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_    :		List[Any]	       =	"""pt"""
elif is_tf_available():
	lowerCamelCase_    :		List[Any]	       =	"""tf"""
else:
	lowerCamelCase_    :		Tuple	       =	"""jax"""


class 						a__							(       __snake_case				,						unittest.TestCase		):
			A__    :       Dict        =      PerceiverTokenizer
			A__    :       Optional[int]        =      False



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



			@cached_property
			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'   )



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   PerceiverTokenizer:
								return self.tokenizer_class.from_pretrained(self.tmpdirname				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase=False				,						UpperCAmelCase=2_0				,						UpperCAmelCase=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.

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

								__a		 =       list(filter(lambda UpperCAmelCase   : re.match(R'^[ a-zA-Z]+$'				,						t[1]   )				,						UpperCAmelCase   )   )
								__a		 =       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:
													__a		 =       toks[:max_length]
								if min_length is not None and len(UpperCAmelCase   ) < min_length and len(UpperCAmelCase   ) > 0:
													while len(UpperCAmelCase   ) < 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(UpperCAmelCase				,						clean_up_tokenization_spaces=UpperCAmelCase   )
								if " " not in output_txt and len(UpperCAmelCase   ) > 1:
													__a		 =       (
													    tokenizer.decode([toks_ids[0]]				,						clean_up_tokenization_spaces=UpperCAmelCase   )
													    + ' '
													    + tokenizer.decode(toks_ids[1:]				,						clean_up_tokenization_spaces=UpperCAmelCase   )
													)
								if with_prefix_space:
													__a		 =       ' ' + output_txt
								__a		 =       tokenizer.encode(UpperCAmelCase				,						add_special_tokens=UpperCAmelCase   )
								return output_txt, output_ids



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       self.perceiver_tokenizer
								__a		 =       'Unicode €.'
								__a		 =       tokenizer(UpperCAmelCase   )
								__a		 =       [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
								__a		 =       tokenizer.decode(UpperCAmelCase   )
								self.assertEqual(UpperCAmelCase				,						'[CLS]Unicode €.[SEP]'   )

								__a		 =       tokenizer('e è é ê ë'   )
								__a		 =       [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
								__a		 =       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   )			->   Any:
								__a		 =       self.perceiver_tokenizer
								__a		 =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
								# fmt: off
								__a		 =       [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
								__a		 =       tokenizer(UpperCAmelCase				,						padding=UpperCAmelCase				,						return_tensors=UpperCAmelCase   )
								self.assertIsInstance(UpperCAmelCase				,						UpperCAmelCase   )

								if FRAMEWORK != "jax":
													__a		 =       list(batch.input_ids.numpy()[0]   )
								else:
													__a		 =       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   )			->   Tuple:
								__a		 =       self.perceiver_tokenizer
								__a		 =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
								__a		 =       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   )			->   Optional[int]:
								__a		 =       self.perceiver_tokenizer
								__a		 =       [
								    'Summary of the text.',
								    'Another summary.',
								]
								__a		 =       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   )			->   Union[str, Any]:
								# safety check on max_len default value so we are sure the test works
								__a		 =       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
								__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(UpperCAmelCase				,						add_special_tokens=UpperCAmelCase   )
																		tokenizer.save_pretrained(UpperCAmelCase   )

																		__a		 =       tokenizer.__class__.from_pretrained(UpperCAmelCase   )
																		__a		 =       after_tokenizer.encode(UpperCAmelCase				,						add_special_tokens=UpperCAmelCase   )
																		self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )

																		shutil.rmtree(UpperCAmelCase   )

								__a		 =       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
																		__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(UpperCAmelCase				,						add_special_tokens=UpperCAmelCase   )
																		tokenizer.save_pretrained(UpperCAmelCase   )

																		__a		 =       tokenizer.__class__.from_pretrained(UpperCAmelCase   )
																		__a		 =       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   )

																		__a		 =       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   )			->   List[str]:
								__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(UpperCAmelCase   )

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

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

																		__a		 =       [f'''<extra_id_{i}>''' for i in range(1_2_5   )]

																		__a		 =       added_tokens_extra_ids + [
																		    'an_additional_special_token'
																		]
																		__a		 =       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
																		__a		 =       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
																		__a		 =       added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'				,						lstrip=UpperCAmelCase   )]
																		__a		 =       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   )			->   Optional[int]:
								__a		 =       self.perceiver_tokenizer
								self.assertEqual(tokenizer.decode([1_7_8]   )				,						'�'   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								pass



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



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								pass



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







			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
								# strings and special added tokens as tokens
								__a		 =       self.get_tokenizers(fast=UpperCAmelCase				,						do_lower_case=UpperCAmelCase   )
								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(UpperCAmelCase   )

																		self.assertIsInstance(UpperCAmelCase				,						UpperCAmelCase   )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowerCamelCase_    :		Dict							=	{"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[Any]							=	["""SpeechEncoderDecoderModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




