
import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
						from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor



class 		UpperCamelCase__  :
			def __init__(self			: Optional[int] ,    snake_case_			: str ,    snake_case_			: List[Any]=3 ,    snake_case_			: int=3_2 ,    snake_case_			: Optional[int]=3 ,    snake_case_			: int=1_0 ,    snake_case_			: int=[1_0, 2_0, 3_0, 4_0] ,    snake_case_			: Dict=[1, 1, 2, 1] ,    snake_case_			: Optional[Any]=True ,    snake_case_			: str=True ,    snake_case_			: List[str]="relu" ,    snake_case_			: Optional[int]=3 ,    snake_case_			: Tuple=None ,    ):
				__a			:  Optional[Any]		    =  parent
				__a			:  Any		    =  batch_size
				__a			:  Any		    =  image_size
				__a			:  int		    =  num_channels
				__a			:  int		    =  embeddings_size
				__a			:  List[Any]		    =  hidden_sizes
				__a			:  List[Any]		    =  depths
				__a			:  int		    =  is_training
				__a			:  Union[str, Any]		    =  use_labels
				__a			:  Any		    =  hidden_act
				__a			:  Tuple		    =  num_labels
				__a			:  List[Any]		    =  scope
				__a			:  str		    =  len(_a       )
			def 				lowerCAmelCase      (self			: Any       ):
				__a			:  Optional[int]		    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

				__a			:  Any		    =  None
				if self.use_labels:
					__a			:  List[str]		    =  ids_tensor([self.batch_size] ,    self.num_labels       )

				__a			:  Optional[Any]		    =  self.get_config()

				return config, pixel_values, labels
			def 				lowerCAmelCase      (self			: Any       ):
				return RegNetConfig(
				    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 ,    )
			def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: Dict ,    snake_case_			: str ,    snake_case_			: Union[str, Any]       ):
				__a			:  Dict		    =  RegNetModel(config=_a       )
				model.to(_a       )
				model.eval()
				__a			:  Union[str, Any]		    =  model(_a       )
				# expected last hidden states: B, C, H // 32, W // 32
				self.parent.assertEqual(
				    result.last_hidden_state.shape ,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) ,    )
			def 				lowerCAmelCase      (self			: int ,    snake_case_			: int ,    snake_case_			: Dict ,    snake_case_			: Union[str, Any]       ):
				__a			:  int		    =  self.num_labels
				__a			:  Tuple		    =  RegNetForImageClassification(_a       )
				model.to(_a       )
				model.eval()
				__a			:  Optional[int]		    =  model(_a ,    labels=_a       )
				self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels)       )







			def 				lowerCAmelCase      (self			: Any       ):
				__a			:  Optional[Any]		    =  self.prepare_config_and_inputs()
				__a			:  Union[str, Any]		    =  config_and_inputs
				__a			:  Any		    =  {"""pixel_values""": pixel_values}
				return config, inputs_dict





@require_torch
class 		UpperCamelCase__  (	UpperCamelCase_   ,UpperCamelCase_   ,unittest.TestCase   ):
			_SCREAMING_SNAKE_CASE				:							List[Any]     	=				(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
			_SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				(
			    {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			_SCREAMING_SNAKE_CASE				:							Dict     	=				False
			_SCREAMING_SNAKE_CASE				:							Optional[int]     	=				False
			_SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				False
			_SCREAMING_SNAKE_CASE				:							Dict     	=				False
			def 				lowerCAmelCase      (self			: int       ):
				__a			:  Tuple		    =  RegNetModelTester(self       )
				__a			:  Dict		    =  ConfigTester(self ,    config_class=_a ,    has_text_modality=_a       )
			def 				lowerCAmelCase      (self			: str       ):
				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 				lowerCAmelCase      (self			: Union[str, Any]       ):
				return
			@unittest.skip(reason='''RegNet does not use inputs_embeds'''       )
			def 				lowerCAmelCase      (self			: Optional[int]       ):
				pass
			@unittest.skip(reason='''RegNet does not support input and output embeddings'''       )
			def 				lowerCAmelCase      (self			: List[Any]       ):
				pass
			def 				lowerCAmelCase      (self			: Any       ):
				__a			:  str		    =  self.model_tester.prepare_config_and_inputs_for_common()

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

					__a			:  List[Any]		    =  ["""pixel_values"""]
					self.assertListEqual(arg_names[:1] ,    _a       )
			def 				lowerCAmelCase      (self			: Dict       ):
				__a			:  List[Any]		    =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_a       )
			def 				lowerCAmelCase      (self			: Tuple       ):
				__a			:  List[str]		    =  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					__a			:  str		    =  model_class(config=_a       )
					for name, module in model.named_modules():
						if isinstance(_a ,    (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 				lowerCAmelCase      (self			: Optional[Any]       ):


				def check_hidden_states_output(snake_case_			: Any ,    snake_case_			: List[str] ,    snake_case_			: List[Any]       ):
					__a			:  Any		    =  model_class(_a       )
					model.to(_a       )
					model.eval()

					with torch.no_grad():
						__a			:  Optional[int]		    =  model(**self._prepare_for_class(_a ,    _a       )       )

					__a			:  int		    =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					__a			:  List[str]		    =  self.model_tester.num_stages
					self.assertEqual(len(_a       ) ,    expected_num_stages + 1       )

					# RegNet'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 // 2, self.model_tester.image_size // 2] ,    )

				__a			:  Union[str, Any]		    =  self.model_tester.prepare_config_and_inputs_for_common()
				__a			:  List[str]		    =  ["""basic""", """bottleneck"""]
				for model_class in self.all_model_classes:
					for layer_type in layers_type:
						__a			:  Tuple		    =  layer_type
						__a			:  Union[str, Any]		    =  True
						check_hidden_states_output(_a ,    _a ,    _a       )

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

						check_hidden_states_output(_a ,    _a ,    _a       )
			def 				lowerCAmelCase      (self			: Tuple       ):
				__a			:  Optional[int]		    =  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_a       )







			@slow
			def 				lowerCAmelCase      (self			: Optional[Any]       ):


				for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					__a			:  Optional[int]		    =  RegNetModel.from_pretrained(_a       )
					self.assertIsNotNone(_a       )





def        __UpperCamelCase					(    ):
	__a			:  Tuple		    =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
	return image



@require_torch
@require_vision
class 		UpperCamelCase__  (	unittest.TestCase   ):
			@cached_property
			def 				lowerCAmelCase      (self			: Union[str, Any]       ):
				return (
				    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
				    if is_vision_available()
				    else None
				)







			@slow
			def 				lowerCAmelCase      (self			: List[str]       ):
				__a			:  int		    =  RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(_a       )

				__a			:  Union[str, Any]		    =  self.default_image_processor
				__a			:  Optional[int]		    =  prepare_img()
				__a			:  int		    =  image_processor(images=_a ,    return_tensors='''pt'''       ).to(_a       )

				# forward pass
				with torch.no_grad():
					__a			:  Dict		    =  model(**_a       )

				# verify the logits
				__a			:  Tuple		    =  torch.Size((1, 1_0_0_0)       )
				self.assertEqual(outputs.logits.shape ,    _a       )

				__a			:  List[Any]		    =  torch.tensor([-0.4180, -1.5051, -3.4836]       ).to(_a       )

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


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowercase__          =logging.get_logger(__name__)

lowercase__          ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__          ={
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

lowercase__          ={'allegro/herbert-base-cased': 514}
lowercase__          ={}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE				:							Dict     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				HerbertTokenizer
   def __init__(self			: str ,    snake_case_			: Optional[Any]=None ,    snake_case_			: Tuple=None ,    snake_case_			: Dict=None ,    snake_case_			: List[str]="<s>" ,    snake_case_			: Any="<unk>" ,    snake_case_			: Optional[int]="<pad>" ,    snake_case_			: Optional[Any]="<mask>" ,    snake_case_			: List[Any]="</s>" ,    **snake_case_			: Optional[int] ,    ):
    super().__init__(
        snake_case_ ,    snake_case_ ,    tokenizer_file=snake_case_ ,    cls_token=snake_case_ ,    unk_token=snake_case_ ,    pad_token=snake_case_ ,    mask_token=snake_case_ ,    sep_token=snake_case_ ,    **snake_case_ ,    )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  int		    =  [self.cls_token_id]
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    if token_ids_a is None:
     return cls + token_ids_a + sep

    return cls + token_ids_a + sep + token_ids_a + sep
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):
    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 None:
     return [1] + ([0] * len(snake_case_       )) + [1]
    return [1] + ([0] * len(snake_case_       )) + [1] + ([0] * len(snake_case_       )) + [1]
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Optional[int]		    =  [self.sep_token_id]
    __a			:  int		    =  [self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep       ) * [0]
    return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]







   def 				lowerCAmelCase      (self			: int ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    __a			:  Any		    =  self._tokenizer.model.save(snake_case_ ,    name=snake_case_       )
    return tuple(snake_case_       )
