





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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 transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
      from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor

class 						UpperCamelCase__					( __lowercase  ):



     '''simple docstring'''

     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.config_class(**self.inputs_dict			)
           self.parent.assertTrue(hasattr(__A		,"""hidden_sizes"""			)			)
           self.parent.assertTrue(hasattr(__A		,"""num_encoder_blocks"""			)			)

class 						UpperCamelCase__					:



     '''simple docstring'''

     def __init__(				self		:       Optional[Any]		,lowerCamelCase__		:       Union[str, Any]		,lowerCamelCase__		:       Any=13		,lowerCamelCase__		:       List[Any]=64		,lowerCamelCase__		:       Tuple=3		,lowerCamelCase__		:       Tuple=4		,lowerCamelCase__		:       List[str]=[2, 2, 2, 2]		,lowerCamelCase__		:       Union[str, Any]=[8, 4, 2, 1]		,lowerCamelCase__		:       List[str]=[16, 32, 64, 128]		,lowerCamelCase__		:       Optional[Any]=[1, 4, 8, 16]		,lowerCamelCase__		:       Any=False		,lowerCamelCase__		:       Dict=True		,lowerCamelCase__		:       Any="gelu"		,lowerCamelCase__		:       Optional[Any]=0.1		,lowerCamelCase__		:       str=0.02		,lowerCamelCase__		:       Any=3		,lowerCamelCase__		:       Optional[Any]=None		,)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		parent
           SCREAMING_SNAKE_CASE						=		batch_size
           SCREAMING_SNAKE_CASE						=		image_size
           SCREAMING_SNAKE_CASE						=		num_channels
           SCREAMING_SNAKE_CASE						=		num_encoder_blocks
           SCREAMING_SNAKE_CASE						=		sr_ratios
           SCREAMING_SNAKE_CASE						=		depths
           SCREAMING_SNAKE_CASE						=		hidden_sizes
           SCREAMING_SNAKE_CASE						=		downsampling_rates
           SCREAMING_SNAKE_CASE						=		is_training
           SCREAMING_SNAKE_CASE						=		use_labels
           SCREAMING_SNAKE_CASE						=		hidden_act
           SCREAMING_SNAKE_CASE						=		hidden_dropout_prob
           SCREAMING_SNAKE_CASE						=		initializer_range
           SCREAMING_SNAKE_CASE						=		num_labels
           SCREAMING_SNAKE_CASE						=		scope






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						List[str]:




           '''simple docstring'''


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

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

           SCREAMING_SNAKE_CASE						=		PoolFormerConfig(
               image_size=self.image_size		,num_channels=self.num_channels		,num_encoder_blocks=self.num_encoder_blocks		,depths=self.depths		,hidden_sizes=self.hidden_sizes		,hidden_act=self.hidden_act		,hidden_dropout_prob=self.hidden_dropout_prob		,initializer_range=self.initializer_range		,)

           return config, pixel_values, labels






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict		,lowerCamelCase__		:       List[Any]		,lowerCamelCase__		:       Optional[Any]		,lowerCamelCase__		:       Any			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		PoolFormerModel(config=__A			)
           model.to(__A			)
           model.eval()
           SCREAMING_SNAKE_CASE						=		model(__A			)
           SCREAMING_SNAKE_CASE						=		SCREAMING_SNAKE_CASE						=		self.image_size // 32.0
           self.parent.assertEqual(
               result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]			)	->						Union[str, Any]:




           '''simple docstring'''


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

@require_torch
class 						UpperCamelCase__					( __lowercase				, __lowercase				, unittest.TestCase  ):



     '''simple docstring'''

     __snake_case       :      Tuple						      =     (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     __snake_case       :      List[str]						      =     (
         {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     __snake_case       :      Optional[Any]						      =     False
     __snake_case       :      List[str]						      =     False
     __snake_case       :      int						      =     False
     __snake_case       :      Dict						      =     False
     __snake_case       :      int						      =     False






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		PoolFormerModelTester(self			)
           SCREAMING_SNAKE_CASE						=		PoolFormerConfigTester(self		,config_class=__A			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						int:




           '''simple docstring'''


           self.config_tester.run_common_tests()






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]			)	->						Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__A			)






     @unittest.skip("""PoolFormer does not use inputs_embeds"""			)
     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						List[str]:




           '''simple docstring'''


           pass






     @unittest.skip("""PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"""			)
     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						List[Any]:




           '''simple docstring'''


           pass






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Optional[Any]:




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       List[Any]			):
                 SCREAMING_SNAKE_CASE						=		model_class(__A			)
                 model.to(__A			)
                 model.eval()

                 with torch.no_grad():
                       SCREAMING_SNAKE_CASE						=		model(**self._prepare_for_class(__A		,__A			)			)

                 SCREAMING_SNAKE_CASE						=		outputs.hidden_states

                 SCREAMING_SNAKE_CASE						=		self.model_tester.num_encoder_blocks
                 self.assertEqual(len(__A			)		,__A			)

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:]			)		,[
                         self.model_tester.hidden_sizes[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]		,)

           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE						=		True
                 check_hidden_states_output(__A		,__A		,__A			)

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

                 check_hidden_states_output(__A		,__A		,__A			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]			)	->						Any:




           '''simple docstring'''


           if not self.model_tester.is_training:
                 return

           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE						=		True

           for model_class in self.all_model_classes:
                 if model_class in get_values(__A			):
                       continue
                 SCREAMING_SNAKE_CASE						=		model_class(__A			)
                 model.to(__A			)
                 model.train()
                 SCREAMING_SNAKE_CASE						=		self._prepare_for_class(__A		,__A		,return_labels=__A			)
                 SCREAMING_SNAKE_CASE						=		model(**__A			).loss
                 loss.backward()






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple			)	->						Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE						=		["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]		,__A			)






     @slow
     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Union[str, Any]:




           '''simple docstring'''


           for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE						=		PoolFormerModel.from_pretrained(__A			)
                 self.assertIsNotNone(__A			)



def   __lowercase  ( )					->  Optional[Any]:


      '''simple docstring'''


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



@require_torch
class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     @slow
     def 							SCREAMING_SNAKE_CASE__    (				self		:       Tuple			)	->						Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		PoolFormerImageProcessor()
           SCREAMING_SNAKE_CASE						=		PoolFormerForImageClassification.from_pretrained("""sail/poolformer_s12"""			).to(__A			)

           SCREAMING_SNAKE_CASE						=		image_processor(images=prepare_img()		,return_tensors="""pt"""			).to(__A			)

           # forward pass
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE						=		model(**__A			)

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

           SCREAMING_SNAKE_CASE						=		torch.tensor([-0.6113, 0.1685, -0.0492]			).to(__A			)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]		,__A		,atol=1e-4			)			)



'''simple docstring'''

import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


lowercase_  		=			logging.get_logger(__name__)
class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   Any							=				'semantic'






		def __init__( self		,    __A=1_0000		,    __A=True		,    __A=768		,    __A=False		,    __A=False		,    __A=False		,    __A=False		,    __A=0.7		,    __A=True		,    __A=1_0048		,    __A=12_9595		,    __A=12_9599		,    __A=1_0000		,    __A=256		,    __A=49.9		,    **__A		,    )			->  Any:
								super().__init__(
								    temperature=__A		,    do_sample=__A		,    eos_token_id=__A		,    renormalize_logits=__A		,    max_new_tokens=__A		,    output_scores=__A		,    return_dict_in_generate=__A		,    output_hidden_states=__A		,    output_attentions=__A		,    **__A		,    )

								_lowerCAmelCase					    =text_encoding_offset
								_lowerCAmelCase					    =text_pad_token
								_lowerCAmelCase					    =eos_token_id
								_lowerCAmelCase					    =semantic_infer_token
								_lowerCAmelCase					    =semantic_vocab_size
								_lowerCAmelCase					    =max_input_semantic_length
								_lowerCAmelCase					    =semantic_rate_hz

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   Any							=				'coarse_acoustics'






		def __init__( self		,    __A=True		,    __A=False		,    __A=False		,    __A=False		,    __A=False		,    __A=0.7		,    __A=True		,    __A=1_2048		,    __A=75		,    __A=2		,    __A=1_2050		,    __A=256		,    __A = 630		,    __A = 60		,    **__A		,    )			->  Union[str, Any]:
								super().__init__(
								    temperature=__A		,    do_sample=__A		,    renormalize_logits=__A		,    output_scores=__A		,    return_dict_in_generate=__A		,    output_hidden_states=__A		,    output_attentions=__A		,    **__A		,    )

								_lowerCAmelCase					    =coarse_semantic_pad_token
								_lowerCAmelCase					    =coarse_rate_hz
								_lowerCAmelCase					    =n_coarse_codebooks
								_lowerCAmelCase					    =coarse_infer_token
								_lowerCAmelCase					    =max_coarse_input_length
								_lowerCAmelCase					    =max_coarse_history
								_lowerCAmelCase					    =sliding_window_len

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   str							=				'fine_acoustics'






		def __init__( self		,    __A=0.5		,    __A=512		,    __A=1024		,    __A=8		,    **__A		,    )			->  int:
								super().__init__(temperature=__A					)

								_lowerCAmelCase					    =max_fine_history_length
								_lowerCAmelCase					    =max_fine_input_length
								_lowerCAmelCase					    =n_fine_codebooks

class 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""
		lowercase :   int							=				'bark'
		lowercase :   Optional[Any]							=				True






		def __init__( self		,    __A = None		,    __A = None		,    __A = None		,    __A=2_4000		,    __A=1024		,    **__A		,    )			->  Optional[int]:
								if semantic_config is None:
														_lowerCAmelCase					    ={}
														logger.info('semantic_config is None. initializing the semantic model with default values.'					)

								if coarse_acoustics_config is None:
														_lowerCAmelCase					    ={}
														logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'					)

								if fine_acoustics_config is None:
														_lowerCAmelCase					    ={}
														logger.info('fine_acoustics_config is None. initializing the fine model with default values.'					)

								_lowerCAmelCase					    =BarkSemanticGenerationConfig(**__A					)
								_lowerCAmelCase					    =BarkCoarseGenerationConfig(**__A					)
								_lowerCAmelCase					    =BarkFineGenerationConfig(**__A					)

								_lowerCAmelCase					    =sample_rate
								_lowerCAmelCase					    =codebook_size






		@classmethod
		def 		UpperCamelCase__							( cls		,    __A		,    __A		,    __A		,    **__A		,    )			->  List[Any]:
								return cls(
								    semantic_config=semantic_config.to_dict()		,    coarse_acoustics_config=coarse_acoustics_config.to_dict()		,    fine_acoustics_config=fine_acoustics_config.to_dict()		,    **__A		,    )






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								_lowerCAmelCase					    =copy.deepcopy(self.__dict__					)

								_lowerCAmelCase					    =self.semantic_config.to_dict()
								_lowerCAmelCase					    =self.coarse_acoustics_config.to_dict()
								_lowerCAmelCase					    =self.fine_acoustics_config.to_dict()

								_lowerCAmelCase					    =self.__class__.model_type
								return output





