




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






class lowerCAmelCase__	(     lowercase,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			PerceiverTokenizer
			lowerCamelCase__      =			False
			def 					A_    (  self  ):
							super().setUp()
							_lowerCamelCase     :			Optional[Any]    =    PerceiverTokenizer()
							tokenizer.save_pretrained(self.tmpdirname  )
			@cached_property
			def 					A_    (  self  ):
							return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'  )
			def 					A_    (  self  ,      **lowercase  ):
							return self.tokenizer_class.from_pretrained(self.tmpdirname  ,      **lowercase  )
			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ,      lowercase=20  ,      lowercase=5  ):
							# 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.

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

							_lowerCamelCase     :			Optional[int]    =    list(filter(lambda lowercase  : re.match(r'^[ a-zA-Z]+$'  ,      t[1]  )  ,      lowercase  )  )
							_lowerCamelCase     :			Any    =    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:
											_lowerCamelCase     :			List[str]    =    toks[:max_length]
							if min_length is not None and len(lowercase  ) < min_length and len(lowercase  ) > 0:
											while len(lowercase  ) < min_length:
															_lowerCamelCase     :			int    =    toks + toks
        # toks_str = [t[1] for t in toks]
							_lowerCamelCase     :			str    =    [t[0] for t in toks]

							# Ensure consistency
							_lowerCamelCase     :			Dict    =    tokenizer.decode(lowercase  ,      clean_up_tokenization_spaces=lowercase  )
							if " " not in output_txt and len(lowercase  ) > 1:
											_lowerCamelCase     :			str    =    (
											    tokenizer.decode([toks_ids[0]]  ,      clean_up_tokenization_spaces=lowercase  )
											    + ' '
											    + tokenizer.decode(toks_ids[1:]  ,      clean_up_tokenization_spaces=lowercase  )
											)
							if with_prefix_space:
											_lowerCamelCase     :			Optional[Any]    =    ' ' + output_txt
							_lowerCamelCase     :			Tuple    =    tokenizer.encode(lowercase  ,      add_special_tokens=lowercase  )
							return output_txt, output_ids
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    self.perceiver_tokenizer
							_lowerCamelCase     :			Dict    =    'Unicode €.'
							_lowerCamelCase     :			int    =    tokenizer(lowercase  )
							_lowerCamelCase     :			Tuple    =    [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
							self.assertEqual(encoded['input_ids']  ,      lowercase  )

							# decoding
							_lowerCamelCase     :			Optional[int]    =    tokenizer.decode(lowercase  )
							self.assertEqual(lowercase  ,      '[CLS]Unicode €.[SEP]'  )

							_lowerCamelCase     :			Union[str, Any]    =    tokenizer('e è é ê ë'  )
							_lowerCamelCase     :			Tuple    =    [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
							self.assertEqual(encoded['input_ids']  ,      lowercase  )
							# decoding
							_lowerCamelCase     :			int    =    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 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    self.perceiver_tokenizer
							_lowerCamelCase     :			Optional[Any]    =    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							# fmt: off
							_lowerCamelCase     :			List[Any]    =    [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     :			Dict    =    tokenizer(lowercase  ,      padding=lowercase  ,      return_tensors=lowercase  )
							self.assertIsInstance(lowercase  ,      lowercase  )

							if FRAMEWORK != "jax":
											_lowerCamelCase     :			Optional[Any]    =    list(batch.input_ids.numpy()[0]  )
							else:
											_lowerCamelCase     :			Union[str, Any]    =    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 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    self.perceiver_tokenizer
							_lowerCamelCase     :			List[str]    =    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							_lowerCamelCase     :			List[str]    =    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 					A_    (  self  ):
							_lowerCamelCase     :			str    =    self.perceiver_tokenizer
							_lowerCamelCase     :			Optional[int]    =    [
							    'Summary of the text.',
							    'Another summary.',
							]
							_lowerCamelCase     :			Optional[int]    =    tokenizer(
							    text_target=lowercase  ,      max_length=32  ,      padding='max_length'  ,      truncation=lowercase  ,      return_tensors=lowercase  )
							self.assertEqual(32  ,      targets['input_ids'].shape[1]  )
			def 					A_    (  self  ):
							# safety check on max_len default value so we are sure the test works
							_lowerCamelCase     :			Tuple    =    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     :			List[Any]    =    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     :			Optional[Any]    =    tempfile.mkdtemp()

															_lowerCamelCase     :			List[str]    =    ' He is very happy, UNwant\u00E9d,running'
															_lowerCamelCase     :			List[Any]    =    tokenizer.encode(lowercase  ,      add_special_tokens=lowercase  )
															tokenizer.save_pretrained(lowercase  )

															_lowerCamelCase     :			Any    =    tokenizer.__class__.from_pretrained(lowercase  )
															_lowerCamelCase     :			Optional[int]    =    after_tokenizer.encode(lowercase  ,      add_special_tokens=lowercase  )
															self.assertListEqual(lowercase  ,      lowercase  )

															shutil.rmtree(lowercase  )

							_lowerCamelCase     :			Optional[Any]    =    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     :			Union[str, Any]    =    tempfile.mkdtemp()

															_lowerCamelCase     :			Union[str, Any]    =    ' He is very happy, UNwant\u00E9d,running'
															tokenizer.add_tokens(['bim', 'bambam']  )
															_lowerCamelCase     :			Dict    =    tokenizer.additional_special_tokens
															additional_special_tokens.append('new_additional_special_token'  )
															tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}  )
															_lowerCamelCase     :			Optional[int]    =    tokenizer.encode(lowercase  ,      add_special_tokens=lowercase  )
															tokenizer.save_pretrained(lowercase  )

															_lowerCamelCase     :			str    =    tokenizer.__class__.from_pretrained(lowercase  )
															_lowerCamelCase     :			Any    =    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  )

															_lowerCamelCase     :			List[Any]    =    tokenizer.__class__.from_pretrained(lowercase  ,      model_max_length=43  )
															self.assertEqual(tokenizer.model_max_length  ,      43  )

															shutil.rmtree(lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    []
							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:
																			_lowerCamelCase     :			Tuple    =    json.load(lowercase  )

															with open(os.path.join(lowercase  ,      'tokenizer_config.json'  )  ,      encoding='utf-8'  ) as json_file:
																			_lowerCamelCase     :			List[str]    =    json.load(lowercase  )

															_lowerCamelCase     :			Optional[int]    =    [F'''<extra_id_{i}>''' for i in range(125  )]

															_lowerCamelCase     :			List[Any]    =    added_tokens_extra_ids + [
															    'an_additional_special_token'
															]
															_lowerCamelCase     :			Tuple    =    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
															_lowerCamelCase     :			Optional[int]    =    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
															_lowerCamelCase     :			Any    =    added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'  ,      lstrip=lowercase  )]
															_lowerCamelCase     :			Any    =    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 					A_    (  self  ):
							_lowerCamelCase     :			Optional[int]    =    self.perceiver_tokenizer
							self.assertEqual(tokenizer.decode([178]  )  ,      '�'  )
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ):
							pass






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

															self.assertIsInstance(lowercase  ,      lowercase  )





"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

							# uncondition for scaling
							_lowerCamelCase     :			str    =    nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Tuple    =    (config.num_hidden_layers + 1) // 5
							_lowerCamelCase     :			int    =    config.hidden_size
							_lowerCamelCase     :			Optional[Any]    =    1
							_lowerCamelCase     :			str    =    nn.ModuleList(
							    [
							        BasicTransformerBlock(lowercase  ,      lowercase  ,      lowercase  ,      activation_fn='gelu'  ,      attention_bias=lowercase  )
							        for _ in range(lowercase  )
							    ]  )






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states