


'''simple docstring'''






import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__UpperCAmelCase       =							"true"


def  _snake_case       (					A			,   A=82			,   A=16	)						->   int:
							set_seed(42	)
							lowerCAmelCase__	       =     RegressionModel()
							lowerCAmelCase__	       =     deepcopy(A__	)
							lowerCAmelCase__	       =     RegressionDataset(length=A__	)
							lowerCAmelCase__	       =     DataLoader(A__			,   batch_size=A__	)
							model.to(accelerator.device	)
							lowerCAmelCase__	       =     accelerator.prepare(A__			,   A__	)
							return model, ddp_model, dataloader


def  _snake_case       (					A			,   A=False	)						->   List[Any]:
							lowerCAmelCase__	       =     AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''	)
							lowerCAmelCase__	       =     load_dataset('''glue'''			,   '''mrpc'''			,   split='''validation'''	)

							def tokenize_function(A	):
														lowerCAmelCase__	       =     tokenizer(examples['''sentence1''']			,   examples['''sentence2''']			,   truncation=A__			,   max_length=A__	)
														return outputs

							with accelerator.main_process_first():
														lowerCAmelCase__	       =     dataset.map(
														    A__			,   batched=A__			,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,   )

							lowerCAmelCase__	       =     tokenized_datasets.rename_column('''label'''			,   '''labels'''	)

							def collate_fn(A	):
														if use_longest:
																					return tokenizer.pad(A__			,   padding='''longest'''			,   return_tensors='''pt'''	)
														return tokenizer.pad(A__			,   padding='''max_length'''			,   max_length=128			,   return_tensors='''pt'''	)

							return DataLoader(A__			,   shuffle=A__			,   collate_fn=A__			,   batch_size=16	)


def  _snake_case       (					A			,   A	)						->   str:
							lowerCAmelCase__	       =     Accelerator(dispatch_batches=A__			,   split_batches=A__	)
							lowerCAmelCase__	       =     get_dataloader(A__			,   not dispatch_batches	)
							lowerCAmelCase__	       =     AutoModelForSequenceClassification.from_pretrained(
							    '''hf-internal-testing/mrpc-bert-base-cased'''			,   return_dict=A__	)
							lowerCAmelCase__	       =     accelerator.prepare(A__			,   A__	)
							return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def  _snake_case       (					A			,   A			,   A	)						->   Tuple:
							lowerCAmelCase__	       =     []
							for batch in dataloader:
														lowerCAmelCase__	       =     batch.values()
														with torch.no_grad():
																					lowerCAmelCase__	       =     model(A__	)
																					lowerCAmelCase__	       =     accelerator.gather_for_metrics((logit, target)	)
																					logits_and_targets.append((logit, target)	)
							lowerCAmelCase__	       =     [], []
							for logit, targ in logits_and_targets:
														logits.append(A__	)
														targs.append(A__	)
							lowerCAmelCase__	       =     torch.cat(A__	), torch.cat(A__	)
							return logits, targs


def  _snake_case       (					A			,   A=82			,   A=False			,   A=False			,   A=16	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     get_basic_setup(A__			,   A__			,   A__	)
							lowerCAmelCase__	       =     generate_predictions(A__			,   A__			,   A__	)
							assert (
							    len(A__	) == num_samples
							), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A__	)}"""


def  _snake_case       (					A = False			,   A = False	)						->   Any:
							lowerCAmelCase__	       =     evaluate.load('''glue'''			,   '''mrpc'''	)
							lowerCAmelCase__	       =     get_mrpc_setup(A__			,   A__	)
							# First do baseline
							lowerCAmelCase__	       =     setup["""no"""]
							model.to(A__	)
							model.eval()
							for batch in dataloader:
														batch.to(A__	)
														with torch.inference_mode():
																					lowerCAmelCase__	       =     model(**A__	)
														lowerCAmelCase__	       =     outputs.logits.argmax(dim=-1	)
														metric.add_batch(predictions=A__			,   references=batch['''labels''']	)
							lowerCAmelCase__	       =     metric.compute()

							# Then do distributed
							lowerCAmelCase__	       =     setup["""ddp"""]
							model.eval()
							for batch in dataloader:
														with torch.inference_mode():
																					lowerCAmelCase__	       =     model(**A__	)
														lowerCAmelCase__	       =     outputs.logits.argmax(dim=-1	)
														lowerCAmelCase__	       =     batch["""labels"""]
														lowerCAmelCase__	       =     accelerator.gather_for_metrics((preds, references)	)
														metric.add_batch(predictions=A__			,   references=A__	)
							lowerCAmelCase__	       =     metric.compute()

							for key in "accuracy f1".split():
														assert math.isclose(
														    baseline[key]			,   distributed[key]	), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""


def  _snake_case       (					)						->   int:
							lowerCAmelCase__	       =     Accelerator(split_batches=A__			,   dispatch_batches=A__	)
							if accelerator.is_local_main_process:
														datasets.utils.logging.set_verbosity_warning()
														transformers.utils.logging.set_verbosity_warning()
							else:
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							# These are a bit slower so they should only be ran on the GPU or TPU
							if torch.cuda.is_available() or is_tpu_available():
														if accelerator.is_local_main_process:
																					print('''**Testing gather_for_metrics**'''	)
														for split_batches in [True, False]:
																					for dispatch_batches in [True, False]:
																												if accelerator.is_local_main_process:
																																			print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""	)
																												test_mrpc(A__			,   A__	)
																												accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print('''**Test torch metrics**'''	)
							for split_batches in [True, False]:
														for dispatch_batches in [True, False]:
																					lowerCAmelCase__	       =     Accelerator(split_batches=A__			,   dispatch_batches=A__	)
																					if accelerator.is_local_main_process:
																												print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""	)
																					test_torch_metrics(A__			,   99	)
																					accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print('''**Test last batch is not dropped when perfectly divisible**'''	)
							lowerCAmelCase__	       =     Accelerator()
							test_torch_metrics(A__			,   512	)
							accelerator.state._reset_state()


def  _snake_case       (					A	)						->   Tuple:
							main()


if __name__ == "__main__":
		main()





'''simple docstring'''






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 		a__    (							a__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       List[Any]     						=				CLIPTokenizer
							lowercase__      :       List[str]     						=				CLIPTokenizerFast
							lowercase__      :       Dict     						=				True
							lowercase__      :       Any     						=				{}
							lowercase__      :       Optional[int]     						=				False







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														super().setUp()

														# fmt: off
														lowerCAmelCase__	       =     ['''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
														lowerCAmelCase__	       =     dict(zip(lowerCamelCase_ ,   range(len(lowerCamelCase_       )       )       )       )
														lowerCAmelCase__	       =     ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
														lowerCAmelCase__	       =     {'''unk_token''': '''<unk>'''}

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







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							Any:
														kwargs.update(self.special_tokens_map       )
														return CLIPTokenizer.from_pretrained(self.tmpdirname ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   **lowerCamelCase_       )	->							int:
														kwargs.update(self.special_tokens_map       )
														return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Any:
														lowerCAmelCase__	       =     '''lower newer'''
														lowerCAmelCase__	       =     '''lower newer'''
														return input_text, output_text







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     CLIPTokenizer(self.vocab_file ,   self.merges_file ,   **self.special_tokens_map       )
														lowerCAmelCase__	       =     '''lower newer'''
														lowerCAmelCase__	       =     ['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
														lowerCAmelCase__	       =     tokenizer.tokenize(lowerCamelCase_       )
														self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

														lowerCAmelCase__	       =     tokens + [tokenizer.unk_token]
														lowerCAmelCase__	       =     [10, 2, 16, 9, 3, 2, 16, 20]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_       ) ,   lowerCamelCase_       )







							@require_ftfy
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Tuple:
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
																												lowerCAmelCase__	       =     self.tokenizer_class.from_pretrained(lowerCamelCase_ ,   **lowerCamelCase_       )
																												lowerCAmelCase__	       =     self.rust_tokenizer_class.from_pretrained(lowerCamelCase_ ,   **lowerCamelCase_       )

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

																												self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

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

																												self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

																												# Test that the tokenization is identical on unicode of space type
																												lowerCAmelCase__	       =     [
																												    '''\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:
																																			lowerCAmelCase__	       =     tokenizer_s.tokenize(lowerCamelCase_       )
																																			lowerCAmelCase__	       =     tokenizer_r.tokenize(lowerCamelCase_       )

																																			self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )

																												# Test that the tokenization is identical on unicode of line break type
																												lowerCAmelCase__	       =     [
																												    '''\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:
																																			lowerCAmelCase__	       =     tokenizer_s.tokenize(lowerCamelCase_       )
																																			lowerCAmelCase__	       =     tokenizer_r.tokenize(lowerCamelCase_       )

																																			self.assertListEqual(lowerCamelCase_ ,   lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														# 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})"""       ):
																												lowerCAmelCase__	       =     '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
																												lowerCAmelCase__	       =     F"""{text_of_1_token} {text_of_1_token}"""

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

																												lowerCAmelCase__	       =     F""" {text}"""

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







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														# 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(lowerCamelCase_       ) 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   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														super().test_tokenization_python_rust_equals()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														# CLIP always lower cases letters
														pass