

"""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():
						A_				:      Tuple           =			"pt"
elif is_tf_available():
						A_				:      Optional[int]           =			"tf"
else:
						A_				:      Tuple           =			"jax"





class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Union[str, Any]       		=				PerceiverTokenizer
			lowerCamelCase__ :		List[Any]       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Tuple:
						super().setUp()
						SCREAMING_SNAKE_CASE__										=   PerceiverTokenizer()
						tokenizer.save_pretrained(self.tmpdirname							)

			@cached_property
			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Tuple:
						return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					**__UpperCAmelCase					:    Union[str, Any]							) ->     PerceiverTokenizer:
						return self.tokenizer_class.from_pretrained(self.tmpdirname     ,					**__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Tuple=False     ,					__UpperCAmelCase					:    List[Any]=2_0     ,					__UpperCAmelCase					:    Any=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.

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

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

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

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   self.perceiver_tokenizer
						SCREAMING_SNAKE_CASE__										=   """Unicode €."""
						SCREAMING_SNAKE_CASE__										=   tokenizer(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
						self.assertEqual(encoded["""input_ids"""]     ,					__UpperCAmelCase							)

						# decoding
						SCREAMING_SNAKE_CASE__										=   tokenizer.decode(__UpperCAmelCase							)
						self.assertEqual(__UpperCAmelCase     ,					"""[CLS]Unicode €.[SEP]"""							)

						SCREAMING_SNAKE_CASE__										=   tokenizer("""e è é ê ë"""							)
						SCREAMING_SNAKE_CASE__										=   [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
						self.assertEqual(encoded["""input_ids"""]     ,					__UpperCAmelCase							)
						# decoding
						SCREAMING_SNAKE_CASE__										=   tokenizer.decode(__UpperCAmelCase							)
						self.assertEqual(__UpperCAmelCase     ,					"""[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        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   self.perceiver_tokenizer
						SCREAMING_SNAKE_CASE__										=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
						# fmt: off
						SCREAMING_SNAKE_CASE__										=   [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
						# fmt: on
						SCREAMING_SNAKE_CASE__										=   tokenizer(__UpperCAmelCase     ,					padding=__UpperCAmelCase     ,					return_tensors=__UpperCAmelCase							)
						self.assertIsInstance(__UpperCAmelCase     ,					__UpperCAmelCase							)

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

						self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)

						self.assertEqual((2, 3_8)     ,					batch.input_ids.shape							)
						self.assertEqual((2, 3_8)     ,					batch.attention_mask.shape							)

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

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.perceiver_tokenizer
						SCREAMING_SNAKE_CASE__										=   [
						    """Summary of the text.""",
						    """Another summary.""",
						]
						SCREAMING_SNAKE_CASE__										=   tokenizer(
						    text_target=__UpperCAmelCase     ,					max_length=3_2     ,					padding="""max_length"""     ,					truncation=__UpperCAmelCase     ,					return_tensors=__UpperCAmelCase							)
						self.assertEqual(3_2     ,					targets["""input_ids"""].shape[1]							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Union[str, Any]:
						# safety check on max_len default value so we are sure the test works
						SCREAMING_SNAKE_CASE__										=   self.get_tokenizers()
						for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""							):
												self.assertNotEqual(tokenizer.model_max_length     ,					4_2							)

        # Now let's start the test
						SCREAMING_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
												SCREAMING_SNAKE_CASE__										=   tempfile.mkdtemp()

												SCREAMING_SNAKE_CASE__										=   """ He is very happy, UNwant\u00E9d,running"""
												SCREAMING_SNAKE_CASE__										=   tokenizer.encode(__UpperCAmelCase     ,					add_special_tokens=__UpperCAmelCase							)
												tokenizer.save_pretrained(__UpperCAmelCase							)

												SCREAMING_SNAKE_CASE__										=   tokenizer.__class__.from_pretrained(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   after_tokenizer.encode(__UpperCAmelCase     ,					add_special_tokens=__UpperCAmelCase							)
												self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)

												shutil.rmtree(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   self.get_tokenizers(model_max_length=4_2							)
						for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""							):
												# Isolate this from the other tests because we save additional tokens/etc
												SCREAMING_SNAKE_CASE__										=   tempfile.mkdtemp()

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

												SCREAMING_SNAKE_CASE__										=   tokenizer.__class__.from_pretrained(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   after_tokenizer.encode(__UpperCAmelCase     ,					add_special_tokens=__UpperCAmelCase							)
												self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)
												self.assertIn("""new_additional_special_token"""     ,					after_tokenizer.additional_special_tokens							)
												self.assertEqual(after_tokenizer.model_max_length     ,					4_2							)

												SCREAMING_SNAKE_CASE__										=   tokenizer.__class__.from_pretrained(__UpperCAmelCase     ,					model_max_length=4_3							)
												self.assertEqual(tokenizer.model_max_length     ,					4_3							)

												shutil.rmtree(__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     List[Any]:
						SCREAMING_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(__UpperCAmelCase							)

												with open(os.path.join(__UpperCAmelCase     ,					"""special_tokens_map.json"""							)     ,					encoding="""utf-8"""							) as json_file:
															SCREAMING_SNAKE_CASE__										=   json.load(__UpperCAmelCase							)

												with open(os.path.join(__UpperCAmelCase     ,					"""tokenizer_config.json"""							)     ,					encoding="""utf-8"""							) as json_file:
															SCREAMING_SNAKE_CASE__										=   json.load(__UpperCAmelCase							)

												SCREAMING_SNAKE_CASE__										=   [F"""<extra_id_{i}>""" for i in range(1_2_5							)]

												SCREAMING_SNAKE_CASE__										=   added_tokens_extra_ids + [
												    """an_additional_special_token"""
												]
												SCREAMING_SNAKE_CASE__										=   added_tokens_extra_ids + [
												    """an_additional_special_token"""
												]

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

												# 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
												SCREAMING_SNAKE_CASE__										=   tokenizer_class.from_pretrained(
												    __UpperCAmelCase     ,					)
												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
												SCREAMING_SNAKE_CASE__										=   added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""     ,					lstrip=__UpperCAmelCase							)]
												SCREAMING_SNAKE_CASE__										=   tokenizer_class.from_pretrained(
												    __UpperCAmelCase     ,					additional_special_tokens=__UpperCAmelCase     ,					)

												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        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.perceiver_tokenizer
						self.assertEqual(tokenizer.decode([1_7_8]							)     ,					"""�"""							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[int]:
						pass

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

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Tuple:
						pass

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Tuple:
						pass





			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Optional[int]:
						# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
						# strings and special added tokens as tokens
						SCREAMING_SNAKE_CASE__										=   self.get_tokenizers(fast=__UpperCAmelCase     ,					do_lower_case=__UpperCAmelCase							)
						for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""							):
												SCREAMING_SNAKE_CASE__										=   ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
												SCREAMING_SNAKE_CASE__										=   tokenizer.convert_tokens_to_string(__UpperCAmelCase							)

												self.assertIsInstance(__UpperCAmelCase     ,					__UpperCAmelCase							)





"""simple docstring"""




A_				:      Any           =			"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
						from .utils import rich


