
import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
  from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

class __snake_case		:
    def __init__(  self		,snake_case		,snake_case=3		,snake_case=32		,snake_case=3		,snake_case=10		,snake_case=[8, 16, 32, 64]		,snake_case=[1, 1, 2, 1]		,snake_case=True		,snake_case=True		,snake_case="relu"		,snake_case=3		,snake_case=None		,snake_case=["stage2", "stage3", "stage4"]		,snake_case=[2, 3, 4]		,snake_case=1		,):



           '''simple docstring'''







           lowercase							:					Tuple	     =				parent
           lowercase							:					int	     =				batch_size
           lowercase							:					int	     =				image_size
           lowercase							:					Union[str, Any]	     =				num_channels
           lowercase							:					str	     =				embeddings_size
           lowercase							:					List[str]	     =				hidden_sizes
           lowercase							:					Union[str, Any]	     =				depths
           lowercase							:					Tuple	     =				is_training
           lowercase							:					Union[str, Any]	     =				use_labels
           lowercase							:					int	     =				hidden_act
           lowercase							:					List[Any]	     =				num_labels
           lowercase							:					str	     =				scope
           lowercase							:					int	     =				len(snake_case				)
           lowercase							:					Tuple	     =				out_features
           lowercase							:					Union[str, Any]	     =				out_indices
           lowercase							:					Optional[Any]	     =				num_groups
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           lowercase							:					int	     =				None
           if self.use_labels:
                  lowercase							:					str	     =				ids_tensor([self.batch_size]		,self.num_labels				)

           lowercase							:					Tuple	     =				self.get_config()

           return config, pixel_values, labels
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return BitConfig(
               num_channels=self.num_channels		,embeddings_size=self.embeddings_size		,hidden_sizes=self.hidden_sizes		,depths=self.depths		,hidden_act=self.hidden_act		,num_labels=self.num_labels		,out_features=self.out_features		,out_indices=self.out_indices		,num_groups=self.num_groups		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				BitModel(config=snake_case				)
           model.to(snake_case				)
           model.eval()
           lowercase							:					Optional[int]	     =				model(snake_case				)
           self.parent.assertEqual(
               result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					int	     =				self.num_labels
           lowercase							:					str	     =				BitForImageClassification(snake_case				)
           model.to(snake_case				)
           model.eval()
           lowercase							:					Tuple	     =				model(snake_case		,labels=snake_case				)
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				BitBackbone(config=snake_case				)
           model.to(snake_case				)
           model.eval()
           lowercase							:					int	     =				model(snake_case				)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps				)		,len(config.out_features				)				)
           self.parent.assertListEqual(list(result.feature_maps[0].shape				)		,[self.batch_size, self.hidden_sizes[1], 4, 4]				)

           # verify channels
           self.parent.assertEqual(len(model.channels				)		,len(config.out_features				)				)
           self.parent.assertListEqual(model.channels		,config.hidden_sizes[1:]				)

           # verify backbone works with out_features=None
           lowercase							:					List[str]	     =				None
           lowercase							:					Dict	     =				BitBackbone(config=snake_case				)
           model.to(snake_case				)
           model.eval()
           lowercase							:					str	     =				model(snake_case				)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps				)		,1				)
           self.parent.assertListEqual(list(result.feature_maps[0].shape				)		,[self.batch_size, self.hidden_sizes[-1], 1, 1]				)

           # verify channels
           self.parent.assertEqual(len(model.channels				)		,1				)
           self.parent.assertListEqual(model.channels		,[config.hidden_sizes[-1]]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				self.prepare_config_and_inputs()
           lowercase     ,      lowercase     ,      lowercase							:					int	     =				config_and_inputs
           lowercase							:					str	     =				{"""pixel_values""": pixel_values}
           return config, inputs_dict






@require_torch
class __snake_case		(				lowerCAmelCase ,     lowerCAmelCase ,     unittest.TestCase					):
    _a		:						str=    (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
    _a		:						Optional[Any]=    (
        {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
        if is_torch_available()
        else {}
    )

    _a		:						Optional[int]=    False
    _a		:						Dict=    False
    _a		:						Optional[int]=    False
    _a		:						int=    False
    _a		:						Union[str, Any]=    False
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				BitModelTester(self				)
           lowercase							:					str	     =				ConfigTester(self		,config_class=snake_case		,has_text_modality=snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return
    @unittest.skip(reason="""Bit does not output attentions"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    @unittest.skip(reason="""Bit does not use inputs_embeds"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    @unittest.skip(reason="""Bit does not support input and output embeddings"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase     ,      lowercase							:					Dict	     =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowercase							:					Any	     =				model_class(snake_case				)
                  lowercase							:					Optional[int]	     =				inspect.signature(model.forward				)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  lowercase							:					str	     =				[*signature.parameters.keys()]

                  lowercase							:					Dict	     =				["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase     ,      lowercase							:					Optional[Any]	     =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  lowercase							:					List[str]	     =				model_class(config=snake_case				)
                  for name, module in model.named_modules():
                         if isinstance(snake_case		,(nn.BatchNormad, nn.GroupNorm)				):
                                self.assertTrue(
                                    torch.all(module.weight == 1				)		,msg=f"Parameter {name} of model {model_class} seems not properly initialized"		,)
                                self.assertTrue(
                                    torch.all(module.bias == 0				)		,msg=f"Parameter {name} of model {model_class} seems not properly initialized"		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           def check_hidden_states_output(snake_case		,snake_case		,snake_case				):
                  lowercase							:					List[Any]	     =				model_class(snake_case				)
                  model.to(snake_case				)
                  model.eval()

                  with torch.no_grad():
                         lowercase							:					List[Any]	     =				model(**self._prepare_for_class(snake_case		,snake_case				)				)

                  lowercase							:					Dict	     =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                  lowercase							:					Tuple	     =				self.model_tester.num_stages
                  self.assertEqual(len(snake_case				)		,expected_num_stages + 1				)

                  # Bit's feature maps are of shape (batch_size, num_channels, height, width)
                  self.assertListEqual(
                      list(hidden_states[0].shape[-2:]				)		,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,)

           lowercase     ,      lowercase							:					List[Any]	     =				self.model_tester.prepare_config_and_inputs_for_common()
           lowercase							:					Optional[int]	     =				["""preactivation""", """bottleneck"""]
           for model_class in self.all_model_classes:
                  for layer_type in layers_type:
                         lowercase							:					str	     =				layer_type
                         lowercase							:					str	     =				True
                         check_hidden_states_output(snake_case		,snake_case		,snake_case				)

                         # check that output_hidden_states also work using config
                         del inputs_dict["output_hidden_states"]
                         lowercase							:					Any	     =				True

                         check_hidden_states_output(snake_case		,snake_case		,snake_case				)
    @unittest.skip(reason="""Bit does not use feedforward chunking"""				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           pass
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*snake_case				)
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowercase							:					List[str]	     =				BitModel.from_pretrained(snake_case				)
                  self.assertIsNotNone(snake_case				)







def 						_snake_case(  )			->   str:
       lowercase							:					Tuple	     =				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
       return image

@require_torch
@require_vision
class __snake_case		(				unittest.TestCase					):
    @cached_property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return (
               BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				) if is_vision_available() else None
           )
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(snake_case				)

           lowercase							:					Any	     =				self.default_image_processor
           lowercase							:					Union[str, Any]	     =				prepare_img()
           lowercase							:					str	     =				image_processor(images=snake_case		,return_tensors="""pt"""				).to(snake_case				)

           # forward pass
           with torch.no_grad():
                  lowercase							:					Any	     =				model(**snake_case				)

           # verify the logits
           lowercase							:					Optional[int]	     =				torch.Size((1, 1000)				)
           self.assertEqual(outputs.logits.shape		,snake_case				)

           lowercase							:					List[Any]	     =				torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]				).to(snake_case				)

           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,snake_case		,atol=1e-4				)				)






@require_torch
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						List[str]=    (BitBackbone,) if is_torch_available() else ()
    _a		:						List[Any]=    BitConfig

    _a		:						Tuple=    False
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				BitModelTester(self				)


import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowercase  : Union[str, Any]						   =							logging.get_logger(__name__)

lowercase  : Union[str, Any]						   =							{"""vocab_file""": """spiece.model"""}

lowercase  : Any						   =							{
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

lowercase  : List[Any]						   =							{
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
lowercase  : Optional[int]						   =							0
lowercase  : Any						   =							1
lowercase  : Any						   =							2
lowercase  : str						   =							3
lowercase  : Optional[Any]						   =							4

class __snake_case		(				lowerCAmelCase					):
    _a		:						Optional[Any]=    VOCAB_FILES_NAMES
    _a		:						Optional[Any]=    PRETRAINED_VOCAB_FILES_MAP
    _a		:						List[str]=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _a		:						Union[str, Any]=    "left"
    def __init__(  self		,snake_case		,snake_case=False		,snake_case=True		,snake_case=False		,snake_case="<s>"		,snake_case="</s>"		,snake_case="<unk>"		,snake_case="<sep>"		,snake_case="<pad>"		,snake_case="<cls>"		,snake_case="<mask>"		,snake_case=["<eop>", "<eod>"]		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					str	     =				AddedToken(snake_case		,lstrip=snake_case		,rstrip=snake_case				) if isinstance(snake_case		,snake_case				) else mask_token

           lowercase							:					List[str]	     =				{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=snake_case		,remove_space=snake_case		,keep_accents=snake_case		,bos_token=snake_case		,eos_token=snake_case		,unk_token=snake_case		,sep_token=snake_case		,pad_token=snake_case		,cls_token=snake_case		,mask_token=snake_case		,additional_special_tokens=snake_case		,sp_model_kwargs=self.sp_model_kwargs		,**snake_case		,)

           lowercase							:					str	     =				3

           lowercase							:					str	     =				do_lower_case
           lowercase							:					List[Any]	     =				remove_space
           lowercase							:					Dict	     =				keep_accents
           lowercase							:					Union[str, Any]	     =				vocab_file

           lowercase							:					Any	     =				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
           self.sp_model.Load(snake_case				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return len(self.sp_model				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				{self.convert_ids_to_tokens(snake_case				): i for i in range(self.vocab_size				)}
           vocab.update(self.added_tokens_encoder				)
           return vocab
    def __getstate__(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.__dict__.copy()
           lowercase							:					List[str]	     =				None
           return state
    def __setstate__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				d

           # for backward compatibility
           if not hasattr(self		,"""sp_model_kwargs"""				):
                  lowercase							:					List[str]	     =				{}

           lowercase							:					List[str]	     =				spm.SentencePieceProcessor(**self.sp_model_kwargs				)
           self.sp_model.Load(self.vocab_file				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           if self.remove_space:
                  lowercase							:					Optional[int]	     =				""" """.join(inputs.strip().split()				)
           else:
                  lowercase							:					Optional[int]	     =				inputs
           lowercase							:					Dict	     =				outputs.replace("""``"""		,"""\""""				).replace("""''"""		,"""\""""				)

           if not self.keep_accents:
                  lowercase							:					int	     =				unicodedata.normalize("""NFKD"""		,snake_case				)
                  lowercase							:					Any	     =				"""""".join([c for c in outputs if not unicodedata.combining(snake_case				)]				)
           if self.do_lower_case:
                  lowercase							:					Any	     =				outputs.lower()

           return outputs
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.preprocess_text(snake_case				)
           lowercase							:					Any	     =				self.sp_model.encode(snake_case		,out_type=snake_case				)
           lowercase							:					Tuple	     =				[]
           for piece in pieces:
                  if len(snake_case				) > 1 and piece[-1] == str(""","""				) and piece[-2].isdigit():
                         lowercase							:					Dict	     =				self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case		,""""""				)				)
                         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                if len(cur_pieces[0]				) == 1:
                                       lowercase							:					Any	     =				cur_pieces[1:]
                                else:
                                       lowercase							:					List[str]	     =				cur_pieces[0][1:]
                         cur_pieces.append(piece[-1]				)
                         new_pieces.extend(snake_case				)
                  else:
                         new_pieces.append(snake_case				)

           return new_pieces
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           return self.sp_model.PieceToId(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           return self.sp_model.IdToPiece(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				"""""".join(snake_case				).replace(snake_case		,""" """				).strip()
           return out_string
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = False		,snake_case = None		,snake_case = True		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					List[str]	     =				kwargs.pop("""use_source_tokenizer"""		,snake_case				)

           lowercase							:					List[str]	     =				self.convert_ids_to_tokens(snake_case		,skip_special_tokens=snake_case				)

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           lowercase							:					Optional[int]	     =				[]
           lowercase							:					Optional[int]	     =				[]
           for token in filtered_tokens:
                  if skip_special_tokens and token in self.all_special_ids:
                         continue
                  if token in self.added_tokens_encoder:
                         if current_sub_text:
                                sub_texts.append(self.convert_tokens_to_string(snake_case				)				)
                                lowercase							:					Union[str, Any]	     =				[]
                         sub_texts.append(snake_case				)
                  else:
                         current_sub_text.append(snake_case				)
           if current_sub_text:
                  sub_texts.append(self.convert_tokens_to_string(snake_case				)				)

           # Mimic the behavior of the Rust tokenizer:
           # By default, there are no spaces between special tokens
           lowercase							:					Any	     =				"""""".join(snake_case				)

           lowercase							:					str	     =				(
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                  lowercase							:					int	     =				self.clean_up_tokenization(snake_case				)
                  return clean_text
           else:
                  return text
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					int	     =				[self.sep_token_id]
           lowercase							:					str	     =				[self.cls_token_id]
           if token_ids_a is None:
                  return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None		,snake_case = False				):



           '''simple docstring'''







           if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=snake_case		,token_ids_a=snake_case		,already_has_special_tokens=snake_case				)

           if token_ids_a is not None:
                  return ([0] * len(snake_case				)) + [1] + ([0] * len(snake_case				)) + [1, 1]
           return ([0] * len(snake_case				)) + [1, 1]
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				[self.sep_token_id]
           lowercase							:					Any	     =				[2]

           if token_ids_a is None:
                  return len(token_ids_a + sep				) * [0] + cls_segment_id
           return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None				):



           '''simple docstring'''







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

           if os.path.abspath(self.vocab_file				) != os.path.abspath(snake_case				) and os.path.isfile(self.vocab_file				):
                  copyfile(self.vocab_file		,snake_case				)
           elif not os.path.isfile(self.vocab_file				):
                  with open(snake_case		,"""wb"""				) as fi:
                         lowercase							:					str	     =				self.sp_model.serialized_model_proto()
                         fi.write(snake_case				)

           return (out_vocab_file,)
