=			model_class(lowercase__  )
            model.to(lowercase__  )
            model.eval()
            with torch.no_grad():
                __UpperCAmelCase									=			model(**self._prepare_for_class(lowercase__     ,      lowercase__  )  )

            if hasattr(self.model_tester     ,      '''num_hidden_states_types'''  ):
                __UpperCAmelCase									=			self.model_tester.num_hidden_states_types
            else:
                # also another +1 for reshaped_hidden_states
                __UpperCAmelCase									=			2
            self.assertEqual(out_len + added_hidden_states     ,      len(lowercase__  )  )

            __UpperCAmelCase									=			outputs.attentions

            self.assertEqual(len(lowercase__  )     ,      lowercase__  )

            self.assertListEqual(
                list(self_attentions[0].shape[-3:]  )     ,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,      )


    def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )     ->      List[str]:
        __UpperCAmelCase									=			model_class(lowercase__  )
        model.to(lowercase__  )
        model.eval()

        with torch.no_grad():
            __UpperCAmelCase									=			model(**self._prepare_for_class(lowercase__     ,      lowercase__  )  )

        __UpperCAmelCase									=			outputs.hidden_states

        __UpperCAmelCase									=			getattr(
            self.model_tester     ,      '''expected_num_hidden_layers'''     ,      len(self.model_tester.depths  ) + 1  )
        self.assertEqual(len(lowercase__  )     ,      lowercase__  )

        # DonutSwin has a different seq_length
        __UpperCAmelCase									=			(
            config.patch_size
            if isinstance(config.patch_size     ,      collections.abc.Iterable  )
            else (config.patch_size, config.patch_size)
        )

        __UpperCAmelCase									=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]  )     ,      [num_patches, self.model_tester.embed_dim]     ,      )

        __UpperCAmelCase									=			outputs.reshaped_hidden_states
        self.assertEqual(len(lowercase__  )     ,      lowercase__  )

        __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			reshaped_hidden_states[0].shape
        __UpperCAmelCase									=			(
            reshaped_hidden_states[0].view(lowercase__     ,      lowercase__     ,      height * width  ).permute(0     ,      2     ,      1  )
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]  )     ,      [num_patches, self.model_tester.embed_dim]     ,      )


    def      lowerCAmelCase_						(self  )     ->      Any:
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCAmelCase									=			(
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size     ,      collections.abc.Iterable  )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes:
            __UpperCAmelCase									=			True
            self.check_hidden_states_output(lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __UpperCAmelCase									=			True

            self.check_hidden_states_output(lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__  )


    def      lowerCAmelCase_						(self  )     ->      int:
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCAmelCase									=			3

        __UpperCAmelCase									=			(
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size     ,      collections.abc.Iterable  )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        __UpperCAmelCase									=			(
            config.patch_size
            if isinstance(config.patch_size     ,      collections.abc.Iterable  )
            else (config.patch_size, config.patch_size)
        )

        __UpperCAmelCase									=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        __UpperCAmelCase									=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
            __UpperCAmelCase									=			True
            self.check_hidden_states_output(lowercase__     ,      lowercase__     ,      lowercase__     ,      (padded_height, padded_width)  )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __UpperCAmelCase									=			True
            self.check_hidden_states_output(lowercase__     ,      lowercase__     ,      lowercase__     ,      (padded_height, padded_width)  )


    @slow
    def      lowerCAmelCase_						(self  )     ->      Tuple:
        for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCAmelCase									=			DonutSwinModel.from_pretrained(lowercase__  )
            self.assertIsNotNone(lowercase__  )






    def      lowerCAmelCase_						(self  )     ->      Tuple:
        __UpperCAmelCase					,     __UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCAmelCase									=			_config_zero_init(lowercase__  )
        for model_class in self.all_model_classes:
            __UpperCAmelCase									=			model_class(config=lowercase__  )
            for name, param in model.named_parameters():
                if "embeddings" not in name and param.requires_grad:
                    self.assertIn(
                        ((param.data.mean() * 1E9).round() / 1E9).item()     ,      [0.0, 1.0]     ,      msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''     ,      )




