


import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

a					:				Tuple       =      logging.getLogger()
a					:				Tuple       =      torch.cuda.is_available()
a					:				Tuple       =      {
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1e-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase							)		->Union[str, Any]:


 '''simple docstring'''
 snake_case :			int							=   '''\n'''.join(__lowerCamelCase							)
 Path(__lowerCamelCase							).open('''w'''							).writelines(__lowerCamelCase							)


a					:				Dict       =      [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a					:				Optional[int]       =      ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a					:				str       =      """patrickvonplaten/t5-tiny-random"""
a					:				Optional[int]       =      """sshleifer/t5-tinier-random"""
a					:				str       =      """sshleifer/bart-tiny-random"""
a					:				Optional[Any]       =      """sshleifer/tiny-mbart"""
a					:				Optional[Any]       =      """sshleifer/tiny-marian-en-de"""
a					:				Dict       =      """stas/tiny-wmt19-en-de"""


a					:				Tuple       =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






def  _UpperCAmelCase						( __lowerCamelCase							)		->int:


 '''simple docstring'''
 for split in ["train", "val", "test"]:
  _dump_articles(os.path.join(__lowerCamelCase			,  f'''{split}.source'''							)			,  __lowerCamelCase							)
  _dump_articles(os.path.join(__lowerCamelCase			,  f'''{split}.target'''							)			,  __lowerCamelCase							)
 return tmp_dir

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):

       @classmethod
       def 					UpperCAmelCase					(      cls       ):






        '''simple docstring'''






        logging.disable(logging.CRITICAL       )  # remove noisy download output from tracebacks
        return cls

       @slow
       @require_torch_gpu
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Union[str, Any]							=   list_models()
        snake_case :			Union[str, Any]							=   '''sshleifer'''
        snake_case :			List[Any]							=   [x.modelId for x in model_list if x.modelId.startswith(a__       )]
        snake_case :			Optional[int]							=   ['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
        snake_case :			Optional[Any]							=   []
        for m in model_ids:
         if m in allowed_to_be_broken:
          continue
         try:
          AutoConfig.from_pretrained(a__       )
         except Exception:
          failures.append(a__       )
        assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			str							=   {'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
        self._test_distiller_cli(a__       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   {
            '''student_encoder_layers''': 2,
            '''student_decoder_layers''': 1,
            '''max_epochs''': 4,
            '''val_check_interval''': 0.25,
            '''alpha_hid''': 2.0,
            '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
        }
        snake_case :			Union[str, Any]							=   self._test_distiller_cli(a__						,    check_contents=a__       )

        snake_case :			Any							=   list(Path(model.output_dir       ).glob('''*.ckpt'''       )       )
        self.assertEqual(1						,    len(a__       )       )
        snake_case :			List[str]							=   list(Path(model.output_dir       ).glob('''**/*.bin'''       )       )
        self.assertEqual(len(a__       )						,    2       )
        snake_case :			List[Any]							=   lmap(str.strip						,    Path(model.hparams.data_dir       ).joinpath('''test.source'''       ).open().readlines()       )
        snake_case :			List[str]							=   tempfile.mktemp()  # XXX: not being cleaned up
        generate_summaries_or_translations(a__						,    a__						,    str(model.output_dir / '''best_tfmr'''       )       )
        self.assertTrue(Path(a__       ).exists()       )

        snake_case :			int							=   self.get_auto_remove_tmp_dir()
        convert_pl_to_hf(ckpts[0]						,    transformer_ckpts[0].parent						,    a__       )
        assert os.path.exists(os.path.join(a__						,    '''pytorch_model.bin'''       )       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   AutoModelForSeqaSeqLM.from_pretrained(a__       )
        snake_case	,       snake_case :			Tuple							=   model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
        snake_case :			Dict							=   torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]						,    dtype=torch.long						,    device=model.device       )
        snake_case :			Dict							=   target_ids[:, :-1].contiguous()  # Why this line?
        snake_case :			Any							=   target_ids[:, 1:].clone()  # why clone?
        snake_case :			int							=   model(
            a__						,    attention_mask=a__						,    decoder_input_ids=a__						,    labels=a__						,    use_cache=a__       ).loss

        snake_case :			Dict							=   model(a__						,    attention_mask=a__						,    decoder_input_ids=a__						,    use_cache=a__       ).logits

        snake_case :			List[Any]							=   nn.functional.log_softmax(a__						,    dim=-1       )
        snake_case	,       snake_case :			Optional[Any]							=   label_smoothed_nll_loss(
            a__						,    a__						,    0.1						,    ignore_index=model.config.pad_token_id       )
        with self.assertRaises(a__       ):
         # TODO: understand why this breaks
         self.assertEqual(a__						,    a__       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			str							=   {
            '''student_encoder_layers''': 2,
            '''student_decoder_layers''': 1,
            '''num_train_epochs''': 4,
            '''val_check_interval''': 0.25,
            '''alpha_hid''': 2.0,
            '''task''': '''translation''',
            '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
            '''tokenizer_name''': MBART_TINY,
            '''teacher''': MBART_TINY,
            '''src_lang''': '''en_XX''',
            '''tgt_lang''': '''ro_RO''',
        }
        snake_case :			Tuple							=   self._test_distiller_cli(a__						,    check_contents=a__       )
        assert model.model.config.model_type == "mbart"

        snake_case :			Union[str, Any]							=   list(Path(model.output_dir       ).glob('''*.ckpt'''       )       )
        self.assertEqual(1						,    len(a__       )       )
        snake_case :			List[str]							=   list(Path(model.output_dir       ).glob('''**/*.bin'''       )       )
        snake_case :			List[Any]							=   list(Path(model.output_dir       ).glob('''best_tfmr/*'''       )       )
        assert len(a__       ) > 2
        self.assertEqual(len(a__       )						,    2       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   {
            '''student_encoder_layers''': 1,
            '''student_decoder_layers''': 1,
            '''alpha_hid''': 2.0,
            '''teacher''': T5_TINY,
            '''model_name_or_path''': T5_TINY,
            '''tokenizer_name''': T5_TINY,
        }
        self._test_distiller_cli(a__       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Union[str, Any]							=   {
            '''teacher''': T5_TINY,
            '''student''': T5_TINIER,
            '''model_name_or_path''': T5_TINIER,
            '''tokenizer_name''': T5_TINIER,
        }
        self._test_distiller_cli(a__       )



       def 					UpperCAmelCase					(      self						,    a__						,    a__=True       ):






        '''simple docstring'''






        snake_case :			List[Any]							=   {
            '''label_smoothing''': 0.0,
            '''early_stopping_patience''': -1,
            '''train_batch_size''': 1,
            '''eval_batch_size''': 2,
            '''max_epochs''': 2,
            '''alpha_mlm''': 0.2,
            '''alpha_ce''': 0.8,
            '''do_predict''': True,
            '''model_name_or_path''': '''sshleifer/tinier_bart''',
            '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
            '''val_check_interval''': 0.5,
        }
        default_updates.update(a__       )
        snake_case :			List[str]							=   CHEAP_ARGS.copy()
        snake_case :			Optional[Any]							=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
        snake_case :			Dict							=   self.get_auto_remove_tmp_dir()

        args_d.update(data_dir=a__						,    output_dir=a__						,    **a__       )
        snake_case :			List[str]							=   distill_main(argparse.Namespace(**a__       )       )
        if not check_contents:
         return model
        snake_case :			str							=   os.listdir(a__       )
        snake_case :			List[Any]							=   {os.path.basename(a__       ) for p in contents}
        snake_case :			Optional[Any]							=   [p for p in contents if p.endswith('''ckpt'''       )]
        assert len(a__       ) > 0

        self.assertIn('''test_generations.txt'''						,    a__       )
        self.assertIn('''test_results.txt'''						,    a__       )

        snake_case :			Optional[Any]							=   load_json(model.metrics_save_path       )
        snake_case :			Optional[Any]							=   metrics['''val'''][-1]
        self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']						,    0.01       )
        self.assertGreaterEqual(1.0						,    last_step_stats['''val_avg_gen_time''']       )
        self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']						,    a__       )
        snake_case :			int							=   int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1       )
        self.assertEqual(len(metrics['''val''']       )						,    a__       )
        self.assertEqual(len(metrics['''test''']       )						,    1       )
        return model







class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):

       @parameterized.expand(
           [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]						,    )
       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






        snake_case :			Union[str, Any]							=   CHEAP_ARGS.copy()
        snake_case :			Optional[int]							=   '''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
        snake_case :			str							=   0.1 if task == '''translation''' else 0

        snake_case :			int							=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
        snake_case :			int							=   self.get_auto_remove_tmp_dir()
        args_d.update(
            data_dir=a__						,    model_name_or_path=a__						,    tokenizer_name=a__						,    train_batch_size=2						,    eval_batch_size=2						,    output_dir=a__						,    do_predict=a__						,    task=a__						,    src_lang='''en_XX'''						,    tgt_lang='''ro_RO'''						,    freeze_encoder=a__						,    freeze_embeds=a__						,    )
        assert "n_train" in args_d
        snake_case :			List[str]							=   argparse.Namespace(**a__       )
        snake_case :			List[Any]							=   main(a__       )

        snake_case :			List[str]							=   module.model.get_input_embeddings()
        assert not input_embeds.weight.requires_grad
        if model == T5_TINY:
         snake_case :			List[Any]							=   module.model.lm_head
         assert not lm_head.weight.requires_grad
         assert (lm_head.weight == input_embeds.weight).all().item()
        elif model == FSMT_TINY:
         snake_case :			Any							=   module.model.model
         snake_case :			Any							=   fsmt.decoder.embed_positions
         assert not embed_pos.weight.requires_grad
         assert not fsmt.decoder.embed_tokens.weight.requires_grad
         # check that embeds are not the same
         assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
        else:
         snake_case :			List[Any]							=   module.model.model
         snake_case :			Tuple							=   bart.decoder.embed_positions
         assert not embed_pos.weight.requires_grad
         assert not bart.shared.weight.requires_grad
         # check that embeds are the same
         assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
         assert bart.decoder.embed_tokens == bart.shared

        snake_case :			List[str]							=   load_json(module.output_dir / '''text_batch.json'''       )
        assert isinstance(a__						,    a__       )
        assert len(a__       ) >= 4

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			str							=   CHEAP_ARGS.copy()

        snake_case :			str							=   '''summarization'''
        snake_case :			int							=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )

        args_d.update(
            data_dir=a__						,    tokenizer_name=a__						,    train_batch_size=2						,    eval_batch_size=2						,    do_predict=a__						,    task=a__						,    src_lang='''en_XX'''						,    tgt_lang='''ro_RO'''						,    freeze_encoder=a__						,    freeze_embeds=a__						,    )

        # test models whose config includes the extra_model_args
        snake_case :			str							=   BART_TINY
        snake_case :			int							=   self.get_auto_remove_tmp_dir()
        snake_case :			Optional[Any]							=   args_d.copy()
        args_da.update(
            model_name_or_path=a__						,    output_dir=a__						,    )
        snake_case :			Optional[int]							=   ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
        for p in extra_model_params:
         snake_case :			List[Any]							=   0.5
        snake_case :			Union[str, Any]							=   argparse.Namespace(**a__       )
        snake_case :			Tuple							=   main(a__       )
        for p in extra_model_params:
         assert getattr(model.config						,    a__       ) == 0.5, f'''failed to override the model config for param {p}'''

        # test models whose config doesn't include the extra_model_args
        snake_case :			Dict							=   T5_TINY
        snake_case :			Dict							=   self.get_auto_remove_tmp_dir()
        snake_case :			int							=   args_d.copy()
        args_da.update(
            model_name_or_path=a__						,    output_dir=a__						,    )
        snake_case :			str							=   '''encoder_layerdrop'''
        snake_case :			Union[str, Any]							=   0.5
        snake_case :			List[str]							=   argparse.Namespace(**a__       )
        with pytest.raises(a__       ) as excinfo:
         snake_case :			List[str]							=   main(a__       )
        assert str(excinfo.value       ) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   CHEAP_ARGS.copy()

        snake_case :			Union[str, Any]							=   '''summarization'''
        snake_case :			Optional[Any]							=   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )

        snake_case :			Optional[int]							=   BART_TINY
        snake_case :			Optional[Any]							=   self.get_auto_remove_tmp_dir()

        args_d.update(
            data_dir=a__						,    model_name_or_path=a__						,    output_dir=a__						,    tokenizer_name=a__						,    train_batch_size=2						,    eval_batch_size=2						,    do_predict=a__						,    task=a__						,    src_lang='''en_XX'''						,    tgt_lang='''ro_RO'''						,    freeze_encoder=a__						,    freeze_embeds=a__						,    )

        # emulate finetune.py
        snake_case :			Union[str, Any]							=   argparse.ArgumentParser()
        snake_case :			List[str]							=   pl.Trainer.add_argparse_args(a__       )
        snake_case :			List[str]							=   SummarizationModule.add_model_specific_args(a__						,    os.getcwd()       )
        snake_case :			List[str]							=   {'''--help''': True}

        # --help test
        with pytest.raises(a__       ) as excinfo:
         with CaptureStdout() as cs:
          snake_case :			Optional[Any]							=   parser.parse_args(a__       )
         assert False, "--help is expected to sys.exit"
        assert excinfo.type == SystemExit
        snake_case :			Dict							=   lightning_base.arg_to_scheduler_metavar
        assert expected in cs.out, "--help is expected to list the supported schedulers"

        # --lr_scheduler=non_existing_scheduler test
        snake_case :			Any							=   '''non_existing_scheduler'''
        snake_case :			Union[str, Any]							=   {f'''--lr_scheduler={unsupported_param}'''}
        with pytest.raises(a__       ) as excinfo:
         with CaptureStderr() as cs:
          snake_case :			Optional[Any]							=   parser.parse_args(a__       )
         assert False, "invalid argument is expected to sys.exit"
        assert excinfo.type == SystemExit
        snake_case :			int							=   f'''invalid choice: \'{unsupported_param}\''''
        assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

        # --lr_scheduler=existing_scheduler test
        snake_case :			int							=   '''cosine'''
        snake_case :			Union[str, Any]							=   args_d.copy()
        snake_case :			str							=   supported_param
        snake_case :			Union[str, Any]							=   argparse.Namespace(**a__       )
        snake_case :			str							=   main(a__       )
        assert (
            getattr(model.hparams						,    '''lr_scheduler'''       ) == supported_param
        ), f'''lr_scheduler={supported_param} shouldn\'t fail'''












import random
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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		import PIL

		from transformers import FlavaImageProcessor
		from transformers.image_utils import PILImageResampling
		from transformers.models.flava.image_processing_flava import (
		    FLAVA_CODEBOOK_MEAN,
		    FLAVA_CODEBOOK_STD,
		    FLAVA_IMAGE_MEAN,
		    FLAVA_IMAGE_STD,
		)
else:
		__A    :				List[str]    						=	None




class       UpperCAmelCase_   ( unittest.TestCase							):




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						List[Any]							,  a							:						Tuple=7							,  a							:						Dict=3							,  a							:						List[Any]=30							,  a							:						List[Any]=400							,  a							:						Optional[Any]=True							,  a							:						Optional[int]=None							,  a							:						Any=True							,  a							:						Union[str, Any]=None							,  a							:						Union[str, Any]=None							,  a							:						List[Any]=True							,  a							:						Union[str, Any]=1 / 255							,  a							:						Optional[Any]=True							,  a							:						int=FLAVA_IMAGE_MEAN							,  a							:						int=FLAVA_IMAGE_STD							,  a							:						Tuple=14							,  a							:						str=75							,  a							:						str=None							,  a							:						int=16							,  a							:						List[str]=0.3							,  a							:						Optional[Any]=None							,  a							:						int=True							,  a							:						int=None							,  a							:						Optional[int]=None							,  a							:						Optional[Any]=True							,  a							:						List[Any]=None							,  a							:						str=True							,  a							:						int=True							,  a							:						str=FLAVA_CODEBOOK_MEAN							,  a							:						str=FLAVA_CODEBOOK_STD							,  )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       size if size is not None else {"""height""": 224, """width""": 224}
										SCREAMING_SNAKE_CASE     =       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										SCREAMING_SNAKE_CASE     =       codebook_size if codebook_size is not None else {"""height""": 112, """width""": 112}
										SCREAMING_SNAKE_CASE     =       codebook_crop_size if codebook_crop_size is not None else {"""height""": 112, """width""": 112}

										SCREAMING_SNAKE_CASE     =       parent
										SCREAMING_SNAKE_CASE     =       batch_size
										SCREAMING_SNAKE_CASE     =       num_channels
										SCREAMING_SNAKE_CASE     =       do_resize
										SCREAMING_SNAKE_CASE     =       do_rescale
										SCREAMING_SNAKE_CASE     =       rescale_factor
										SCREAMING_SNAKE_CASE     =       min_resolution
										SCREAMING_SNAKE_CASE     =       max_resolution
										SCREAMING_SNAKE_CASE     =       size
										SCREAMING_SNAKE_CASE     =       resample if resample is not None else PILImageResampling.BICUBIC
										SCREAMING_SNAKE_CASE     =       do_normalize
										SCREAMING_SNAKE_CASE     =       image_mean
										SCREAMING_SNAKE_CASE     =       image_std
										SCREAMING_SNAKE_CASE     =       do_center_crop
										SCREAMING_SNAKE_CASE     =       crop_size

										SCREAMING_SNAKE_CASE     =       input_size_patches
										SCREAMING_SNAKE_CASE     =       total_mask_patches
										SCREAMING_SNAKE_CASE     =       mask_group_max_patches
										SCREAMING_SNAKE_CASE     =       mask_group_min_patches
										SCREAMING_SNAKE_CASE     =       mask_group_min_aspect_ratio
										SCREAMING_SNAKE_CASE     =       mask_group_max_aspect_ratio

										SCREAMING_SNAKE_CASE     =       codebook_do_resize
										SCREAMING_SNAKE_CASE     =       codebook_size
										SCREAMING_SNAKE_CASE     =       codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
										SCREAMING_SNAKE_CASE     =       codebook_do_center_crop
										SCREAMING_SNAKE_CASE     =       codebook_crop_size
										SCREAMING_SNAKE_CASE     =       codebook_do_map_pixels
										SCREAMING_SNAKE_CASE     =       codebook_do_normalize
										SCREAMING_SNAKE_CASE     =       codebook_image_mean
										SCREAMING_SNAKE_CASE     =       codebook_image_std





							def  _UpperCAmelCase  (       self							:						str      )      ->       str:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "resample": self.resample,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "input_size_patches": self.input_size_patches,
										    "total_mask_patches": self.total_mask_patches,
										    "mask_group_max_patches": self.mask_group_max_patches,
										    "mask_group_min_patches": self.mask_group_min_patches,
										    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
										    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
										    "codebook_do_resize": self.codebook_do_resize,
										    "codebook_size": self.codebook_size,
										    "codebook_resample": self.codebook_resample,
										    "codebook_do_center_crop": self.codebook_do_center_crop,
										    "codebook_crop_size": self.codebook_crop_size,
										    "codebook_do_map_pixels": self.codebook_do_map_pixels,
										    "codebook_do_normalize": self.codebook_do_normalize,
										    "codebook_image_mean": self.codebook_image_mean,
										    "codebook_image_std": self.codebook_image_std,
										}





							def  _UpperCAmelCase  (       self							:						Dict      )      ->       List[Any]:
										return (self.size["height"], self.size["width"])





							def  _UpperCAmelCase  (       self							:						int      )      ->       int:
										return (
										    (self.input_size_patches, self.input_size_patches)
										    if not isinstance(self.input_size_patches							,  a      )
										    else self.input_size_patches
										)







							def  _UpperCAmelCase  (       self							:						str      )      ->       List[Any]:
										return (self.codebook_size["height"], self.codebook_size["width"])





@require_torch
@require_vision
class       UpperCAmelCase_   ( A		,   unittest.TestCase							):




							'''simple docstring'''



							a__    	=  FlavaImageProcessor if is_vision_available() else None
							a__    	=  None





							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Any:
										SCREAMING_SNAKE_CASE     =       FlavaImageProcessingTester(self      )





							@property
							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Any:
										return self.image_processor_tester.prepare_image_processor_dict()





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       str:
										SCREAMING_SNAKE_CASE     =       self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(a							,  """image_mean"""      )      )
										self.assertTrue(hasattr(a							,  """image_std"""      )      )
										self.assertTrue(hasattr(a							,  """do_normalize"""      )      )
										self.assertTrue(hasattr(a							,  """do_resize"""      )      )
										self.assertTrue(hasattr(a							,  """resample"""      )      )
										self.assertTrue(hasattr(a							,  """crop_size"""      )      )
										self.assertTrue(hasattr(a							,  """do_center_crop"""      )      )
										self.assertTrue(hasattr(a							,  """do_rescale"""      )      )
										self.assertTrue(hasattr(a							,  """rescale_factor"""      )      )
										self.assertTrue(hasattr(a							,  """masking_generator"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_do_resize"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_size"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_resample"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_do_center_crop"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_crop_size"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_do_map_pixels"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_do_normalize"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_image_mean"""      )      )
										self.assertTrue(hasattr(a							,  """codebook_image_std"""      )      )





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       Optional[Any]:
										SCREAMING_SNAKE_CASE     =       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""": 224, """width""": 224}      )
										self.assertEqual(image_processor.codebook_size							,  {"""height""": 112, """width""": 112}      )
										self.assertEqual(image_processor.codebook_crop_size							,  {"""height""": 112, """width""": 112}      )

										SCREAMING_SNAKE_CASE     =       self.image_processing_class.from_dict(
										    self.image_processor_dict							,  size=42							,  crop_size=84							,  codebook_size=33							,  codebook_crop_size=66      )
										self.assertEqual(image_processor.size							,  {"""height""": 42, """width""": 42}      )
										self.assertEqual(image_processor.crop_size							,  {"""height""": 84, """width""": 84}      )
										self.assertEqual(image_processor.codebook_size							,  {"""height""": 33, """width""": 33}      )
										self.assertEqual(image_processor.codebook_crop_size							,  {"""height""": 66, """width""": 66}      )





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       Optional[int]:
										pass





							def  _UpperCAmelCase  (       self							:						Optional[Any]      )      ->       Union[str, Any]:
										# Initialize image_processing
										SCREAMING_SNAKE_CASE     =       self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										SCREAMING_SNAKE_CASE     =       prepare_image_inputs(self.image_processor_tester							,  equal_resolution=a      )
										for image in image_inputs:
													self.assertIsInstance(a							,  PIL.Image.Image      )

										# Test not batched input
										SCREAMING_SNAKE_CASE     =       image_processing(image_inputs[0]							,  return_tensors="""pt"""      )

										# Test no bool masked pos
										self.assertFalse("""bool_masked_pos""" in encoded_images      )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()

										self.assertEqual(
										    encoded_images.pixel_values.shape							,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,  )

										# Test batched
										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_tensors="""pt"""      )
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()

										# Test no bool masked pos
										self.assertFalse("""bool_masked_pos""" in encoded_images      )

										self.assertEqual(
										    encoded_images.pixel_values.shape							,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )							,  )





							def  _UpperCAmelCase  (       self							:						Any							,  a							:						List[str]							,  a							:						Tuple      )      ->       Union[str, Any]:
										# Initialize image_processing
										SCREAMING_SNAKE_CASE     =       self.image_processing_class(**self.image_processor_dict      )
										# create random tensors
										SCREAMING_SNAKE_CASE     =       prepare_image_inputs(self.image_processor_tester							,  equal_resolution=a							,  **a      )
										for image in image_inputs:
													self.assertIsInstance(a							,  a      )

										# Test not batched input
										SCREAMING_SNAKE_CASE     =       image_processing(image_inputs[0]							,  return_tensors="""pt"""      )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.pixel_values.shape							,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,  )

										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_image_mask=a							,  return_tensors="""pt"""      )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.pixel_values.shape							,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )							,  )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_mask_size()
										self.assertEqual(
										    encoded_images.bool_masked_pos.shape							,  (
										        self.image_processor_tester.batch_size,
										        expected_height,
										        expected_width,
										    )							,  )

										# Test batched
										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_tensors="""pt"""      ).pixel_values

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.shape							,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )							,  )

										# Test masking
										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_image_mask=a							,  return_tensors="""pt"""      )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_image_size()
										self.assertEqual(
										    encoded_images.pixel_values.shape							,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )							,  )

										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_mask_size()
										self.assertEqual(
										    encoded_images.bool_masked_pos.shape							,  (
										        self.image_processor_tester.batch_size,
										        expected_height,
										        expected_width,
										    )							,  )





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Dict:
										self._test_call_framework(np.ndarray							,  prepare_kwargs={"""numpify""": True}      )





							def  _UpperCAmelCase  (       self							:						Dict      )      ->       Dict:
										self._test_call_framework(torch.Tensor							,  prepare_kwargs={"""torchify""": True}      )





							def  _UpperCAmelCase  (       self							:						str      )      ->       Optional[int]:
										# Initialize image_processing
										random.seed(1_234      )
										SCREAMING_SNAKE_CASE     =       self.image_processing_class(**self.image_processor_dict      )
										SCREAMING_SNAKE_CASE     =       prepare_image_inputs(self.image_processor_tester							,  equal_resolution=a							,  torchify=a      )

										# Test not batched input
										SCREAMING_SNAKE_CASE     =       image_processing(image_inputs[0]							,  return_image_mask=a							,  return_tensors="""pt"""      )
										self.assertEqual(encoded_images.bool_masked_pos.sum().item()							,  75      )







							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Tuple:
										# Initialize image_processing
										SCREAMING_SNAKE_CASE     =       self.image_processing_class(**self.image_processor_dict      )
										# create random PIL images
										SCREAMING_SNAKE_CASE     =       prepare_image_inputs(self.image_processor_tester							,  equal_resolution=a      )
										for image in image_inputs:
													self.assertIsInstance(a							,  PIL.Image.Image      )

										# Test not batched input
										SCREAMING_SNAKE_CASE     =       image_processing(image_inputs[0]							,  return_codebook_pixels=a							,  return_tensors="""pt"""      )
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_codebook_image_size()
										self.assertEqual(
										    encoded_images.codebook_pixel_values.shape							,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)							,  )

										# Test batched
										SCREAMING_SNAKE_CASE     =       image_processing(a							,  return_codebook_pixels=a							,  return_tensors="""pt"""      )
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.image_processor_tester.get_expected_codebook_image_size()
										self.assertEqual(
										    encoded_images.codebook_pixel_values.shape							,  (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )							,  )






