





'''simple docstring'''






import json
import os
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 FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class 					__snake_case  (					unittest.TestCase							):






   '''simple docstring'''





   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					str    =  tempfile.mkdtemp()

    snake_case__     :					str    =  [
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """[PAD]""",
        """[MASK]""",
        """的""",
        """价""",
        """格""",
        """是""",
        """15""",
        """便""",
        """alex""",
        """##andra""",
        """，""",
        """。""",
        """-""",
        """t""",
        """shirt""",
    ]
    snake_case__     :					int    =  os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES["""vocab_file"""]       )
    with open(self.vocab_file	,		"""w"""	,		encoding="""utf-8"""       ) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )

    snake_case__     :					List[Any]    =  {
        """do_resize""": True,
        """size""": {"""height""": 2_2_4, """width""": 2_2_4},
        """do_center_crop""": True,
        """crop_size""": {"""height""": 1_8, """width""": 1_8},
        """do_normalize""": True,
        """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
        """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
        """do_convert_rgb""": True,
    }
    snake_case__     :					Optional[int]    =  os.path.join(self.tmpdirname	,		__SCREAMING_SNAKE_CASE       )
    with open(self.image_processor_file	,		"""w"""	,		encoding="""utf-8"""       ) as fp:
     json.dump(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    return BertTokenizer.from_pretrained(self.tmpdirname	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    return BertTokenizerFast.from_pretrained(self.tmpdirname	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    shutil.rmtree(self.tmpdirname       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Dict    =  [np.random.randint(2_5_5	,		size=(3, 3_0, 4_0_0)	,		dtype=np.uinta       )]

    snake_case__     :					Optional[Any]    =  [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE	,		0	,		-1       )       ) for x in image_inputs]

    return image_inputs

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[Any]    =  self.get_tokenizer()
    snake_case__     :					Optional[Any]    =  self.get_rust_tokenizer()
    snake_case__     :					Dict    =  self.get_image_processor()

    snake_case__     :					int    =  ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )
    processor_slow.save_pretrained(self.tmpdirname       )
    snake_case__     :					Optional[int]    =  ChineseCLIPProcessor.from_pretrained(self.tmpdirname	,		use_fast=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Any    =  ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )
    processor_fast.save_pretrained(self.tmpdirname       )
    snake_case__     :					int    =  ChineseCLIPProcessor.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	,		__SCREAMING_SNAKE_CASE       )
    self.assertIsInstance(processor_fast.tokenizer	,		__SCREAMING_SNAKE_CASE       )

    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	,		__SCREAMING_SNAKE_CASE       )
    self.assertIsInstance(processor_fast.image_processor	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Any    =  ChineseCLIPProcessor(tokenizer=self.get_tokenizer()	,		image_processor=self.get_image_processor()       )
    processor.save_pretrained(self.tmpdirname       )

    snake_case__     :					Any    =  self.get_tokenizer(cls_token="""(CLS)"""	,		sep_token="""(SEP)"""       )
    snake_case__     :					int    =  self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					List[str]    =  ChineseCLIPProcessor.from_pretrained(
        self.tmpdirname	,		cls_token="""(CLS)"""	,		sep_token="""(SEP)"""	,		do_normalize=__SCREAMING_SNAKE_CASE       )

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

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

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					int    =  self.get_image_processor()
    snake_case__     :					Tuple    =  self.get_tokenizer()

    snake_case__     :					Optional[Any]    =  ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					List[Any]    =  self.prepare_image_inputs()

    snake_case__     :					List[str]    =  image_processor(__SCREAMING_SNAKE_CASE	,		return_tensors="""np"""       )
    snake_case__     :					Optional[Any]    =  processor(images=__SCREAMING_SNAKE_CASE	,		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 		__UpperCamelCase						(							self       ):
    snake_case__     :					Any    =  self.get_image_processor()
    snake_case__     :					Any    =  self.get_tokenizer()

    snake_case__     :					Tuple    =  ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					List[str]    =  """Alexandra，T-shirt的价格是15便士。"""

    snake_case__     :					List[Any]    =  processor(text=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Tuple    =  tokenizer(__SCREAMING_SNAKE_CASE       )

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

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Any    =  self.get_image_processor()
    snake_case__     :					str    =  self.get_tokenizer()

    snake_case__     :					Tuple    =  ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					str    =  """Alexandra，T-shirt的价格是15便士。"""
    snake_case__     :					Union[str, Any]    =  self.prepare_image_inputs()

    snake_case__     :					Tuple    =  processor(text=__SCREAMING_SNAKE_CASE	,		images=__SCREAMING_SNAKE_CASE       )

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

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

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[int]    =  self.get_image_processor()
    snake_case__     :					Dict    =  self.get_tokenizer()

    snake_case__     :					Optional[int]    =  ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					List[Any]    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    snake_case__     :					Union[str, Any]    =  processor.batch_decode(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Optional[int]    =  tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )

    self.assertListEqual(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )


   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					int    =  self.get_image_processor()
    snake_case__     :					Dict    =  self.get_tokenizer()

    snake_case__     :					str    =  ChineseCLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,		image_processor=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					str    =  """Alexandra，T-shirt的价格是15便士。"""
    snake_case__     :					List[str]    =  self.prepare_image_inputs()

    snake_case__     :					Tuple    =  processor(text=__SCREAMING_SNAKE_CASE	,		images=__SCREAMING_SNAKE_CASE       )

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








'''simple docstring'''
import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__snake_case				 =	16
__snake_case				 =	32


def      a (     __a					,   __a = 16  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :List[str]				   =					AutoTokenizer.from_pretrained('''bert-base-cased'''  )
					UpperCamelCase__  :Optional[Any]				   =					load_dataset('''glue'''					,   '''mrpc'''  )

					def tokenize_function(__a  ):
										# max_length=None => use the model max length (it's actually the default)
										UpperCamelCase__  :Any				   =					tokenizer(examples['''sentence1''']					,   examples['''sentence2''']					,   truncation=__a					,   max_length=__a  )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										UpperCamelCase__  :str				   =					datasets.map(
										    __a					,   batched=__a					,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']					,   )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					UpperCamelCase__  :Optional[int]				   =					tokenized_datasets.rename_column('''label'''					,   '''labels'''  )

					def collate_fn(__a  ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										UpperCamelCase__  :Union[str, Any]				   =					128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															UpperCamelCase__  :str				   =					16
										elif accelerator.mixed_precision != "no":
															UpperCamelCase__  :Tuple				   =					8
										else:
															UpperCamelCase__  :int				   =					None

										return tokenizer.pad(
										    __a					,   padding='''longest'''					,   max_length=__a					,   pad_to_multiple_of=__a					,   return_tensors='''pt'''					,   )

					# Instantiate dataloaders.
					UpperCamelCase__  :Any				   =					DataLoader(
					    tokenized_datasets['''train''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a					,   drop_last=__a  )
					UpperCamelCase__  :Any				   =					DataLoader(
					    tokenized_datasets['''validation''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a					,   drop_last=(accelerator.mixed_precision == '''fp8''')					,   )

					return train_dataloader, eval_dataloader


def      a (     __a					,   __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :List[Any]				   =					Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCamelCase__  :List[Any]				   =					config['''lr''']
					UpperCamelCase__  :List[str]				   =					int(config['''num_epochs''']  )
					UpperCamelCase__  :int				   =					int(config['''seed''']  )
					UpperCamelCase__  :str				   =					int(config['''batch_size''']  )

					UpperCamelCase__  :List[str]				   =					evaluate.load('''glue'''					,   '''mrpc'''  )

					# If the batch size is too big we use gradient accumulation
					UpperCamelCase__  :Tuple				   =					1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										UpperCamelCase__  :Optional[Any]				   =					batch_size // MAX_GPU_BATCH_SIZE
										UpperCamelCase__  :Dict				   =					MAX_GPU_BATCH_SIZE

					set_seed(__a  )
					UpperCamelCase__   ,							UpperCamelCase__  :Tuple				   =					get_dataloaders(__a					,   __a  )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase__  :List[Any]				   =					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''					,   return_dict=__a  )

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					UpperCamelCase__  :Optional[int]				   =					model.to(accelerator.device  )
					# Instantiate optimizer
					UpperCamelCase__  :Union[str, Any]				   =					AdamW(params=model.parameters()					,   lr=__a  )

					# Instantiate scheduler
					UpperCamelCase__  :Optional[Any]				   =					get_linear_schedule_with_warmup(
					    optimizer=__a					,   num_warmup_steps=100					,   num_training_steps=(len(__a  ) * num_epochs) // gradient_accumulation_steps					,   )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.

					UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__  :str				   =					accelerator.prepare(
					    __a					,   __a					,   __a					,   __a					,   __a  )

					# Now we train the model
					for epoch in range(__a  ):
										model.train()
										for step, batch in enumerate(__a  ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device  )
															UpperCamelCase__  :Dict				   =					model(**__a  )
															UpperCamelCase__  :Union[str, Any]				   =					outputs.loss
															UpperCamelCase__  :Tuple				   =					loss / gradient_accumulation_steps
															accelerator.backward(__a  )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

										model.eval()
										for step, batch in enumerate(__a  ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device  )
															with torch.no_grad():
																				UpperCamelCase__  :Optional[int]				   =					model(**__a  )
															UpperCamelCase__  :int				   =					outputs.logits.argmax(dim=-1  )
															UpperCamelCase__   ,							UpperCamelCase__  :List[Any]				   =					accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
															metric.add_batch(
															    predictions=__a					,   references=__a					,   )

										UpperCamelCase__  :List[str]				   =					metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}:'''					,   __a  )


def      a (     )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :Union[str, Any]				   =					argparse.ArgumentParser(description='''Simple example of training script.'''  )
					parser.add_argument(
					    '''--mixed_precision'''					,   type=__a					,   default=__a					,   choices=['''no''', '''fp16''', '''bf16''', '''fp8''']					,   help='''Whether to use mixed precision. Choose'''
					    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
					    '''and an Nvidia Ampere GPU.'''					,   )
					parser.add_argument('''--cpu'''					,   action='''store_true'''					,   help='''If passed, will train on the CPU.'''  )
					UpperCamelCase__  :int				   =					parser.parse_args()
					UpperCamelCase__  :int				   =					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__a					,   __a  )


if __name__ == "__main__":
		main()