





"""simple docstring"""
import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType






@require_torch
class  _UpperCAmelCase       :
					UpperCamelCase								=							()
					UpperCamelCase								=							True


					def      lowerCamelCase	( self      :Optional[int]		):
												A		=  self.config_class

												# test default config
												A		=  config_class()
												self.assertIsNotNone(__UpperCamelCase		)
												A		=  ["stem"] + [f"stage{idx}" for idx in range(1					,	len(config.depths		) + 1		)]
												self.assertEqual(config.stage_names					,	__UpperCamelCase		)
												self.assertTrue(set(config.out_features		).issubset(set(config.stage_names		)		)		)

												# Test out_features and out_indices are correctly set
												# out_features and out_indices both None
												A		=  config_class(out_features=__UpperCamelCase					,	out_indices=__UpperCamelCase		)
												self.assertEqual(config.out_features					,	[config.stage_names[-1]]		)
												self.assertEqual(config.out_indices					,	[len(config.stage_names		) - 1]		)

												# out_features and out_indices both set
												A		=  config_class(out_features=["stem", "stage1"]					,	out_indices=[0, 1]		)
												self.assertEqual(config.out_features					,	["stem", "stage1"]		)
												self.assertEqual(config.out_indices					,	[0, 1]		)

												# Only out_features set
												A		=  config_class(out_features=["stage1", "stage3"]		)
												self.assertEqual(config.out_features					,	["stage1", "stage3"]		)
												self.assertEqual(config.out_indices					,	[1, 3]		)

												# Only out_indices set
												A		=  config_class(out_indices=[0, 2]		)
												self.assertEqual(config.out_features					,	[config.stage_names[0], config.stage_names[2]]		)
												self.assertEqual(config.out_indices					,	[0, 2]		)

												# Error raised when out_indices do not correspond to out_features
												with self.assertRaises(__UpperCamelCase		):
																			A		=  config_class(out_features=["stage1", "stage2"]					,	out_indices=[0, 2]		)


					def      lowerCamelCase	( self      :Dict		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A		=  model_class(__UpperCamelCase		)
																			A		=  inspect.signature(model.forward		)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			A		=  [*signature.parameters.keys()]
																			A		=  ["pixel_values"]
																			self.assertListEqual(arg_names[:1]					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A		=  model_class(__UpperCamelCase		)
																			self.assertEqual(len(model.channels		)					,	len(config.out_features		)		)
																			A		=  model.num_features
																			A		=  [config.stage_names.index(__UpperCamelCase		) for feat in config.out_features]
																			A		=  [num_features[idx] for idx in out_indices]
																			self.assertListEqual(model.channels					,	__UpperCamelCase		)

																			A		=  copy.deepcopy(__UpperCamelCase		)
																			A		=  None
																			A		=  model_class(__UpperCamelCase		)
																			self.assertEqual(len(model.channels		)					,	1		)
																			self.assertListEqual(model.channels					,	[num_features[-1]]		)

																			A		=  copy.deepcopy(__UpperCamelCase		)
																			A		=  None
																			A		=  model_class(__UpperCamelCase		)
																			self.assertEqual(len(model.channels		)					,	1		)
																			self.assertListEqual(model.channels					,	[num_features[-1]]		)


					def      lowerCamelCase	( self      :Optional[int]		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A		=  model_class(__UpperCamelCase		)
																			model.to(__UpperCamelCase		)
																			model.eval()
																			A		=  model(**__UpperCamelCase		)

																			self.assertEqual(len(result.feature_maps		)					,	len(config.out_features		)		)
																			self.assertEqual(len(model.channels		)					,	len(config.out_features		)		)
																			self.assertEqual(len(result.feature_maps		)					,	len(config.out_indices		)		)
																			self.assertEqual(len(model.channels		)					,	len(config.out_indices		)		)

																			# Check output of last stage is taken if out_features=None, out_indices=None
																			A		=  copy.deepcopy(__UpperCamelCase		)
																			A		=  None
																			A		=  model_class(__UpperCamelCase		)
																			model.to(__UpperCamelCase		)
																			model.eval()
																			A		=  model(**__UpperCamelCase		)

																			self.assertEqual(len(result.feature_maps		)					,	1		)
																			self.assertEqual(len(model.channels		)					,	1		)

																			A		=  copy.deepcopy(__UpperCamelCase		)
																			A		=  None
																			A		=  model_class(__UpperCamelCase		)
																			model.to(__UpperCamelCase		)
																			model.eval()
																			A		=  model(**__UpperCamelCase		)

																			self.assertEqual(len(result.feature_maps		)					,	1		)
																			self.assertEqual(len(model.channels		)					,	1		)

																			# Check backbone can be initialized with fresh weights
																			A		=  copy.deepcopy(__UpperCamelCase		)
																			A		=  False
																			A		=  model_class(__UpperCamelCase		)
																			model.to(__UpperCamelCase		)
																			model.eval()
																			A		=  model(**__UpperCamelCase		)


					def      lowerCamelCase	( self      :str		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()

												for backbone_class in self.all_model_classes:
																			A		=  backbone_class(__UpperCamelCase		)

																			self.assertTrue(hasattr(__UpperCamelCase					,	"backbone_type"		)		)
																			self.assertTrue(hasattr(__UpperCamelCase					,	"stage_names"		)		)
																			self.assertTrue(hasattr(__UpperCamelCase					,	"num_features"		)		)
																			self.assertTrue(hasattr(__UpperCamelCase					,	"out_indices"		)		)
																			self.assertTrue(hasattr(__UpperCamelCase					,	"out_features"		)		)
																			self.assertTrue(hasattr(__UpperCamelCase					,	"out_feature_channels"		)		)
																			self.assertTrue(hasattr(__UpperCamelCase					,	"channels"		)		)

																			self.assertIsInstance(backbone.backbone_type					,	__UpperCamelCase		)
																			# Verify num_features has been initialized in the backbone init
																			self.assertIsNotNone(backbone.num_features		)
																			self.assertTrue(len(backbone.channels		) == len(backbone.out_indices		)		)
																			self.assertTrue(len(backbone.stage_names		) == len(backbone.num_features		)		)
																			self.assertTrue(len(backbone.channels		) <= len(backbone.num_features		)		)
																			self.assertTrue(len(backbone.out_feature_channels		) == len(backbone.stage_names		)		)


					def      lowerCamelCase	( self      :int		):
												A,     A		=  self.model_tester.prepare_config_and_inputs_for_common()
												A		=  inputs_dict["pixel_values"].shape[0]

												for backbone_class in self.all_model_classes:
																			A		=  backbone_class(__UpperCamelCase		)
																			backbone.to(__UpperCamelCase		)
																			backbone.eval()

																			A		=  backbone(**__UpperCamelCase		)

																			# Test default outputs and verify feature maps
																			self.assertIsInstance(outputs.feature_maps					,	__UpperCamelCase		)
																			self.assertTrue(len(outputs.feature_maps		) == len(backbone.channels		)		)
																			for feature_map, n_channels in zip(outputs.feature_maps					,	backbone.channels		):
																										self.assertTrue(feature_map.shape[:2]					,	(batch_size, n_channels)		)
																			self.assertIsNone(outputs.hidden_states		)
																			self.assertIsNone(outputs.attentions		)

																			# Test output_hidden_states=True
																			A		=  backbone(**__UpperCamelCase					,	output_hidden_states=__UpperCamelCase		)
																			self.assertIsNotNone(outputs.hidden_states		)
																			self.assertTrue(len(outputs.hidden_states		)					,	len(backbone.stage_names		)		)
																			for hidden_state, n_channels in zip(outputs.hidden_states					,	backbone.channels		):
																										self.assertTrue(hidden_state.shape[:2]					,	(batch_size, n_channels)		)

																			# Test output_attentions=True
																			if self.has_attentions:
																										A		=  backbone(**__UpperCamelCase					,	output_attentions=__UpperCamelCase		)
																										self.assertIsNotNone(outputs.attentions		)









"""simple docstring"""
import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		_snake_case					:		Dict							=			None


_snake_case					:		int							=			logging.get_logger(__name__)


_snake_case					:		List[Any]							=			'▁'

_snake_case					:		Tuple							=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case					:		Tuple							=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

_snake_case					:		List[Any]							=			{
    'google/reformer-crime-and-punishment': 52_4288,
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							VOCAB_FILES_NAMES
					UpperCamelCase								=							PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase								=							['''input_ids''', '''attention_mask''']
					UpperCamelCase								=							ReformerTokenizer


					def __init__( self      :Dict					,	__UpperCamelCase      :Dict=None					,	__UpperCamelCase      :Optional[Any]=None					,	__UpperCamelCase      :Dict="</s>"					,	__UpperCamelCase      :Optional[Any]="<unk>"					,	__UpperCamelCase      :Union[str, Any]=[]					,	**__UpperCamelCase      :str					,	):
												super().__init__(
												    __UpperCamelCase					,	tokenizer_file=__UpperCamelCase					,	eos_token=__UpperCamelCase					,	unk_token=__UpperCamelCase					,	additional_special_tokens=__UpperCamelCase					,	**__UpperCamelCase					,	)

												A		=  vocab_file
												A		=  False if not self.vocab_file else True


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Optional[str] = None		):
												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																			    "tokenizer."		)

												if not os.path.isdir(__UpperCamelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
												A		=  os.path.join(
												    __UpperCamelCase					,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCamelCase		):
																			copyfile(self.vocab_file					,	__UpperCamelCase		)

												return (out_vocab_file,)


