

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():
	lowerCAmelCase__		      =       """pt"""
elif is_tf_available():
	lowerCAmelCase__		      =       """tf"""
else:
	lowerCAmelCase__		      =       """jax"""

class     a__  (    snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				PerceiverTokenizer
			__lowerCamelCase       				=				False


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							super().setUp()
							A__						=	PerceiverTokenizer()
							tokenizer.save_pretrained(self.tmpdirname				)


			@cached_property
			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"				)


			def    UpperCamelCase						(			self					,					**lowercase				)     ->  PerceiverTokenizer:
							'''simple docstring'''






							return self.tokenizer_class.from_pretrained(self.tmpdirname					,					**lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase=False					,					lowercase=20					,					lowercase=5				)     ->  Tuple[str, list]:
							'''simple docstring'''






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

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

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


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							A__						=	self.perceiver_tokenizer
							A__						=	"Unicode €."
							A__						=	tokenizer(lowercase				)
							A__						=	[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
							self.assertEqual(encoded["input_ids"]					,					lowercase				)

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

							A__						=	tokenizer("e è é ê ë"				)
							A__						=	[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
							self.assertEqual(encoded["input_ids"]					,					lowercase				)
							# decoding
							A__						=	tokenizer.decode(lowercase				)
							self.assertEqual(lowercase					,					"[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    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	self.perceiver_tokenizer
							A__						=	["A long paragraph for summarization.", "Another paragraph for summarization."]
							# fmt: off
							A__						=	[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
							A__						=	tokenizer(lowercase					,					padding=lowercase					,					return_tensors=lowercase				)
							self.assertIsInstance(lowercase					,					lowercase				)

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

							self.assertListEqual(lowercase					,					lowercase				)

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


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	self.perceiver_tokenizer
							A__						=	["A long paragraph for summarization.", "Another paragraph for summarization."]
							A__						=	tokenizer(lowercase					,					padding=lowercase					,					return_tensors=lowercase				)
							# check if input_ids are returned and no decoder_input_ids
							self.assertIn("input_ids"					,					lowercase				)
							self.assertIn("attention_mask"					,					lowercase				)
							self.assertNotIn("decoder_input_ids"					,					lowercase				)
							self.assertNotIn("decoder_attention_mask"					,					lowercase				)


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							A__						=	self.perceiver_tokenizer
							A__						=	[
							    "Summary of the text.",
							    "Another summary.",
							]
							A__						=	tokenizer(
							    text_target=lowercase					,					max_length=32					,					padding="max_length"					,					truncation=lowercase					,					return_tensors=lowercase				)
							self.assertEqual(32					,					targets["input_ids"].shape[1]				)


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	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
							A__						=	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
															A__						=	tempfile.mkdtemp()

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

															A__						=	tokenizer.__class__.from_pretrained(lowercase				)
															A__						=	after_tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
															self.assertListEqual(lowercase					,					lowercase				)

															shutil.rmtree(lowercase				)

							A__						=	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
															A__						=	tempfile.mkdtemp()

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

															A__						=	tokenizer.__class__.from_pretrained(lowercase				)
															A__						=	after_tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
															self.assertListEqual(lowercase					,					lowercase				)
															self.assertIn("new_additional_special_token"					,					after_tokenizer.additional_special_tokens				)
															self.assertEqual(after_tokenizer.model_max_length					,					42				)

															A__						=	tokenizer.__class__.from_pretrained(lowercase					,					model_max_length=43				)
															self.assertEqual(tokenizer.model_max_length					,					43				)

															shutil.rmtree(lowercase				)


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	[]
							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(lowercase				)

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

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

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

															A__						=	added_tokens_extra_ids + [
															    "an_additional_special_token"
															]
															A__						=	added_tokens_extra_ids + [
															    "an_additional_special_token"
															]

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

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

															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    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							A__						=	self.perceiver_tokenizer
							self.assertEqual(tokenizer.decode([178]				)					,					"�"				)


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.get_tokenizers(fast=lowercase					,					do_lower_case=lowercase				)
							for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'				):
															A__						=	["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
															A__						=	tokenizer.convert_tokens_to_string(lowercase				)

															self.assertIsInstance(lowercase					,					lowercase				)



import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE)
class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    str           =			field(default="""automatic-speech-recognition"""  ,   metadata={"""include_in_asdict_even_if_is_default""": True})
      _UpperCAmelCase				:    ClassVar[Features]           =			Features({"""audio""": Audio()})
      _UpperCAmelCase				:    ClassVar[Features]           =			Features({"""transcription""": Value("""string""")})
      _UpperCAmelCase				:    str           =			"audio"
      _UpperCAmelCase				:    str           =			"transcription"
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       if self.audio_column not in features:
        raise ValueError(F'''Column {self.audio_column} is not present in features.'''      )
       if not isinstance(features[self.audio_column]    ,  __magic_name__      ):
        raise ValueError(F'''Column {self.audio_column} is not an Audio type.'''      )
       lowerCamelCase		:	Optional[Any]							=      copy.deepcopy(self      )
       lowerCamelCase		:	List[Any]							=      self.input_schema.copy()
       lowerCamelCase		:	Tuple							=      features[self.audio_column]
       lowerCamelCase		:	int							=      input_schema
       return task_template






      @property
      def 						UpperCamelCase__							(      self      ):
       return {self.audio_column: "audio", self.transcription_column: "transcription"}




