


import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import FlavaImageProcessor, FlavaProcessor
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )







@require_vision
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Dict:
      _lowercase							   =tempfile.mkdtemp()

      # fmt: off
      _lowercase							   =['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
      # fmt: on
      _lowercase							   =os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES['''vocab_file'''] )

      with open(self.vocab_file	,  '''w'''	,  encoding='''utf-8''' ) as fp:
       fp.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )

      _lowercase							   ={
          '''image_mean''': FLAVA_IMAGE_MEAN,
          '''image_std''': FLAVA_IMAGE_STD,
          '''do_normalize''': True,
          '''do_resize''': True,
          '''size''': 2_2_4,
          '''do_center_crop''': True,
          '''crop_size''': 2_2_4,
          '''input_size_patches''': 1_4,
          '''total_mask_patches''': 7_5,
          '''mask_group_max_patches''': None,
          '''mask_group_min_patches''': 1_6,
          '''mask_group_min_aspect_ratio''': 0.3,
          '''mask_group_max_aspect_ratio''': None,
          '''codebook_do_resize''': True,
          '''codebook_size''': 1_1_2,
          '''codebook_do_center_crop''': True,
          '''codebook_crop_size''': 1_1_2,
          '''codebook_do_map_pixels''': True,
          '''codebook_do_normalize''': True,
          '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
          '''codebook_image_std''': FLAVA_CODEBOOK_STD,
      }

      _lowercase							   =os.path.join(self.tmpdirname	,  UpperCAmelCase )
      with open(self.image_processor_file	,  '''w'''	,  encoding='''utf-8''' ) as fp:
       json.dump(UpperCAmelCase	,  UpperCAmelCase )





     def   __A      (self	,  **UpperCAmelCase )  ->     List[str]:
      return BertTokenizer.from_pretrained(self.tmpdirname	,  **UpperCAmelCase )





     def   __A      (self	,  **UpperCAmelCase )  ->     str:
      return BertTokenizerFast.from_pretrained(self.tmpdirname	,  **UpperCAmelCase )





     def   __A      (self	,  **UpperCAmelCase )  ->     List[Any]:
      return FlavaImageProcessor.from_pretrained(self.tmpdirname	,  **UpperCAmelCase )





     def   __A      (self )  ->     Tuple:
      shutil.rmtree(self.tmpdirname )





     def   __A      (self )  ->     Dict:
      _lowercase							   =[np.random.randint(2_5_5	,  size=(3, 3_0, 4_0_0)	,  dtype=np.uinta )]

      _lowercase							   =[Image.fromarray(np.moveaxis(UpperCAmelCase	,  0	,  -1 ) ) for x in image_inputs]

      return image_inputs





     def   __A      (self )  ->     Optional[int]:
      _lowercase							   =self.get_tokenizer()
      _lowercase							   =self.get_rust_tokenizer()
      _lowercase							   =self.get_image_processor()

      _lowercase							   =FlavaProcessor(tokenizer=UpperCAmelCase	,  image_processor=UpperCAmelCase )
      processor_slow.save_pretrained(self.tmpdirname )
      _lowercase							   =FlavaProcessor.from_pretrained(self.tmpdirname	,  use_fast=UpperCAmelCase )

      _lowercase							   =FlavaProcessor(tokenizer=UpperCAmelCase	,  image_processor=UpperCAmelCase )
      processor_fast.save_pretrained(self.tmpdirname )
      _lowercase							   =FlavaProcessor.from_pretrained(self.tmpdirname )

      self.assertEqual(processor_slow.tokenizer.get_vocab()	,  tokenizer_slow.get_vocab() )
      self.assertEqual(processor_fast.tokenizer.get_vocab()	,  tokenizer_fast.get_vocab() )
      self.assertEqual(tokenizer_slow.get_vocab()	,  tokenizer_fast.get_vocab() )
      self.assertIsInstance(processor_slow.tokenizer	,  UpperCAmelCase )
      self.assertIsInstance(processor_fast.tokenizer	,  UpperCAmelCase )

      self.assertEqual(processor_slow.image_processor.to_json_string()	,  image_processor.to_json_string() )
      self.assertEqual(processor_fast.image_processor.to_json_string()	,  image_processor.to_json_string() )
      self.assertIsInstance(processor_slow.image_processor	,  UpperCAmelCase )
      self.assertIsInstance(processor_fast.image_processor	,  UpperCAmelCase )





     def   __A      (self )  ->     Dict:
      _lowercase							   =FlavaProcessor(tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor() )
      processor.save_pretrained(self.tmpdirname )

      _lowercase							   =self.get_tokenizer(bos_token='''(BOS)'''	,  eos_token='''(EOS)''' )
      _lowercase							   =self.get_image_processor(do_normalize=UpperCAmelCase	,  padding_value=1.0 )

      _lowercase							   =FlavaProcessor.from_pretrained(
          self.tmpdirname	,  bos_token='''(BOS)'''	,  eos_token='''(EOS)'''	,  do_normalize=UpperCAmelCase	,  padding_value=1.0 )

      self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab() )
      self.assertIsInstance(processor.tokenizer	,  UpperCAmelCase )

      self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string() )
      self.assertIsInstance(processor.image_processor	,  UpperCAmelCase )





     def   __A      (self )  ->     Optional[int]:
      _lowercase							   =self.get_image_processor()
      _lowercase							   =self.get_tokenizer()

      _lowercase							   =FlavaProcessor(tokenizer=UpperCAmelCase	,  image_processor=UpperCAmelCase )

      _lowercase							   =self.prepare_image_inputs()

      _lowercase							   =image_processor(UpperCAmelCase	,  return_tensors='''np''' )
      _lowercase							   =processor(images=UpperCAmelCase	,  return_tensors='''np''' )

      for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()	,  input_processor[key].sum()	,  delta=1e-2 )

      # With rest of the args
      random.seed(1_2_3_4 )
      _lowercase							   =image_processor(
          UpperCAmelCase	,  return_image_mask=UpperCAmelCase	,  return_codebook_pixels=UpperCAmelCase	,  return_tensors='''np''' )
      random.seed(1_2_3_4 )
      _lowercase							   =processor(
          images=UpperCAmelCase	,  return_image_mask=UpperCAmelCase	,  return_codebook_pixels=UpperCAmelCase	,  return_tensors='''np''' )

      for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()	,  input_processor[key].sum()	,  delta=1e-2 )





     def   __A      (self )  ->     int:
      _lowercase							   =self.get_image_processor()
      _lowercase							   =self.get_tokenizer()

      _lowercase							   =FlavaProcessor(tokenizer=UpperCAmelCase	,  image_processor=UpperCAmelCase )

      _lowercase							   ='''lower newer'''

      _lowercase							   =processor(text=UpperCAmelCase )

      _lowercase							   =tokenizer(UpperCAmelCase )

      for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]	,  encoded_processor[key] )





     def   __A      (self )  ->     Optional[int]:
      _lowercase							   =self.get_image_processor()
      _lowercase							   =self.get_tokenizer()

      _lowercase							   =FlavaProcessor(tokenizer=UpperCAmelCase	,  image_processor=UpperCAmelCase )

      _lowercase							   ='''lower newer'''
      _lowercase							   =self.prepare_image_inputs()

      _lowercase							   =processor(text=UpperCAmelCase	,  images=UpperCAmelCase )

      self.assertListEqual(list(inputs.keys() )	,  ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )

      # add extra args
      _lowercase							   =processor(text=UpperCAmelCase	,  images=UpperCAmelCase	,  return_codebook_pixels=UpperCAmelCase	,  return_image_mask=UpperCAmelCase )

      self.assertListEqual(
          list(inputs.keys() )	,  [
              '''input_ids''',
              '''token_type_ids''',
              '''attention_mask''',
              '''pixel_values''',
              '''codebook_pixel_values''',
              '''bool_masked_pos''',
          ]	,  )

      # test if it raises when no input is passed
      with pytest.raises(UpperCAmelCase ):
       processor()





     def   __A      (self )  ->     Tuple:
      _lowercase							   =self.get_image_processor()
      _lowercase							   =self.get_tokenizer()

      _lowercase							   =FlavaProcessor(tokenizer=UpperCAmelCase	,  image_processor=UpperCAmelCase )

      _lowercase							   =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _lowercase							   =processor.batch_decode(UpperCAmelCase )
      _lowercase							   =tokenizer.batch_decode(UpperCAmelCase )

      self.assertListEqual(UpperCAmelCase	,  UpperCAmelCase )





     def   __A      (self )  ->     List[Any]:
      _lowercase							   =self.get_image_processor()
      _lowercase							   =self.get_tokenizer()

      _lowercase							   =FlavaProcessor(tokenizer=UpperCAmelCase	,  image_processor=UpperCAmelCase )

      _lowercase							   ='''lower newer'''
      _lowercase							   =self.prepare_image_inputs()

      _lowercase							   =processor(text=UpperCAmelCase	,  images=UpperCAmelCase )

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





import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Dict:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.OneCycleLR(__snake_case  ,		max_lr=0.01  ,		steps_per_epoch=2  ,		epochs=1		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 scheduler.step()
 if step_scheduler_with_optimizer or (num_processes == 1):
  assert (
      scheduler.scheduler.last_epoch == num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
 else:
  assert (
      scheduler.scheduler.last_epoch != num_processes
  ), F"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"
def        UpperCAmelCase_      (							__snake_case=2  ,		__snake_case=True  ,		__snake_case=False		)  ->    Optional[int]:







 """simple docstring"""


 _lowercase							   =Accelerator(step_scheduler_with_optimizer=__snake_case  ,		split_batches=__snake_case		)
 _lowercase							   =torch.nn.Linear(2  ,		4		)
 _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=1.0		)
 _lowercase							   =torch.optim.lr_scheduler.LambdaLR(__snake_case  ,		lr_lambda=lambda __snake_case		: 1 - n / 10		)
 _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

 # Optimizer has stepped
 _lowercase							   =False
 scheduler.step()
 _lowercase							   =1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

 # Optimizer has not stepped
 _lowercase							   =True
 scheduler.step()
 if not step_scheduler_with_optimizer:
  _lowercase							   =1 - 2 / 10
 assert (
     scheduler.get_last_lr()[0] == expected_lr
 ), F"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"
def        UpperCAmelCase_      (							__snake_case = 2		)  ->    Optional[int]:







 """simple docstring"""


 from transformers import get_linear_schedule_with_warmup

 _lowercase							   =[1, 2, 4]
 for num_steps in steps:
  _lowercase							   =GradientAccumulationPlugin(num_steps=__snake_case  ,		adjust_scheduler=num_steps > 1		)
  _lowercase							   =Accelerator(gradient_accumulation_plugin=__snake_case		)
  _lowercase							   =torch.nn.Linear(2  ,		4		)
  _lowercase							   =torch.optim.AdamW(model.parameters()  ,		lr=10.0		)
  _lowercase							   =get_linear_schedule_with_warmup(optimizer=__snake_case  ,		num_warmup_steps=0  ,		num_training_steps=20		)

  _lowercase   ,					_lowercase   ,					_lowercase							   =accelerator.prepare(__snake_case  ,		__snake_case  ,		__snake_case		)

  for i in range(10 * num_steps		):
   with accelerator.accumulate(__snake_case		):
    optimizer.step()
    scheduler.step()

   if i == (10 * num_steps - 2):
    assert (
        scheduler.get_last_lr()[0] != 0
    ), F"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
  assert (
      scheduler.get_last_lr()[0] == 0
  ), F"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
  GradientState._reset_state()







@require_cpu
class    lowerCamelCase__     ( unittest.TestCase):





     def   __A      (self )  ->     Optional[Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Any:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 )	,  num_processes=1 )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Union[str, Any]:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Dict:
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  step_scheduler_with_optimizer=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     Optional[Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1	,  split_batches=UpperCAmelCase )	,  num_processes=1 )





     def   __A      (self )  ->     List[str]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     def   __A      (self )  ->     Union[str, Any]:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  step_scheduler_with_optimizer=UpperCAmelCase ) )





     @require_huggingface_suite
     def   __A      (self )  ->     int:
      AcceleratorState._reset_state(UpperCAmelCase )
      debug_launcher(partial(UpperCAmelCase	,  num_processes=1 ) )
      debug_launcher(UpperCAmelCase )

