"""simple docstring"""


import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    __UpperCamelCase									=			'''pt'''
elif is_tf_available():
    __UpperCamelCase									=			'''tf'''
else:
    __UpperCamelCase									=			'''jax'''




class  UpperCamelCase					(				lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      PerceiverTokenizer
       SCREAMING_SNAKE_CASE_			=      False







       def 		a_						( self)      ->      Union[str, Any]:
            super().setUp()
            snake_case_					       =					PerceiverTokenizer()
            tokenizer.save_pretrained(self.tmpdirname)







       @cached_property
       def 		a_						( self)      ->      Union[str, Any]:
            return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver')







       def 		a_						( self,							**lowerCAmelCase__)      ->      PerceiverTokenizer:
            return self.tokenizer_class.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__=False,							lowerCAmelCase__=20,							lowerCAmelCase__=5)      ->      Tuple[str, list]:
            # XXX The default common tokenizer tests assume that every ID is decodable on its own.
            # This assumption is invalid for Perceiver because single bytes might not be
            # valid utf-8 (byte 128 for instance).
            # Here we're overriding the smallest possible method to provide
            # a clean sequence without making the same assumption.

            snake_case_					       =					[]
            for i in range(len(lowerCAmelCase__)):
                 try:
                      snake_case_					       =					tokenizer.decode([i],							clean_up_tokenization_spaces=lowerCAmelCase__)
                 except UnicodeDecodeError:
                      pass
                 toks.append((i, tok))

            snake_case_					       =					list(filter(lambda lowerCAmelCase__: re.match(R'^[ a-zA-Z]+$',							t[1]),							lowerCAmelCase__))
            snake_case_					       =					list(filter(lambda lowerCAmelCase__: [t[0]] == tokenizer.encode(t[1],							add_special_tokens=lowerCAmelCase__),							lowerCAmelCase__))
            if max_length is not None and len(lowerCAmelCase__) > max_length:
                 snake_case_					       =					toks[:max_length]
            if min_length is not None and len(lowerCAmelCase__) < min_length and len(lowerCAmelCase__) > 0:
                 while len(lowerCAmelCase__) < min_length:
                      snake_case_					       =					toks + toks
        # toks_str = [t[1] for t in toks]
            snake_case_					       =					[t[0] for t in toks]

            # Ensure consistency
            snake_case_					       =					tokenizer.decode(lowerCAmelCase__,							clean_up_tokenization_spaces=lowerCAmelCase__)
            if " " not in output_txt and len(lowerCAmelCase__) > 1:
                 snake_case_					       =					(
                     tokenizer.decode([toks_ids[0]],							clean_up_tokenization_spaces=lowerCAmelCase__)
                     + ' '
                     + tokenizer.decode(toks_ids[1:],							clean_up_tokenization_spaces=lowerCAmelCase__)
                 )
            if with_prefix_space:
                 snake_case_					       =					' ' + output_txt
            snake_case_					       =					tokenizer.encode(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__)
            return output_txt, output_ids







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					self.perceiver_tokenizer
            snake_case_					       =					'Unicode €.'
            snake_case_					       =					tokenizer(lowerCAmelCase__)
            snake_case_					       =					[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
            self.assertEqual(encoded['input_ids'],							lowerCAmelCase__)

            # decoding
            snake_case_					       =					tokenizer.decode(lowerCAmelCase__)
            self.assertEqual(lowerCAmelCase__,							'[CLS]Unicode €.[SEP]')

            snake_case_					       =					tokenizer('e è é ê ë')
            snake_case_					       =					[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
            self.assertEqual(encoded['input_ids'],							lowerCAmelCase__)
            # decoding
            snake_case_					       =					tokenizer.decode(lowerCAmelCase__)
            self.assertEqual(lowerCAmelCase__,							'[CLS]e è é ê ë[SEP]')

            # encode/decode, but with `encode` instead of `__call__`
            self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë')),							'[CLS]e è é ê ë[SEP]')







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					self.perceiver_tokenizer
            snake_case_					       =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
            # fmt: off
            snake_case_					       =					[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
            # fmt: on
            snake_case_					       =					tokenizer(lowerCAmelCase__,							padding=lowerCAmelCase__,							return_tensors=lowerCAmelCase__)
            self.assertIsInstance(lowerCAmelCase__,							lowerCAmelCase__)

            if FRAMEWORK != "jax":
                 snake_case_					       =					list(batch.input_ids.numpy()[0])
            else:
                 snake_case_					       =					list(batch.input_ids.tolist()[0])

            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)

            self.assertEqual((2, 38),							batch.input_ids.shape)
            self.assertEqual((2, 38),							batch.attention_mask.shape)







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					self.perceiver_tokenizer
            snake_case_					       =					['A long paragraph for summarization.', 'Another paragraph for summarization.']
            snake_case_					       =					tokenizer(lowerCAmelCase__,							padding=lowerCAmelCase__,							return_tensors=lowerCAmelCase__)
            # check if input_ids are returned and no decoder_input_ids
            self.assertIn('input_ids',							lowerCAmelCase__)
            self.assertIn('attention_mask',							lowerCAmelCase__)
            self.assertNotIn('decoder_input_ids',							lowerCAmelCase__)
            self.assertNotIn('decoder_attention_mask',							lowerCAmelCase__)







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					self.perceiver_tokenizer
            snake_case_					       =					[
                'Summary of the text.',
                'Another summary.',
            ]
            snake_case_					       =					tokenizer(
                text_target=lowerCAmelCase__,							max_length=32,							padding='max_length',							truncation=lowerCAmelCase__,							return_tensors=lowerCAmelCase__)
            self.assertEqual(32,							targets['input_ids'].shape[1])







       def 		a_						( self)      ->      Optional[int]:
            # safety check on max_len default value so we are sure the test works
            snake_case_					       =					self.get_tokenizers()
            for tokenizer in tokenizers:
                 with self.subTest(f'{tokenizer.__class__.__name__}'):
                      self.assertNotEqual(tokenizer.model_max_length,							42)

        # Now let's start the test
            snake_case_					       =					self.get_tokenizers()
            for tokenizer in tokenizers:
                 with self.subTest(f'{tokenizer.__class__.__name__}'):
                      # Isolate this from the other tests because we save additional tokens/etc
                      snake_case_					       =					tempfile.mkdtemp()

                      snake_case_					       =					' He is very happy, UNwant\u00E9d,running'
                      snake_case_					       =					tokenizer.encode(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__)
                      tokenizer.save_pretrained(lowerCAmelCase__)

                      snake_case_					       =					tokenizer.__class__.from_pretrained(lowerCAmelCase__)
                      snake_case_					       =					after_tokenizer.encode(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__)
                      self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)

                      shutil.rmtree(lowerCAmelCase__)

            snake_case_					       =					self.get_tokenizers(model_max_length=42)
            for tokenizer in tokenizers:
                 with self.subTest(f'{tokenizer.__class__.__name__}'):
                      # Isolate this from the other tests because we save additional tokens/etc
                      snake_case_					       =					tempfile.mkdtemp()

                      snake_case_					       =					' He is very happy, UNwant\u00E9d,running'
                      tokenizer.add_tokens(['bim', 'bambam'])
                      snake_case_					       =					tokenizer.additional_special_tokens
                      additional_special_tokens.append('new_additional_special_token')
                      tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens})
                      snake_case_					       =					tokenizer.encode(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__)
                      tokenizer.save_pretrained(lowerCAmelCase__)

                      snake_case_					       =					tokenizer.__class__.from_pretrained(lowerCAmelCase__)
                      snake_case_					       =					after_tokenizer.encode(lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__)
                      self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)
                      self.assertIn('new_additional_special_token',							after_tokenizer.additional_special_tokens)
                      self.assertEqual(after_tokenizer.model_max_length,							42)

                      snake_case_					       =					tokenizer.__class__.from_pretrained(lowerCAmelCase__,							model_max_length=43)
                      self.assertEqual(tokenizer.model_max_length,							43)

                      shutil.rmtree(lowerCAmelCase__)







       def 		a_						( self)      ->      Any:
            snake_case_					       =					[]
            if self.test_slow_tokenizer:
                 tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

            if self.test_rust_tokenizer:
                 tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

            for tokenizer_class, tokenizer_utils in tokenizer_list:
                 with tempfile.TemporaryDirectory() as tmp_dir:
                      tokenizer_utils.save_pretrained(lowerCAmelCase__)

                      with open(os.path.join(lowerCAmelCase__,							'special_tokens_map.json'),							encoding='utf-8') as json_file:
                           snake_case_					       =					json.load(lowerCAmelCase__)

                      with open(os.path.join(lowerCAmelCase__,							'tokenizer_config.json'),							encoding='utf-8') as json_file:
                           snake_case_					       =					json.load(lowerCAmelCase__)

                      snake_case_					       =					[f'<extra_id_{i}>' for i in range(125)]

                      snake_case_					       =					added_tokens_extra_ids + [
                          'an_additional_special_token'
                      ]
                      snake_case_					       =					added_tokens_extra_ids + [
                          'an_additional_special_token'
                      ]

                      with open(os.path.join(lowerCAmelCase__,							'special_tokens_map.json'),							'w',							encoding='utf-8') as outfile:
                           json.dump(lowerCAmelCase__,							lowerCAmelCase__)
                      with open(os.path.join(lowerCAmelCase__,							'tokenizer_config.json'),							'w',							encoding='utf-8') as outfile:
                           json.dump(lowerCAmelCase__,							lowerCAmelCase__)

                      # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                      # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                      # "special_tokens_map.json" files
                      snake_case_					       =					tokenizer_class.from_pretrained(
                          lowerCAmelCase__,							)
                      self.assertIn(
                          'an_additional_special_token',							tokenizer_without_change_in_init.additional_special_tokens)
                      self.assertEqual(
                          ['an_additional_special_token'],							tokenizer_without_change_in_init.convert_ids_to_tokens(
                              tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'])),							)

                      # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                      snake_case_					       =					added_tokens_extra_ids + [AddedToken('a_new_additional_special_token',							lstrip=lowerCAmelCase__)]
                      snake_case_					       =					tokenizer_class.from_pretrained(
                          lowerCAmelCase__,							additional_special_tokens=lowerCAmelCase__,							)

                      self.assertIn('a_new_additional_special_token',							tokenizer.additional_special_tokens)
                      self.assertEqual(
                          ['a_new_additional_special_token'],							tokenizer.convert_ids_to_tokens(
                              tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'])),							)







       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					self.perceiver_tokenizer
            self.assertEqual(tokenizer.decode([178]),							'�')







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







       def 		a_						( self)      ->      List[str]:
            pass







       def 		a_						( self)      ->      Union[str, Any]:
            pass







       def 		a_						( self)      ->      List[Any]:
            pass







       def 		a_						( self)      ->      Optional[Any]:
            # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
            # strings and special added tokens as tokens
            snake_case_					       =					self.get_tokenizers(fast=lowerCAmelCase__,							do_lower_case=lowerCAmelCase__)
            for tokenizer in tokenizers:
                 with self.subTest(f'{tokenizer.__class__.__name__}'):
                      snake_case_					       =					['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
                      snake_case_					       =					tokenizer.convert_tokens_to_string(lowerCAmelCase__)

                      self.assertIsInstance(lowerCAmelCase__,							lowerCAmelCase__)





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
    from sagemaker import Session, TrainingJobAnalytics
    from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"     ,    "False")) is not True     ,    reason="Skipping test because should only be run when releasing minor transformers version"     ,    )
@pytest.mark.usefixtures("sm_env")
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 650, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "pytorch",
            "script": "run_ddp.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.7, "eval_loss": 0.6},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf_dist.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.p3.16xlarge",
            "results": {"train_runtime": 600, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ])
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				str:
            if self.framework == "pytorch":
                   subprocess.run(
                       F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split() ,						encoding="utf-8" ,						check=__lowerCamelCase ,						)
            assert hasattr(self ,						"env")


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Tuple:
            _A      :       Dict				    = F"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
            # distributed data settings
            _A      :       Optional[Any]				    = {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

            # creates estimator
            return HuggingFace(
                entry_point=self.script ,						source_dir=self.env.test_path ,						role=self.env.role ,						image_uri=self.env.image_uri ,						base_job_name=__lowerCamelCase ,						instance_count=__lowerCamelCase ,						instance_type=self.instance_type ,						debugger_hook_config=__lowerCamelCase ,						hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path} ,						metric_definitions=self.env.metric_definitions ,						distribution=__lowerCamelCase ,						py_version="py36" ,						)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Optional[Any]:
            TrainingJobAnalytics(__lowerCamelCase).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv")


     @parameterized.expand([(2,)])
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Any:
            # create estimator
            _A      :       Union[str, Any]				    = self.create_estimator(__lowerCamelCase)

            # run training
            estimator.fit()

            # result dataframe
            _A      :       Optional[Any]				    = TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

            # extract kpis
            _A      :       List[Any]				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"])
            _A      :       Dict				    = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"])
            # get train time from SageMaker job, this includes starting, preprocessing, stopping
            _A      :       Optional[Any]				    = (
                Session().describe_training_job(estimator.latest_training_job.name).get("TrainingTimeInSeconds" ,						9_9_9_9_9_9)
            )

            # assert kpis
            assert train_runtime <= self.results["train_runtime"]
            assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy)
            assert all(t <= self.results["eval_loss"] for t in eval_loss)

            # dump tests result into json file to share in PR
            with open(F"{estimator.latest_training_job.name}.json" ,						"w") as outfile:
                   json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss} ,						__lowerCamelCase)
