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():
	lowercase__			:				List[Any]		=	'''pt'''
elif is_tf_available():
	lowercase__			:				Dict		=	'''tf'''
else:
	lowercase__			:				Optional[int]		=	'''jax'''




class    lowercase_  (  UpperCamelCase_      ,     unittest.TestCase						):



	"""simple docstring"""





	UpperCAmelCase_  :					Optional[int]   =				PerceiverTokenizer
	UpperCAmelCase_  :					Optional[Any]   =				False


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Tuple:
								super().setUp()
								lowerCAmelCase			=		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		,			**__SCREAMING_SNAKE_CASE				)						->PerceiverTokenizer:
								return self.tokenizer_class.from_pretrained(self.tmpdirname		,			**__SCREAMING_SNAKE_CASE				)


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE=False		,			__SCREAMING_SNAKE_CASE=20		,			__SCREAMING_SNAKE_CASE=5				)						->Tuple[str, list]:
								lowerCAmelCase			=		[]
								for i in range(len(__SCREAMING_SNAKE_CASE				)				):
															try:
																						lowerCAmelCase			=		tokenizer.decode([i]		,			clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE				)
															except UnicodeDecodeError:
																						pass
															toks.append((i, tok)				)

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

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


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

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

								lowerCAmelCase			=		tokenizer('''e è é ê ë'''				)
								lowerCAmelCase			=		[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
								self.assertEqual(encoded['''input_ids''']		,			__SCREAMING_SNAKE_CASE				)
								# decoding
								lowerCAmelCase			=		tokenizer.decode(__SCREAMING_SNAKE_CASE				)
								self.assertEqual(__SCREAMING_SNAKE_CASE		,			'''[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				)						->Union[str, Any]:
								lowerCAmelCase			=		self.perceiver_tokenizer
								lowerCAmelCase			=		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								# fmt: off
								lowerCAmelCase			=		[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
								lowerCAmelCase			=		tokenizer(__SCREAMING_SNAKE_CASE		,			padding=__SCREAMING_SNAKE_CASE		,			return_tensors=__SCREAMING_SNAKE_CASE				)
								self.assertIsInstance(__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				)

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

								self.assertListEqual(__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				)

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


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


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Tuple:
								lowerCAmelCase			=		self.perceiver_tokenizer
								lowerCAmelCase			=		[
								    '''Summary of the text.''',
								    '''Another summary.''',
								]
								lowerCAmelCase			=		tokenizer(
								    text_target=__SCREAMING_SNAKE_CASE		,			max_length=32		,			padding='''max_length'''		,			truncation=__SCREAMING_SNAKE_CASE		,			return_tensors=__SCREAMING_SNAKE_CASE				)
								self.assertEqual(32		,			targets['''input_ids'''].shape[1]				)


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Union[str, Any]:
								lowerCAmelCase			=		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
								lowerCAmelCase			=		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
																						lowerCAmelCase			=		tempfile.mkdtemp()

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

																						lowerCAmelCase			=		tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE				)
																						lowerCAmelCase			=		after_tokenizer.encode(__SCREAMING_SNAKE_CASE		,			add_special_tokens=__SCREAMING_SNAKE_CASE				)
																						self.assertListEqual(__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				)

																						shutil.rmtree(__SCREAMING_SNAKE_CASE				)

								lowerCAmelCase			=		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
																						lowerCAmelCase			=		tempfile.mkdtemp()

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

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

																						lowerCAmelCase			=		tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE		,			model_max_length=43				)
																						self.assertEqual(tokenizer.model_max_length		,			43				)

																						shutil.rmtree(__SCREAMING_SNAKE_CASE				)


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->int:
								lowerCAmelCase			=		[]
								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(__SCREAMING_SNAKE_CASE				)

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

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

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

																						lowerCAmelCase			=		added_tokens_extra_ids + [
																						    '''an_additional_special_token'''
																						]
																						lowerCAmelCase			=		added_tokens_extra_ids + [
																						    '''an_additional_special_token'''
																						]

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

																						# 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
																						lowerCAmelCase			=		tokenizer_class.from_pretrained(
																						    __SCREAMING_SNAKE_CASE		,			)
																						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
																						lowerCAmelCase			=		added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''		,			lstrip=__SCREAMING_SNAKE_CASE				)]
																						lowerCAmelCase			=		tokenizer_class.from_pretrained(
																						    __SCREAMING_SNAKE_CASE		,			additional_special_tokens=__SCREAMING_SNAKE_CASE		,			)

																						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				)						->Union[str, Any]:
								lowerCAmelCase			=		self.perceiver_tokenizer
								self.assertEqual(tokenizer.decode([178]				)		,			'''�'''				)


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


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


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Tuple:
								pass


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




	def 			SCREAMING_SNAKE_CASE_				(							self				)						->List[Any]:
								lowerCAmelCase			=		self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE		,			do_lower_case=__SCREAMING_SNAKE_CASE				)
								for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"				):
																						lowerCAmelCase			=		['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
																						lowerCAmelCase			=		tokenizer.convert_tokens_to_string(__SCREAMING_SNAKE_CASE				)

																						self.assertIsInstance(__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				)








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       'glpn'



 def __init__(self ,   A=3 ,   A=4 ,   A=[2, 2, 2, 2] ,   A=[8, 4, 2, 1] ,   A=[32, 64, 160, 256] ,   A=[7, 3, 3, 3] ,   A=[4, 2, 2, 2] ,   A=[1, 2, 5, 8] ,   A=[4, 4, 4, 4] ,   A="gelu" ,   A=0.0 ,   A=0.0 ,   A=0.02 ,   A=0.1 ,   A=1E-6 ,   A=64 ,   A=10 ,   A=-1 ,   **A ,   )    ->     Any:


      """simple docstring"""



      super().__init__(**A       )

      _a      =  num_channels
      _a      =  num_encoder_blocks
      _a      =  depths
      _a      =  sr_ratios
      _a      =  hidden_sizes
      _a      =  patch_sizes
      _a      =  strides
      _a      =  mlp_ratios
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  initializer_range
      _a      =  drop_path_rate
      _a      =  layer_norm_eps
      _a      =  decoder_hidden_size
      _a      =  max_depth
      _a      =  head_in_index



