"""simple docstring"""
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Optional[int]     					=  MvpTokenizer
				_lowerCAmelCase       :      int     					=  MvpTokenizerFast
				_lowerCAmelCase       :      Optional[int]     					=  True
				_lowerCAmelCase       :      str     					=  filter_roberta_detectors

				def 						snake_case	(			self      ):






										"""simple docstring"""

										super().setUp()
										snake_case        =     [
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										]
										snake_case        =     dict(zip(lowerCAmelCase						,      range(len(lowerCAmelCase      )      )      )      )
										snake_case        =     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
										snake_case        =     {'unk_token': '<unk>'}

										snake_case        =     os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['vocab_file']      )
										snake_case        =     os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['merges_file']      )
										with open(self.vocab_file						,      'w'						,      encoding='utf-8'      ) as fp:
																fp.write(json.dumps(lowerCAmelCase      ) + '\n'      )
										with open(self.merges_file						,      'w'						,      encoding='utf-8'      ) as fp:
																fp.write('\n'.join(lowerCAmelCase      )      )

				def 						snake_case	(			self						,      **lowerCAmelCase      ):






										"""simple docstring"""

										kwargs.update(self.special_tokens_map      )
										return self.tokenizer_class.from_pretrained(self.tmpdirname						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      **lowerCAmelCase      ):






										"""simple docstring"""

										kwargs.update(self.special_tokens_map      )
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,      **lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										return "lower newer", "lower newer"

				@cached_property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return MvpTokenizer.from_pretrained('RUCAIBox/mvp'      )

				@cached_property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'      )

				@require_torch
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
										snake_case        =     [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																snake_case        =     tokenizer(lowerCAmelCase						,      max_length=len(lowerCAmelCase      )						,      padding=lowerCAmelCase						,      return_tensors='pt'      )
																self.assertIsInstance(lowerCAmelCase						,      lowerCAmelCase      )

																self.assertEqual((2, 9)						,      batch.input_ids.shape      )
																self.assertEqual((2, 9)						,      batch.attention_mask.shape      )
																snake_case        =     batch.input_ids.tolist()[0]
																self.assertListEqual(lowerCAmelCase						,      lowerCAmelCase      )
																# Test that special tokens are reset

				@require_torch
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																snake_case        =     tokenizer(lowerCAmelCase						,      padding=lowerCAmelCase						,      return_tensors='pt'      )
																# check if input_ids are returned and no labels
																self.assertIn('input_ids'						,      lowerCAmelCase      )
																self.assertIn('attention_mask'						,      lowerCAmelCase      )
																self.assertNotIn('labels'						,      lowerCAmelCase      )
																self.assertNotIn('decoder_attention_mask'						,      lowerCAmelCase      )

				@require_torch
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     [
										    'Summary of the text.',
										    'Another summary.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																snake_case        =     tokenizer(text_target=lowerCAmelCase						,      max_length=32						,      padding='max_length'						,      return_tensors='pt'      )
																self.assertEqual(32						,      targets['input_ids'].shape[1]      )

				@require_torch
				def 						snake_case	(			self      ):






										"""simple docstring"""

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																snake_case        =     tokenizer(
																    ['I am a small frog' * 10_24, 'I am a small frog']						,      padding=lowerCAmelCase						,      truncation=lowerCAmelCase						,      return_tensors='pt'      )
																self.assertIsInstance(lowerCAmelCase						,      lowerCAmelCase      )
																self.assertEqual(batch.input_ids.shape						,      (2, 10_24)      )

				@require_torch
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     ['A long paragraph for summarization.']
										snake_case        =     [
										    'Summary of the text.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																snake_case        =     tokenizer(lowerCAmelCase						,      text_target=lowerCAmelCase						,      return_tensors='pt'      )
																snake_case        =     inputs['input_ids']
																snake_case        =     inputs['labels']
																self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
																self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
																self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
																self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										pass

				def 						snake_case	(			self      ):






										"""simple docstring"""

										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																						snake_case        =     self.rust_tokenizer_class.from_pretrained(lowerCAmelCase						,      **lowerCAmelCase      )
																						snake_case        =     self.tokenizer_class.from_pretrained(lowerCAmelCase						,      **lowerCAmelCase      )
																						snake_case        =     'A, <mask> AllenNLP sentence.'
																						snake_case        =     tokenizer_r.encode_plus(lowerCAmelCase						,      add_special_tokens=lowerCAmelCase						,      return_token_type_ids=lowerCAmelCase      )
																						snake_case        =     tokenizer_p.encode_plus(lowerCAmelCase						,      add_special_tokens=lowerCAmelCase						,      return_token_type_ids=lowerCAmelCase      )

																						# token_type_ids should put 0 everywhere
																						self.assertEqual(sum(tokens_r['token_type_ids']      )						,      sum(tokens_p['token_type_ids']      )      )

																						# attention_mask should put 1 everywhere, so sum over length should be 1
																						self.assertEqual(
																						    sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      )						,      sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      )						,      )

																						snake_case        =     tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
																						snake_case        =     tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )

																						# Rust correctly handles the space before the mask while python doesnt
																						self.assertSequenceEqual(tokens_p['input_ids']						,      [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )
																						self.assertSequenceEqual(tokens_r['input_ids']						,      [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )

																						self.assertSequenceEqual(
																						    lowerCAmelCase						,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
																						self.assertSequenceEqual(
																						    lowerCAmelCase						,      ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Tuple     					=  """swinv2"""

				_lowerCAmelCase       :      Any     					=  {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}

				def __init__(			self						,      lowerCAmelCase=2_24						,      lowerCAmelCase=4						,      lowerCAmelCase=3						,      lowerCAmelCase=96						,      lowerCAmelCase=[2, 2, 6, 2]						,      lowerCAmelCase=[3, 6, 12, 24]						,      lowerCAmelCase=7						,      lowerCAmelCase=4.0						,      lowerCAmelCase=True						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.1						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=False						,      lowerCAmelCase=0.02						,      lowerCAmelCase=1E-5						,      lowerCAmelCase=32						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )

										snake_case        =     image_size
										snake_case        =     patch_size
										snake_case        =     num_channels
										snake_case        =     embed_dim
										snake_case        =     depths
										snake_case        =     len(lowerCAmelCase      )
										snake_case        =     num_heads
										snake_case        =     window_size
										snake_case        =     mlp_ratio
										snake_case        =     qkv_bias
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     drop_path_rate
										snake_case        =     hidden_act
										snake_case        =     use_absolute_embeddings
										snake_case        =     layer_norm_eps
										snake_case        =     initializer_range
										snake_case        =     encoder_stride
										# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										snake_case        =     int(embed_dim * 2 ** (len(lowerCAmelCase      ) - 1)      )
										snake_case        =     (0, 0, 0, 0)


