
"""simple docstring"""



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 				a			(					lowercase      ):
      UpperCamelCase							:   Tuple           =     ["""image_processor""", """tokenizer"""]
      UpperCamelCase							:   List[str]           =     """LayoutLMv2ImageProcessor"""
      UpperCamelCase							:   Optional[int]           =     ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'     ,	UpperCamelCase_     ,	)
              UpperCAmelCase__	:      Tuple     							=					kwargs.pop('feature_extractor'      )

          UpperCAmelCase__	:      Optional[Any]     							=					image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('You need to specify an `image_processor`.'      )
          if tokenizer is None:
              raise ValueError('You need to specify a `tokenizer`.'      )

          super().__init__(UpperCamelCase_     ,	UpperCamelCase_      )



      def __call__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = 0     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  'You cannot provide bounding boxes '
                  'if you initialized the image processor with apply_ocr set to True.'      )

          if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'      )

          if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'      )

          # first, apply the image processor
          UpperCAmelCase__	:      List[Any]     							=					self.image_processor(images=UpperCamelCase_     ,	return_tensors=UpperCamelCase_      )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ):
                  UpperCAmelCase__	:      Any     							=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
              UpperCAmelCase__	:      Union[str, Any]     							=					features['words']

          UpperCAmelCase__	:      List[Any]     							=					self.tokenizer(
              text=text if text is not None else features['words']     ,	text_pair=text_pair if text_pair is not None else None     ,	boxes=boxes if boxes is not None else features['boxes']     ,	word_labels=UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_     ,	padding=UpperCamelCase_     ,	truncation=UpperCamelCase_     ,	max_length=UpperCamelCase_     ,	stride=UpperCamelCase_     ,	pad_to_multiple_of=UpperCamelCase_     ,	return_token_type_ids=UpperCamelCase_     ,	return_attention_mask=UpperCamelCase_     ,	return_overflowing_tokens=UpperCamelCase_     ,	return_special_tokens_mask=UpperCamelCase_     ,	return_offsets_mapping=UpperCamelCase_     ,	return_length=UpperCamelCase_     ,	verbose=UpperCamelCase_     ,	return_tensors=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          # add pixel values
          UpperCAmelCase__	:      List[Any]     							=					features.pop('pixel_values'      )
          if return_overflowing_tokens is True:
              UpperCAmelCase__	:      int     							=					self.get_overflowing_images(UpperCamelCase_     ,	encoded_inputs['overflow_to_sample_mapping']      )
          UpperCAmelCase__	:      Dict     							=					images

          return encoded_inputs



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          UpperCAmelCase__	:      Dict     							=					[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]      )

          if len(UpperCamelCase_      ) != len(UpperCamelCase_      ):
              raise ValueError(
                  'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                  F''' {len(UpperCamelCase_      )} and {len(UpperCamelCase_      )}'''      )

          return images_with_overflow



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.batch_decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.decode(*UpperCamelCase_     ,	**UpperCamelCase_      )



      @property
      def  __snake_case							(   self      ):
          return ["input_ids", "bbox", "attention_mask", "image"]



      @property
      def  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor_class



      @property
      def  __snake_case							(   self      ):
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,	UpperCamelCase_     ,	)
          return self.image_processor



"""simple docstring"""



import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402


UpperCamelCase__          =	{
    'return_dict': False,
    'output_hidden_states': True,
    'output_attentions': True,
    'torchscript': True,
    'torch_dtype': 'float16',
    'use_bfloat16': True,
    'tf_legacy_loss': True,
    'pruned_heads': {'a': 1},
    'tie_word_embeddings': False,
    'is_decoder': True,
    'cross_attention_hidden_size': 1_28,
    'add_cross_attention': True,
    'tie_encoder_decoder': True,
    'max_length': 50,
    'min_length': 3,
    'do_sample': True,
    'early_stopping': True,
    'num_beams': 3,
    'num_beam_groups': 3,
    'diversity_penalty': 0.5,
    'temperature': 2.0,
    'top_k': 10,
    'top_p': 0.7,
    'typical_p': 0.2,
    'repetition_penalty': 0.8,
    'length_penalty': 0.8,
    'no_repeat_ngram_size': 5,
    'encoder_no_repeat_ngram_size': 5,
    'bad_words_ids': [1, 2, 3],
    'num_return_sequences': 3,
    'chunk_size_feed_forward': 5,
    'output_scores': True,
    'return_dict_in_generate': True,
    'forced_bos_token_id': 2,
    'forced_eos_token_id': 3,
    'remove_invalid_values': True,
    'architectures': ['BertModel'],
    'finetuning_task': 'translation',
    'id2label': {0: 'label'},
    'label2id': {'label': '0'},
    'tokenizer_class': 'BertTokenizerFast',
    'prefix': 'prefix',
    'bos_token_id': 6,
    'pad_token_id': 7,
    'eos_token_id': 8,
    'sep_token_id': 9,
    'decoder_start_token_id': 10,
    'exponential_decay_length_penalty': (5, 1.01),
    'suppress_tokens': [0, 1],
    'begin_suppress_tokens': 2,
    'task_specific_params': {'translation': 'some_params'},
    'problem_type': 'regression',
}





@is_staging_test
class 				a			(					unittest.TestCase      ):



      @classmethod
      def  __snake_case							(   cls      ):
          UpperCAmelCase__	:      List[str]     							=					TOKEN
          HfFolder.save_token(UpperCamelCase_      )



      @classmethod
      def  __snake_case							(   cls      ):
          try:
              delete_repo(token=cls._token     ,	repo_id='test-config'      )
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token     ,	repo_id='valid_org/test-config-org'      )
          except HTTPError:
              pass

          try:
              delete_repo(token=cls._token     ,	repo_id='test-dynamic-config'      )
          except HTTPError:
              pass



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					BertConfig(
              vocab_size=99     ,	hidden_size=32     ,	num_hidden_layers=5     ,	num_attention_heads=4     ,	intermediate_size=37      )
          config.push_to_hub('test-config'     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Optional[int]     							=					BertConfig.from_pretrained(F'''{USER}/test-config'''      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )

        # Reset repo
          delete_repo(token=self._token     ,	repo_id='test-config'      )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(UpperCamelCase_     ,	repo_id='test-config'     ,	push_to_hub=UpperCamelCase_     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained(F'''{USER}/test-config'''      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[Any]     							=					BertConfig(
              vocab_size=99     ,	hidden_size=32     ,	num_hidden_layers=5     ,	num_attention_heads=4     ,	intermediate_size=37      )
          config.push_to_hub('valid_org/test-config-org'     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained('valid_org/test-config-org'      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )

        # Reset repo
          delete_repo(token=self._token     ,	repo_id='valid_org/test-config-org'      )

          # Push to hub via save_pretrained
          with tempfile.TemporaryDirectory() as tmp_dir:
              config.save_pretrained(
                  UpperCamelCase_     ,	repo_id='valid_org/test-config-org'     ,	push_to_hub=UpperCamelCase_     ,	use_auth_token=self._token      )

          UpperCAmelCase__	:      Optional[Any]     							=					BertConfig.from_pretrained('valid_org/test-config-org'      )
          for k, v in config.to_dict().items():
              if k != "transformers_version":
                  self.assertEqual(UpperCamelCase_     ,	getattr(UpperCamelCase_     ,	UpperCamelCase_      )      )



      def  __snake_case							(   self      ):
          CustomConfig.register_for_auto_class()
          UpperCAmelCase__	:      Dict     							=					CustomConfig(attribute=42      )

          config.push_to_hub('test-dynamic-config'     ,	use_auth_token=self._token      )

          # This has added the proper auto_map field to the config
          self.assertDictEqual(config.auto_map     ,	{'AutoConfig': 'custom_configuration.CustomConfig'}      )

          UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config'''     ,	trust_remote_code=UpperCamelCase_      )
          # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
          self.assertEqual(new_config.__class__.__name__     ,	'CustomConfig'      )
          self.assertEqual(new_config.attribute     ,	42      )





class 				a			(					unittest.TestCase      ):



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      List[str]     							=					GPTaConfig()

          # attempt to modify each of int/float/bool/str config records and verify they were updated
          UpperCAmelCase__	:      List[str]     							=					c.n_embd + 1  # int
          UpperCAmelCase__	:      Optional[int]     							=					c.resid_pdrop + 1.0  # float
          UpperCAmelCase__	:      Optional[int]     							=					not c.scale_attn_weights  # bool
          UpperCAmelCase__	:      str     							=					c.summary_type + 'foo'  # str
          c.update_from_string(
              F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}'''      )
          self.assertEqual(UpperCamelCase_     ,	c.n_embd     ,	'mismatch for key: n_embd'      )
          self.assertEqual(UpperCamelCase_     ,	c.resid_pdrop     ,	'mismatch for key: resid_pdrop'      )
          self.assertEqual(UpperCamelCase_     ,	c.scale_attn_weights     ,	'mismatch for key: scale_attn_weights'      )
          self.assertEqual(UpperCamelCase_     ,	c.summary_type     ,	'mismatch for key: summary_type'      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					PretrainedConfig()
          UpperCAmelCase__	:      str     							=					[key for key in base_config.__dict__ if key not in config_common_kwargs]
          # If this part of the test fails, you have arguments to addin config_common_kwargs above.
          self.assertListEqual(
              UpperCamelCase_     ,	['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version']      )
          UpperCAmelCase__	:      int     							=					[key for key, value in config_common_kwargs.items() if value == getattr(UpperCamelCase_     ,	UpperCamelCase_      )]
          if len(UpperCamelCase_      ) > 0:
              raise ValueError(
                  'The following keys are set with the default values in'
                  ' `test_configuration_common.config_common_kwargs` pick another value for them:'
                  F''' {', '.join(UpperCamelCase_      )}.'''      )



      def  __snake_case							(   self      ):
          with self.assertRaises(UpperCamelCase_      ):
              # config is in subfolder, the following should not work without specifying the subfolder
              UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'      )

          UpperCAmelCase__	:      str     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'     ,	subfolder='bert'      )

          self.assertIsNotNone(UpperCamelCase_      )



      def  __snake_case							(   self      ):
          # A mock response for an HTTP head request to emulate server down
          UpperCAmelCase__	:      str     							=					mock.Mock()
          UpperCAmelCase__	:      List[Any]     							=					500
          UpperCAmelCase__	:      Optional[int]     							=					{}
          UpperCAmelCase__	:      Dict     							=					HTTPError
          UpperCAmelCase__	:      Optional[int]     							=					{}

          # Download this model to make sure it's in the cache.
          UpperCAmelCase__	:      List[str]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'      )

          # Under the mock environment we get a 500 error when trying to reach the model.
          with mock.patch('requests.Session.request'     ,	return_value=UpperCamelCase_      ) as mock_head:
              UpperCAmelCase__	:      Optional[Any]     							=					BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'      )
              # This check we did call the fake head request
              mock_head.assert_called()



      def  __snake_case							(   self      ):
          # This test is for deprecated behavior and can be removed in v5
          UpperCAmelCase__	:      List[str]     							=					BertConfig.from_pretrained(
              'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      int     							=					AutoConfig.from_pretrained('bert-base-cased'      )
          UpperCAmelCase__	:      str     							=					['config.4.0.0.json']

          with tempfile.TemporaryDirectory() as tmp_dir:
              configuration.save_pretrained(UpperCamelCase_      )
              UpperCAmelCase__	:      List[str]     							=					2
              json.dump(configuration.to_dict()     ,	open(os.path.join(UpperCamelCase_     ,	'config.4.0.0.json'      )     ,	'w'      )      )

              # This should pick the new configuration file as the version of Transformers is > 4.0.0
              UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(UpperCamelCase_      )
              self.assertEqual(new_configuration.hidden_size     ,	2      )

              # Will need to be adjusted if we reach v42 and this test is still here.
              # Should pick the old configuration file as the version of Transformers is < 4.42.0
              UpperCAmelCase__	:      Tuple     							=					['config.42.0.0.json']
              UpperCAmelCase__	:      Tuple     							=					768
              configuration.save_pretrained(UpperCamelCase_      )
              shutil.move(os.path.join(UpperCamelCase_     ,	'config.4.0.0.json'      )     ,	os.path.join(UpperCamelCase_     ,	'config.42.0.0.json'      )      )
              UpperCAmelCase__	:      Any     							=					AutoConfig.from_pretrained(UpperCamelCase_      )
              self.assertEqual(new_configuration.hidden_size     ,	768      )



      def  __snake_case							(   self      ):
          # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
          UpperCAmelCase__	:      Tuple     							=					'hf-internal-testing/test-two-configs'

          import transformers as new_transformers

          UpperCAmelCase__	:      Optional[int]     							=					'v4.0.0'
          UpperCAmelCase__							,							UpperCAmelCase__	:      Any     							=					new_transformers.models.auto.AutoConfig.from_pretrained(
              UpperCamelCase_     ,	return_unused_kwargs=UpperCamelCase_      )
          self.assertEqual(new_configuration.hidden_size     ,	2      )
          # This checks `_configuration_file` ia not kept in the kwargs by mistake.
          self.assertDictEqual(UpperCamelCase_     ,	{}      )

          # Testing an older version by monkey-patching the version in the module it's used.
          import transformers as old_transformers

          UpperCAmelCase__	:      List[Any]     							=					'v3.0.0'
          UpperCAmelCase__	:      Union[str, Any]     							=					old_transformers.models.auto.AutoConfig.from_pretrained(UpperCamelCase_      )
          self.assertEqual(old_configuration.hidden_size     ,	768      )

