
import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class     lowercase		( SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	Optional[Any]	 =CLIPTokenizer
							lowercase_	:	int	 =CLIPTokenizerFast
							lowercase_	:	str	 =True
							lowercase_	:	Dict	 ={}
							lowercase_	:	Any	 =False




							def 			A__				(    self):
												super().setUp()

												# fmt: off
												lowercase						     =						['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
												# fmt: on
												lowercase						     =						dict(zip(A__       ,range(len(A__))))
												lowercase						     =						['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
												lowercase						     =						{'''unk_token''': '''<unk>'''}

												lowercase						     =						os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['''vocab_file'''])
												lowercase						     =						os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['''merges_file'''])
												with open(self.vocab_file       ,'''w'''       ,encoding='''utf-8''') as fp:
																	fp.write(json.dumps(A__) + '''\n''')
												with open(self.merges_file       ,'''w'''       ,encoding='''utf-8''') as fp:
																	fp.write('''\n'''.join(A__))




							def 			A__				(    self       ,**A__):
												kwargs.update(self.special_tokens_map)
												return CLIPTokenizer.from_pretrained(self.tmpdirname       ,**A__)




							def 			A__				(    self       ,**A__):
												kwargs.update(self.special_tokens_map)
												return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,**A__)




							def 			A__				(    self       ,A__):
												lowercase						     =						'''lower newer'''
												lowercase						     =						'''lower newer'''
												return input_text, output_text




							def 			A__				(    self):
												lowercase						     =						CLIPTokenizer(self.vocab_file       ,self.merges_file       ,**self.special_tokens_map)
												lowercase						     =						'''lower newer'''
												lowercase						     =						['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
												lowercase						     =						tokenizer.tokenize(A__)
												self.assertListEqual(A__       ,A__)

												lowercase						     =						tokens + [tokenizer.unk_token]
												lowercase						     =						[1_0, 2, 1_6, 9, 3, 2, 1_6, 2_0]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(A__)       ,A__)




							@require_ftfy
							def 			A__				(    self):
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
																						lowercase						     =						self.tokenizer_class.from_pretrained(A__       ,**A__)
																						lowercase						     =						self.rust_tokenizer_class.from_pretrained(A__       ,**A__)

																						lowercase						     =						'''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
																						lowercase						     =						tokenizer_s.tokenize(A__)
																						lowercase						     =						tokenizer_r.tokenize(A__)

																						self.assertListEqual(A__       ,A__)

																						# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																						# with Tilde) encoded in 2 different ways
																						lowercase						     =						'''xa\u0303y''' + ''' ''' + '''x\xe3y'''
																						lowercase						     =						tokenizer_s.tokenize(A__)
																						lowercase						     =						tokenizer_r.tokenize(A__)

																						self.assertListEqual(A__       ,A__)

																						# Test that the tokenization is identical on unicode of space type
																						lowercase						     =						[
																						    '''\u0009''',  # (horizontal tab, '\t')
																						    '''\u000B''',  # (vertical tab)
																						    '''\u000C''',  # (form feed)
																						    '''\u0020''',  # (space, ' ')
																						    '''\u200E''',  # (left-to-right mark):w
																						    '''\u200F''',  # (right-to-left mark)
																						]
																						for unicode_seq in spaces_unicodes:
																											lowercase						     =						tokenizer_s.tokenize(A__)
																											lowercase						     =						tokenizer_r.tokenize(A__)

																											self.assertListEqual(A__       ,A__)

																						# Test that the tokenization is identical on unicode of line break type
																						lowercase						     =						[
																						    '''\u000A''',  # (line feed, '\n')
																						    '''\r\n''',  # (carriage return and line feed, '\r\n')
																						    '''\u000D''',  # (carriage return, '\r')
																						    '''\r''',  # (carriage return, '\r')
																						    '''\u000D''',  # (carriage return, '\r')
																						    '''\u2028''',  # (line separator)
																						    '''\u2029''',  # (paragraph separator)
																						    # "\u0085", # (next line)
																						]

																						# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																						# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																						# space (and thus into an empty list).

																						for unicode_seq in line_break_unicodes:
																											lowercase						     =						tokenizer_s.tokenize(A__)
																											lowercase						     =						tokenizer_r.tokenize(A__)

																											self.assertListEqual(A__       ,A__)




							def 			A__				(    self):
												# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
																						lowercase						     =						'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
																						lowercase						     =						f'{text_of_1_token} {text_of_1_token}'

																						lowercase						     =						self.rust_tokenizer_class.from_pretrained(
																						    A__       ,use_fast=A__       ,)
																						lowercase						     =						tokenizer_r(A__       ,return_offsets_mapping=A__       ,add_special_tokens=A__)
																						self.assertEqual(encoding.offset_mapping[0]       ,(0, len(A__)))
																						self.assertEqual(
																						    encoding.offset_mapping[1]       ,(len(A__) + 1, len(A__) + 1 + len(A__))       ,)

																						lowercase						     =						f' {text}'

																						lowercase						     =						self.rust_tokenizer_class.from_pretrained(
																						    A__       ,use_fast=A__       ,)
																						lowercase						     =						tokenizer_r(A__       ,return_offsets_mapping=A__       ,add_special_tokens=A__)
																						self.assertEqual(encoding.offset_mapping[0]       ,(1, 1 + len(A__)))
																						self.assertEqual(
																						    encoding.offset_mapping[1]       ,(1 + len(A__) + 1, 1 + len(A__) + 1 + len(A__))       ,)




							def 			A__				(    self):
												# Test related to the breaking change introduced in transformers v4.17.0
												# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
												with self.assertRaises(A__) as context:
																	self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer''')

												self.assertTrue(
												    context.exception.args[0].startswith(
												        '''The `backend_tokenizer` provided does not match the expected format.'''))




							@require_ftfy
							def 			A__				(    self):
												super().test_tokenization_python_rust_equals()





							def 			A__				(    self):
												# CLIP always lower cases letters
												pass






import argparse
import os

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
# and perform gradient accumulation
#
# 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
#
########################################################################


lowercase__		:Union[str, Any]			  =   16
lowercase__		:Optional[Any]			  =   32



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__ = 16		):
					'''simple docstring'''
					lowercase						     =						AutoTokenizer.from_pretrained('''bert-base-cased'''		)
					lowercase						     =						load_dataset('''glue'''			,      '''mrpc'''		)

					def tokenize_function(lowerCAmelCase__		):
										# max_length=None => use the model max length (it's actually the default)
										lowercase						     =						tokenizer(examples['''sentence1''']			,      examples['''sentence2''']			,      truncation=lowerCAmelCase__			,      max_length=lowerCAmelCase__		)
										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():
										lowercase						     =						datasets.map(
										    lowerCAmelCase__			,      batched=lowerCAmelCase__			,      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
					lowercase						     =						tokenized_datasets.rename_column('''label'''			,      '''labels'''		)

					def collate_fn(lowerCAmelCase__		):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										lowercase						     =						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":
															lowercase						     =						16
										elif accelerator.mixed_precision != "no":
															lowercase						     =						8
										else:
															lowercase						     =						None

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

					# Instantiate dataloaders.
					lowercase						     =						DataLoader(
					    tokenized_datasets['''train''']			,      shuffle=lowerCAmelCase__			,      collate_fn=lowerCAmelCase__			,      batch_size=lowerCAmelCase__		)
					lowercase						     =						DataLoader(
					    tokenized_datasets['''validation''']			,      shuffle=lowerCAmelCase__			,      collate_fn=lowerCAmelCase__			,      batch_size=lowerCAmelCase__		)

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							lowercase__		:List[str]			  =   mocked_dataloaders  # noqa: F811



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					# For testing only
					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,      lowerCAmelCase__		) == "1":
										lowercase						     =						2
					# New Code #
					lowercase						     =						int(args.gradient_accumulation_steps		)
					# Initialize accelerator
					lowercase						     =						Accelerator(
					    cpu=args.cpu			,      mixed_precision=args.mixed_precision			,      gradient_accumulation_steps=lowerCAmelCase__		)
					if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
										raise NotImplementedError(
										    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''		)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					lowercase						     =						config['''lr''']
					lowercase						     =						int(config['''num_epochs''']		)
					lowercase						     =						int(config['''seed''']		)
					lowercase						     =						int(config['''batch_size''']		)

					lowercase						     =						evaluate.load('''glue'''			,      '''mrpc'''		)

					set_seed(lowerCAmelCase__		)
					lowercase     ,						lowercase						     =						get_dataloaders(lowerCAmelCase__			,      lowerCAmelCase__		)
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowercase						     =						AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,      return_dict=lowerCAmelCase__		)

					# 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).
					lowercase						     =						model.to(accelerator.device		)

					# Instantiate optimizer
					lowercase						     =						AdamW(params=model.parameters()			,      lr=lowerCAmelCase__		)

					# Instantiate scheduler
					lowercase						     =						get_linear_schedule_with_warmup(
					    optimizer=lowerCAmelCase__			,      num_warmup_steps=100			,      num_training_steps=(len(lowerCAmelCase__		) * num_epochs)			,      )

					# 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.
					lowercase     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase						     =						accelerator.prepare(
					    lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		)

					# Now we train the model
					for epoch in range(lowerCAmelCase__		):
										model.train()
										for step, batch in enumerate(lowerCAmelCase__		):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device		)
															# New code #
															# We use the new `accumulate` context manager to perform gradient accumulation
															# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
															with accelerator.accumulate(lowerCAmelCase__		):
																				lowercase						     =						model(**lowerCAmelCase__		)
																				lowercase						     =						output.loss
																				accelerator.backward(lowerCAmelCase__		)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

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

										lowercase						     =						metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'epoch {epoch}:'			,      lowerCAmelCase__		)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						argparse.ArgumentParser(description='''Simple example of training script.'''		)
					parser.add_argument(
					    '''--mixed_precision'''			,      type=lowerCAmelCase__			,      default=lowerCAmelCase__			,      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.'''			,      )
					# New Code #
					parser.add_argument(
					    '''--gradient_accumulation_steps'''			,      type=lowerCAmelCase__			,      default=1			,      help='''The number of minibatches to be ran before gradients are accumulated.'''			,      )
					parser.add_argument('''--cpu'''			,      action='''store_true'''			,      help='''If passed, will train on the CPU.'''		)
					lowercase						     =						parser.parse_args()
					lowercase						     =						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(lowerCAmelCase__			,      lowerCAmelCase__		)


if __name__ == "__main__":
							main()




