

'''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  UpperCAmelCase :

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=3   ,			SCREAMING_SNAKE_CASE_=32   ,			SCREAMING_SNAKE_CASE_=3   ,			SCREAMING_SNAKE_CASE_=10   ,			SCREAMING_SNAKE_CASE_=[8, 16, 32, 64]   ,			SCREAMING_SNAKE_CASE_=[1, 1, 2, 1]   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_="relu"   ,			SCREAMING_SNAKE_CASE_=3   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=["stage2", "stage3", "stage4"]   ,			SCREAMING_SNAKE_CASE_=[2, 3, 4]   ,			SCREAMING_SNAKE_CASE_=1   ,			)  ->     Union[str, Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      parent
							lowerCamelCase_  				=      batch_size
							lowerCamelCase_  				=      image_size
							lowerCamelCase_  				=      num_channels
							lowerCamelCase_  				=      embeddings_size
							lowerCamelCase_  				=      hidden_sizes
							lowerCamelCase_  				=      depths
							lowerCamelCase_  				=      is_training
							lowerCamelCase_  				=      use_labels
							lowerCamelCase_  				=      hidden_act
							lowerCamelCase_  				=      num_labels
							lowerCamelCase_  				=      scope
							lowerCamelCase_  				=      len(lowercase__		)
							lowerCamelCase_  				=      out_features
							lowerCamelCase_  				=      out_indices
							lowerCamelCase_  				=      num_groups
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							lowerCamelCase_  				=      None
							if self.use_labels:
												lowerCamelCase_  				=      ids_tensor([self.batch_size]   ,			self.num_labels		)

							lowerCamelCase_  				=      self.get_config()

							return config, pixel_values, labels
		def 			UpperCamelCase(					self		)  ->     Any:






							'''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 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      BitModel(config=lowercase__		)
							model.to(lowercase__		)
							model.eval()
							lowerCamelCase_  				=      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 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.num_labels
							lowerCamelCase_  				=      BitForImageClassification(lowercase__		)
							model.to(lowercase__		)
							model.eval()
							lowerCamelCase_  				=      model(lowercase__   ,			labels=lowercase__		)
							self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.num_labels)		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      BitBackbone(config=lowercase__		)
							model.to(lowercase__		)
							model.eval()
							lowerCamelCase_  				=      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
							lowerCamelCase_  				=      None
							lowerCamelCase_  				=      BitBackbone(config=lowercase__		)
							model.to(lowercase__		)
							model.eval()
							lowerCamelCase_  				=      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 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      self.prepare_config_and_inputs()
							lowerCamelCase_  				=      config_and_inputs
							lowerCamelCase_  				=      {"""pixel_values""": pixel_values}
							return config, inputs_dict


@require_torch
class  UpperCAmelCase (							snake_case_  , snake_case_  , unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
		SCREAMING_SNAKE_CASE_           =					(
		    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
		    if is_torch_available()
		    else {}
		)

		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      BitModelTester(self		)
							lowerCamelCase_  				=      ConfigTester(self   ,			config_class=lowercase__   ,			has_text_modality=lowercase__		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''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 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							return
		@unittest.skip(reason='Bit does not output attentions'		)
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							pass
		@unittest.skip(reason='Bit does not use inputs_embeds'		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							pass
		@unittest.skip(reason='Bit does not support input and output embeddings'		)
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							pass
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs_for_common()

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

												lowerCamelCase_  				=      ["""pixel_values"""]
												self.assertListEqual(arg_names[:1]   ,			lowercase__		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase__		)
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*lowercase__		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												lowerCamelCase_  				=      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 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							def check_hidden_states_output(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		):
												lowerCamelCase_  				=      model_class(lowercase__		)
												model.to(lowercase__		)
												model.eval()

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

												lowerCamelCase_  				=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												lowerCamelCase_  				=      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]   ,			)

							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs_for_common()
							lowerCamelCase_  				=      ["""preactivation""", """bottleneck"""]
							for model_class in self.all_model_classes:
												for layer_type in layers_type:
																	lowerCamelCase_  				=      layer_type
																	lowerCamelCase_  				=      True
																	check_hidden_states_output(lowercase__   ,			lowercase__   ,			lowercase__		)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	lowerCamelCase_  				=      True

																	check_hidden_states_output(lowercase__   ,			lowercase__   ,			lowercase__		)
		@unittest.skip(reason='Bit does not use feedforward chunking'		)
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							pass
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowercase__		)






		@slow
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



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







def   _UpperCamelCase	(		)       ->			Optional[int]:
					lowerCamelCase_  				=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
					return image


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

		'''simple docstring'''






		@cached_property
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



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






		@slow
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(lowercase__		)

							lowerCamelCase_  				=      self.default_image_processor
							lowerCamelCase_  				=      prepare_img()
							lowerCamelCase_  				=      image_processor(images=lowercase__   ,			return_tensors='pt'		).to(lowercase__		)

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

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

							lowerCamelCase_  				=      torch.tensor([[-0.6_526, -0.5_263, -1.4_398]]		).to(lowercase__		)

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


@require_torch
class  UpperCAmelCase (							snake_case_  , unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					(BitBackbone,) if is_torch_available() else ()
		SCREAMING_SNAKE_CASE_           =					BitConfig

		SCREAMING_SNAKE_CASE_           =					False
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							lowerCamelCase_  				=      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}
