


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



class     _A   (    lowercase__	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Union[str, Any]						=PerceiverTokenizer
  SCREAMING_SNAKE_CASE_	:	List[str]						=False






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





       '''simple docstring'''





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






  @cached_property
  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





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






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





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False   ,				SCREAMING_SNAKE_CASE_=20   ,				SCREAMING_SNAKE_CASE_=5	)		->      int:





       '''simple docstring'''





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

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

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






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





       '''simple docstring'''





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

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

       UpperCamelCase__										=      tokenizer('''e è é ê ë'''	)
       UpperCamelCase__										=      [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
       self.assertEqual(encoded['''input_ids''']   ,				__lowerCamelCase	)
       # decoding
       UpperCamelCase__										=      tokenizer.decode(__lowerCamelCase	)
       self.assertEqual(__lowerCamelCase   ,				'''[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	)		->      Tuple:





       '''simple docstring'''





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

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

       self.assertListEqual(__lowerCamelCase   ,				__lowerCamelCase	)

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






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





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






  def 		_a					(self	)		->      str:





       '''simple docstring'''





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






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      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
       UpperCamelCase__										=      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
                 UpperCamelCase__										=      tempfile.mkdtemp()

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

                 UpperCamelCase__										=      tokenizer.__class__.from_pretrained(__lowerCamelCase	)
                 UpperCamelCase__										=      after_tokenizer.encode(__lowerCamelCase   ,				add_special_tokens=__lowerCamelCase	)
                 self.assertListEqual(__lowerCamelCase   ,				__lowerCamelCase	)

                 shutil.rmtree(__lowerCamelCase	)

       UpperCamelCase__										=      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
                 UpperCamelCase__										=      tempfile.mkdtemp()

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

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

                 UpperCamelCase__										=      tokenizer.__class__.from_pretrained(__lowerCamelCase   ,				model_max_length=43	)
                 self.assertEqual(tokenizer.model_max_length   ,				43	)

                 shutil.rmtree(__lowerCamelCase	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      []
       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(__lowerCamelCase	)

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

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

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

                 UpperCamelCase__										=      added_tokens_extra_ids + [
                     "an_additional_special_token"
                 ]
                 UpperCamelCase__										=      added_tokens_extra_ids + [
                     "an_additional_special_token"
                 ]

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

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

                 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	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      self.perceiver_tokenizer
       self.assertEqual(tokenizer.decode([178]	)   ,				'''�'''	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.get_tokenizers(fast=__lowerCamelCase   ,				do_lower_case=__lowerCamelCase	)
       for tokenizer in tokenizers:
            with self.subTest(F"{tokenizer.__class__.__name__}"	):
                 UpperCamelCase__										=      ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
                 UpperCamelCase__										=      tokenizer.convert_tokens_to_string(__lowerCamelCase	)

                 self.assertIsInstance(__lowerCamelCase   ,				__lowerCamelCase	)




import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM



@require_torch
class     _A   :






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=99   ,				SCREAMING_SNAKE_CASE_=13   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=7   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=False   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=32   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=30   ,				SCREAMING_SNAKE_CASE_=0   ,				SCREAMING_SNAKE_CASE_=1   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=None   ,				)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      parent
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      decoder_seq_length
       # For common tests
       UpperCamelCase__										=      self.decoder_seq_length
       UpperCamelCase__										=      is_training
       UpperCamelCase__										=      use_attention_mask
       UpperCamelCase__										=      use_labels

       UpperCamelCase__										=      vocab_size
       UpperCamelCase__										=      d_model
       UpperCamelCase__										=      d_model
       UpperCamelCase__										=      decoder_layers
       UpperCamelCase__										=      decoder_layers
       UpperCamelCase__										=      decoder_ffn_dim
       UpperCamelCase__										=      decoder_attention_heads
       UpperCamelCase__										=      decoder_attention_heads
       UpperCamelCase__										=      eos_token_id
       UpperCamelCase__										=      bos_token_id
       UpperCamelCase__										=      pad_token_id
       UpperCamelCase__										=      decoder_start_token_id
       UpperCamelCase__										=      use_cache
       UpperCamelCase__										=      max_position_embeddings

       UpperCamelCase__										=      None
       UpperCamelCase__										=      decoder_seq_length
       UpperCamelCase__										=      2
       UpperCamelCase__										=      1






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size	)

       UpperCamelCase__										=      None
       if self.use_attention_mask:
            UpperCamelCase__										=      ids_tensor([self.batch_size, self.decoder_seq_length]   ,				vocab_size=2	)

       UpperCamelCase__										=      None
       if self.use_labels:
            UpperCamelCase__										=      ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size	)

       UpperCamelCase__										=      TrOCRConfig(
           vocab_size=self.vocab_size   ,				d_model=self.d_model   ,				decoder_layers=self.decoder_layers   ,				decoder_ffn_dim=self.decoder_ffn_dim   ,				decoder_attention_heads=self.decoder_attention_heads   ,				eos_token_id=self.eos_token_id   ,				bos_token_id=self.bos_token_id   ,				use_cache=self.use_cache   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.decoder_start_token_id   ,				max_position_embeddings=self.max_position_embeddings   ,				)

       return (config, input_ids, attention_mask, lm_labels)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      True
       UpperCamelCase__										=      TrOCRDecoder(config=SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	).eval()
       UpperCamelCase__										=      input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				use_cache=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				use_cache=SCREAMING_SNAKE_CASE_	)

       self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_	) == len(SCREAMING_SNAKE_CASE_	)	)
       self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_	) == len(SCREAMING_SNAKE_CASE_	) + 1	)

       UpperCamelCase__										=      outputs['''past_key_values''']

       # create hypothetical next token and extent to next_input_ids
       UpperCamelCase__										=      ids_tensor((2, 1)   ,				config.vocab_size - 1	) + 1

       # append to next input_ids and
       UpperCamelCase__										=      torch.cat([input_ids, next_tokens]   ,				dim=-1	)

       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)['''last_hidden_state''']
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				past_key_values=SCREAMING_SNAKE_CASE_	)['''last_hidden_state''']

       # select random slice
       UpperCamelCase__										=      ids_tensor((1,)   ,				output_from_past.shape[-1]	).item()
       UpperCamelCase__										=      output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
       UpperCamelCase__										=      output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       assert torch.allclose(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-3	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      config_and_inputs

       UpperCamelCase__										=      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict



@require_torch
class     _A   (    __UpperCamelCase	, __UpperCamelCase	, __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						=(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE_	:	Any						=(TrOCRForCausalLM,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE_	:	Optional[int]						={"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
  SCREAMING_SNAKE_CASE_	:	Optional[int]						=True
  SCREAMING_SNAKE_CASE_	:	Dict						=False






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      TrOCRStandaloneDecoderModelTester(self   ,				is_training=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      ConfigTester(self   ,				config_class=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       self.config_tester.run_common_tests()






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       return






  @unittest.skip('''The model doesn\'t support left padding'''	)  # and it's not used enough to be worth fixing :)
  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       pass



