




'''simple docstring'''


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      SCREAMING_SNAKE_CASE    :






      def __init__( self   :  List[str]						,						lowercase__   :  Tuple						,						lowercase__   :  Dict=3						,						lowercase__   :  Union[str, Any]=32						,						lowercase__   :  Union[str, Any]=3						,						lowercase__   :  Optional[Any]=10						,						lowercase__   :  Any=[8, 16, 32, 64]						,						lowercase__   :  List[str]=[1, 1, 2, 1]						,						lowercase__   :  Optional[int]=True						,						lowercase__   :  int=True						,						lowercase__   :  Dict="relu"						,						lowercase__   :  int=3						,						lowercase__   :  Dict=None						,						lowercase__   :  Any=["stage2", "stage3", "stage4"]						,						lowercase__   :  Optional[Any]=[2, 3, 4]						,						lowercase__   :  List[str]=1						,						):


           '''simple docstring'''

           a_     :							int     						=  parent
           a_     :							Optional[Any]     						=  batch_size
           a_     :							Dict     						=  image_size
           a_     :							List[str]     						=  num_channels
           a_     :							Dict     						=  embeddings_size
           a_     :							List[Any]     						=  hidden_sizes
           a_     :							str     						=  depths
           a_     :							str     						=  is_training
           a_     :							Optional[int]     						=  use_labels
           a_     :							Any     						=  hidden_act
           a_     :							str     						=  num_labels
           a_     :							Optional[Any]     						=  scope
           a_     :							Optional[Any]     						=  len(lowercase__       )
           a_     :							Tuple     						=  out_features
           a_     :							Optional[Any]     						=  out_indices
           a_     :							str     						=  num_groups






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							List[str]     						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

           a_     :							Optional[Any]     						=  None
           if self.use_labels:
                a_     :							Any     						=  ids_tensor([self.batch_size]						,						self.num_labels       )

           a_     :							List[Any]     						=  self.get_config()

           return config, pixel_values, labels






      def 							lowercase_						( self   :  str       ):


           '''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 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  List[str]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           a_     :							str     						=  BitModel(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							Union[str, Any]     						=  model(lowercase__       )
           self.parent.assertEqual(
               result.last_hidden_state.shape						,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,						)






      def 							lowercase_						( self   :  str						,						lowercase__   :  str						,						lowercase__   :  Any						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.num_labels
           a_     :							Union[str, Any]     						=  BitForImageClassification(lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							int     						=  model(lowercase__						,						labels=lowercase__       )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_labels)       )






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Optional[Any]						,						lowercase__   :  List[Any]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           a_     :							List[str]     						=  BitBackbone(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							List[str]     						=  model(lowercase__       )

           # 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
           a_     :							int     						=  None
           a_     :							List[str]     						=  BitBackbone(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							Optional[Any]     						=  model(lowercase__       )

           # 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 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

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







@require_torch
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Any   							=					(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
      __magic_name__  :       str   							=					(
          {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
          if is_torch_available()
          else {}
      )

      __magic_name__  :       Tuple   							=					False
      __magic_name__  :       Any   							=					False
      __magic_name__  :       str   							=					False
      __magic_name__  :       List[Any]   							=					False
      __magic_name__  :       Union[str, Any]   							=					False






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							Tuple     						=  BitModelTester(self       )
           a_     :							Union[str, Any]     						=  ConfigTester(self						,						config_class=lowercase__						,						has_text_modality=lowercase__       )






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''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 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           return






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


           '''simple docstring'''

           pass






      @unittest.skip(reason="""Bit does not use inputs_embeds"""       )
      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           pass






      @unittest.skip(reason="""Bit does not support input and output embeddings"""       )
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           pass






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

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

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

                a_     :							str     						=  ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]						,						lowercase__       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Tuple     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase__       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*lowercase__       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_						,     a_     :							Optional[int]     						=  self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                a_     :							List[str]     						=  model_class(config=lowercase__       )
                for name, module in model.named_modules():
                     if isinstance(lowercase__						,						(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 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           def check_hidden_states_output(lowercase__   :  Union[str, Any]						,						lowercase__   :  int						,						lowercase__   :  Tuple       ):
                a_     :							Optional[Any]     						=  model_class(lowercase__       )
                model.to(lowercase__       )
                model.eval()

                with torch.no_grad():
                     a_     :							Any     						=  model(**self._prepare_for_class(lowercase__						,						lowercase__       )       )

                a_     :							Tuple     						=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                a_     :							List[str]     						=  self.model_tester.num_stages
                self.assertEqual(len(lowercase__       )						,						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]						,						)

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

                     # check that output_hidden_states also work using config
                     del inputs_dict["output_hidden_states"]
                     a_     :							Union[str, Any]     						=  True

                     check_hidden_states_output(lowercase__						,						lowercase__						,						lowercase__       )






      @unittest.skip(reason="""Bit does not use feedforward chunking"""       )
      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           pass






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							str     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowercase__       )






      @slow
      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_     :							List[Any]     						=  BitModel.from_pretrained(lowercase__       )
                self.assertIsNotNone(lowercase__       )



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							int     						=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
     return image







@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      @cached_property
      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           return (
               BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ) if is_vision_available() else None
           )






      @slow
      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							int     						=  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(lowercase__       )

           a_     :							Any     						=  self.default_image_processor
           a_     :							Any     						=  prepare_img()
           a_     :							int     						=  image_processor(images=lowercase__						,						return_tensors="""pt"""       ).to(lowercase__       )

           # forward pass
           with torch.no_grad():
                a_     :							int     						=  model(**lowercase__       )

           # verify the logits
           a_     :							int     						=  torch.Size((1, 1000)       )
           self.assertEqual(outputs.logits.shape						,						lowercase__       )

           a_     :							Any     						=  torch.tensor([[-0.6526, -0.5263, -1.4398]]       ).to(lowercase__       )

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







@require_torch
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Tuple   							=					(BitBackbone,) if is_torch_available() else ()
      __magic_name__  :       List[Any]   							=					BitConfig

      __magic_name__  :       Union[str, Any]   							=					False






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							str     						=  BitModelTester(self       )






'''simple docstring'''


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class      SCREAMING_SNAKE_CASE    (						tf.keras.layers.Layer				):






      def __init__( self   :  List[Any]						,						lowercase__   :  Dict[str, int]						,						lowercase__   :  List[str]						,						lowercase__   :  int = None						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           super().__init__()
           a_     :							Optional[Any]     						=  pad_token_id
           a_     :							str     						=  max_length
           a_     :							List[str]     						=  vocab
           a_     :							Union[str, Any]     						=  merges
           a_     :							Union[str, Any]     						=  BytePairTokenizer(lowercase__						,						lowercase__						,						sequence_length=lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  List[Any]						,						lowercase__   :  GPTaTokenizer						,						*lowercase__   :  Optional[Any]						,						**lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							str     						=  [""" """.join(lowercase__       ) for m in tokenizer.bpe_ranks.keys()]
           a_     :							Any     						=  tokenizer.get_vocab()
           return cls(lowercase__						,						lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Union[str, os.PathLike]						,						*lowercase__   :  str						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  GPTaTokenizer.from_pretrained(lowercase__						,						*lowercase__						,						**lowercase__       )
           return cls.from_tokenizer(lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           return cls(**lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.tf_tokenizer(lowercase__       )
           a_     :							int     						=  tf.ones_like(lowercase__       )

           if self.pad_token_id is not None:
                # pad the tokens up to max length
                a_     :							int     						=  max_length if max_length is not None else self.max_length

                if max_length is not None:
                     a_						,     a_     :							int     						=  pad_model_inputs(
                         lowercase__						,						max_seq_length=lowercase__						,						pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}
