
"""simple docstring"""


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







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



						'''simple docstring'''


						_lowerCamelCase:			Tuple					=			None
						_lowerCamelCase:			Union[str, Any]					=			BloomTokenizerFast
						_lowerCamelCase:			int					=			BloomTokenizerFast
						_lowerCamelCase:			List[str]					=			True
						_lowerCamelCase:			str					=			False
						_lowerCamelCase:			Union[str, Any]					=			'''tokenizer_file'''
						_lowerCamelCase:			Tuple					=			{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      List[Any]:
							super().setUp()
							A        =      BloomTokenizerFast.from_pretrained('bigscience/tokenizer'					)
							tokenizer.save_pretrained(self.tmpdirname					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,**A_   :					str					)   ->      Dict:
							kwargs.update(self.special_tokens_map					)
							return BloomTokenizerFast.from_pretrained(self.tmpdirname     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      List[str]:
							A        =      self.get_rust_tokenizer()

							A        =      ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
							A        =      [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

							A        =      tokenizer.batch_encode_plus(A_					)['input_ids']
							self.assertListEqual(A_     ,A_					)

							A        =      tokenizer.batch_decode(A_					)
							self.assertListEqual(A_     ,A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					List[Any]=6					)   ->      List[str]:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
									A        =      self.rust_tokenizer_class.from_pretrained(A_     ,**A_					)
									# tokenizer_r.pad_token = None # Hotfixing padding = None
									# Simple input
									A        =      'This is a simple input'
									A        =      ['This is a simple input 1', 'This is a simple input 2']
									A        =      ('This is a simple input', 'This is a pair')
									A        =      [
									    ('This is a simple input 1', 'This is a simple input 2'),
									    ('This is a simple pair 1', 'This is a simple pair 2'),
									]

									# Simple input tests
									try:
										tokenizer_r.encode(A_     ,max_length=A_					)
										tokenizer_r.encode_plus(A_     ,max_length=A_					)

										tokenizer_r.batch_encode_plus(A_     ,max_length=A_					)
										tokenizer_r.encode(A_     ,max_length=A_					)
										tokenizer_r.batch_encode_plus(A_     ,max_length=A_					)
									except ValueError:
										self.fail('Bloom Tokenizer should be able to deal with padding'					)

									A        =      None  # Hotfixing padding = None
									self.assertRaises(A_     ,tokenizer_r.encode     ,A_     ,max_length=A_     ,padding='max_length'					)

									# Simple input
									self.assertRaises(A_     ,tokenizer_r.encode_plus     ,A_     ,max_length=A_     ,padding='max_length'					)

									# Simple input
									self.assertRaises(
									    A_     ,tokenizer_r.batch_encode_plus     ,A_     ,max_length=A_     ,padding='max_length'     ,)

									# Pair input
									self.assertRaises(A_     ,tokenizer_r.encode     ,A_     ,max_length=A_     ,padding='max_length'					)

									# Pair input
									self.assertRaises(A_     ,tokenizer_r.encode_plus     ,A_     ,max_length=A_     ,padding='max_length'					)

									# Pair input
									self.assertRaises(
									    A_     ,tokenizer_r.batch_encode_plus     ,A_     ,max_length=A_     ,padding='max_length'     ,)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      List[str]:
							A        =      self.get_rust_tokenizer()
							A        =      load_dataset('xnli'     ,'all_languages'     ,split='test'     ,streaming=A_					)

							A        =      next(iter(A_					)					)['premise']  # pick up one data
							A        =      list(sample_data.values()					)

							A        =      list(map(tokenizer.encode     ,A_					)					)
							A        =      [tokenizer.decode(A_     ,clean_up_tokenization_spaces=A_					) for x in output_tokens]
							self.assertListEqual(A_     ,A_					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      List[Any]:
							# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
							# any sequence length constraints. This test of the parent class will fail since it relies on the
							# maximum sequence length of the positoonal embeddings.
							self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map					)     ,1					)
							self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()					)[0]					)     ,1					)






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


UpperCAmelCase	:	Dict		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Tuple		     =       {
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Any						=			"""swin2sr"""

      _lowercase				:					Tuple						=			{
          """hidden_size""": """embed_dim""",
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }







      def __init__(    self		,      lowerCAmelCase__=6_4		,      lowerCAmelCase__=1		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8_0		,      lowerCAmelCase__=[6, 6, 6, 6, 6, 6]		,      lowerCAmelCase__=[6, 6, 6, 6, 6, 6]		,      lowerCAmelCase__=8		,      lowerCAmelCase__=2.0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__="gelu"		,      lowerCAmelCase__=False		,      lowerCAmelCase__=0.02		,      lowerCAmelCase__=1E-5		,      lowerCAmelCase__=2		,      lowerCAmelCase__=1.0		,      lowerCAmelCase__="1conv"		,      lowerCAmelCase__="pixelshuffle"		,      **lowerCAmelCase__		,      )   ->						int:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

        a__	:					Optional[Any]										=image_size
        a__	:					Dict										=patch_size
        a__	:					Tuple										=num_channels
        a__	:					Union[str, Any]										=embed_dim
        a__	:					Optional[Any]										=depths
        a__	:					List[str]										=len(lowerCAmelCase__							)
        a__	:					Any										=num_heads
        a__	:					Any										=window_size
        a__	:					str										=mlp_ratio
        a__	:					List[str]										=qkv_bias
        a__	:					Dict										=hidden_dropout_prob
        a__	:					List[str]										=attention_probs_dropout_prob
        a__	:					Dict										=drop_path_rate
        a__	:					Optional[Any]										=hidden_act
        a__	:					Union[str, Any]										=use_absolute_embeddings
        a__	:					Optional[Any]										=layer_norm_eps
        a__	:					List[Any]										=initializer_range
        a__	:					int										=upscale
        a__	:					Optional[int]										=img_range
        a__	:					Any										=resi_connection
        a__	:					Optional[Any]										=upsampler






