



import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import DonutSwinModel
 from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST






class 	lowercase		:






 """simple docstring"""


 def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Tuple=13		,						__UpperCAmelCase			:		Dict=32		,						__UpperCAmelCase			:		Union[str, Any]=2		,						__UpperCAmelCase			:		str=3		,						__UpperCAmelCase			:		List[str]=16		,						__UpperCAmelCase			:		List[Any]=[1, 2, 1]		,						__UpperCAmelCase			:		Dict=[2, 2, 4]		,						__UpperCAmelCase			:		int=2		,						__UpperCAmelCase			:		Any=2.0		,						__UpperCAmelCase			:		Tuple=True		,						__UpperCAmelCase			:		Any=0.0		,						__UpperCAmelCase			:		Optional[int]=0.0		,						__UpperCAmelCase			:		Dict=0.1		,						__UpperCAmelCase			:		Tuple="gelu"		,						__UpperCAmelCase			:		str=False		,						__UpperCAmelCase			:		str=True		,						__UpperCAmelCase			:		Optional[int]=0.02		,						__UpperCAmelCase			:		Union[str, Any]=1E-5		,						__UpperCAmelCase			:		Union[str, Any]=True		,						__UpperCAmelCase			:		Optional[Any]=None		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		Optional[Any]=10		,						__UpperCAmelCase			:		Optional[int]=8		,						)    ->				Dict:
       UpperCAmelCase_=     parent
       UpperCAmelCase_=     batch_size
       UpperCAmelCase_=     image_size
       UpperCAmelCase_=     patch_size
       UpperCAmelCase_=     num_channels
       UpperCAmelCase_=     embed_dim
       UpperCAmelCase_=     depths
       UpperCAmelCase_=     num_heads
       UpperCAmelCase_=     window_size
       UpperCAmelCase_=     mlp_ratio
       UpperCAmelCase_=     qkv_bias
       UpperCAmelCase_=     hidden_dropout_prob
       UpperCAmelCase_=     attention_probs_dropout_prob
       UpperCAmelCase_=     drop_path_rate
       UpperCAmelCase_=     hidden_act
       UpperCAmelCase_=     use_absolute_embeddings
       UpperCAmelCase_=     patch_norm
       UpperCAmelCase_=     layer_norm_eps
       UpperCAmelCase_=     initializer_range
       UpperCAmelCase_=     is_training
       UpperCAmelCase_=     scope
       UpperCAmelCase_=     use_labels
       UpperCAmelCase_=     type_sequence_label_size
       UpperCAmelCase_=     encoder_stride




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Dict:
       UpperCAmelCase_=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       UpperCAmelCase_=     None
       if self.use_labels:
             UpperCAmelCase_=     ids_tensor([self.batch_size]		,						self.type_sequence_label_size  )

       UpperCAmelCase_=     self.get_config()

       return config, pixel_values, labels




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Dict:
       return DonutSwinConfig(
           image_size=self.image_size		,						patch_size=self.patch_size		,						num_channels=self.num_channels		,						embed_dim=self.embed_dim		,						depths=self.depths		,						num_heads=self.num_heads		,						window_size=self.window_size		,						mlp_ratio=self.mlp_ratio		,						qkv_bias=self.qkv_bias		,						hidden_dropout_prob=self.hidden_dropout_prob		,						attention_probs_dropout_prob=self.attention_probs_dropout_prob		,						drop_path_rate=self.drop_path_rate		,						hidden_act=self.hidden_act		,						use_absolute_embeddings=self.use_absolute_embeddings		,						path_norm=self.patch_norm		,						layer_norm_eps=self.layer_norm_eps		,						initializer_range=self.initializer_range		,						encoder_stride=self.encoder_stride		,						)




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Optional[int]  )    ->				List[str]:
       UpperCAmelCase_=     DonutSwinModel(config=__UpperCAmelCase  )
       model.to(__UpperCAmelCase  )
       model.eval()
       UpperCAmelCase_=     model(__UpperCAmelCase  )

       UpperCAmelCase_=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
       UpperCAmelCase_=     int(config.embed_dim * 2 ** (len(config.depths  ) - 1)  )

       self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, expected_seq_len, expected_dim)  )




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Optional[int]:
       UpperCAmelCase_=     self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_     
       ),     (
           UpperCAmelCase_     
       ),     (
           UpperCAmelCase_     
       ),          
       )=     config_and_inputs
       UpperCAmelCase_=     {"""pixel_values""": pixel_values}
       return config, inputs_dict






@require_torch
class 	lowercase		(				snake_case__				,					snake_case__				,					unittest.TestCase):






 """simple docstring"""


 a__   :      Tuple	     =							(DonutSwinModel,) if is_torch_available() else ()
 a__   :      Optional[int]	     =							{"feature-extraction": DonutSwinModel} if is_torch_available() else {}
 a__   :      str	     =							True

 a__   :      Tuple	     =							False
 a__   :      Union[str, Any]	     =							False
 a__   :      Optional[Any]	     =							False




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				List[Any]:
       UpperCAmelCase_=     DonutSwinModelTester(self  )
       UpperCAmelCase_=     ConfigTester(self		,						config_class=__UpperCAmelCase		,						embed_dim=37  )




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Optional[int]:
       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			:		Optional[Any]  )    ->				int:
       return




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Union[str, Any]:
       UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCAmelCase  )




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				Any:
       # DonutSwin does not use inputs_embeds
       pass




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Dict:
       UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCAmelCase_=     model_class(__UpperCAmelCase  )
             self.assertIsInstance(model.get_input_embeddings()		,						(nn.Module)  )
             UpperCAmelCase_=     model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__UpperCAmelCase		,						nn.Linear  )  )




 def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				List[Any]:
       UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCAmelCase_=     ["""pixel_values"""]
             self.assertListEqual(arg_names[:1]		,						__UpperCAmelCase  )




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Dict:
       UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_=     True

       for model_class in self.all_model_classes:
             UpperCAmelCase_=     True
             UpperCAmelCase_=     False
             UpperCAmelCase_=     True
             UpperCAmelCase_=     model_class(__UpperCAmelCase  )
             model.to(__UpperCAmelCase  )
             model.eval()
             with torch.no_grad():
                   UpperCAmelCase_=     model(**self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )  )
             UpperCAmelCase_=     outputs.attentions
             UpperCAmelCase_=     len(self.model_tester.depths  )
             self.assertEqual(len(__UpperCAmelCase  )		,						__UpperCAmelCase  )

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             UpperCAmelCase_=     True
             UpperCAmelCase_=     config.window_size**2
             UpperCAmelCase_=     model_class(__UpperCAmelCase  )
             model.to(__UpperCAmelCase  )
             model.eval()
             with torch.no_grad():
                   UpperCAmelCase_=     model(**self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )  )
             UpperCAmelCase_=     outputs.attentions
             self.assertEqual(len(__UpperCAmelCase  )		,						__UpperCAmelCase  )

             self.assertListEqual(
                 list(attentions[0].shape[-3:]  )		,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,						)
             UpperCAmelCase_=     len(__UpperCAmelCase  )

             # Check attention is always last and order is fine
             UpperCAmelCase_=     True
             UpperCAmelCase_=     True
             UpperCAmelCase_=     model_class(__UpperCAmelCase  )
             model.to(__UpperCAmelCase  )
             model.eval()
             with torch.no_grad():
                   UpperCAmelCase_=     model(**self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )  )

             if hasattr(self.model_tester		,						"""num_hidden_states_types"""  ):
                   UpperCAmelCase_=     self.model_tester.num_hidden_states_types
             else:
                   # also another +1 for reshaped_hidden_states
                   UpperCAmelCase_=     2
             self.assertEqual(out_len + added_hidden_states		,						len(__UpperCAmelCase  )  )

             UpperCAmelCase_=     outputs.attentions

             self.assertEqual(len(__UpperCAmelCase  )		,						__UpperCAmelCase  )

             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]  )		,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,						)




 def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Union[str, Any]  )    ->				str:
       UpperCAmelCase_=     model_class(__UpperCAmelCase  )
       model.to(__UpperCAmelCase  )
       model.eval()

       with torch.no_grad():
             UpperCAmelCase_=     model(**self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )  )

       UpperCAmelCase_=     outputs.hidden_states

       UpperCAmelCase_=     getattr(
           self.model_tester		,						"""expected_num_hidden_layers"""		,						len(self.model_tester.depths  ) + 1  )
       self.assertEqual(len(__UpperCAmelCase  )		,						__UpperCAmelCase  )

       # DonutSwin has a different seq_length
       UpperCAmelCase_=     (
           config.patch_size
           if isinstance(config.patch_size		,						collections.abc.Iterable  )
           else (config.patch_size, config.patch_size)
       )

       UpperCAmelCase_=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]  )		,						[num_patches, self.model_tester.embed_dim]		,						)

       UpperCAmelCase_=     outputs.reshaped_hidden_states
       self.assertEqual(len(__UpperCAmelCase  )		,						__UpperCAmelCase  )

       UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     reshaped_hidden_states[0].shape
       UpperCAmelCase_=     (
           reshaped_hidden_states[0].view(__UpperCAmelCase		,						__UpperCAmelCase		,						height * width  ).permute(0		,						2		,						1  )
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:]  )		,						[num_patches, self.model_tester.embed_dim]		,						)




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				List[Any]:
       UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()

       UpperCAmelCase_=     (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size		,						collections.abc.Iterable  )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )

       for model_class in self.all_model_classes:
             UpperCAmelCase_=     True
             self.check_hidden_states_output(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )

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

             self.check_hidden_states_output(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Dict:
       UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_=     3

       UpperCAmelCase_=     (
           self.model_tester.image_size
           if isinstance(self.model_tester.image_size		,						collections.abc.Iterable  )
           else (self.model_tester.image_size, self.model_tester.image_size)
       )
       UpperCAmelCase_=     (
           config.patch_size
           if isinstance(config.patch_size		,						collections.abc.Iterable  )
           else (config.patch_size, config.patch_size)
       )

       UpperCAmelCase_=     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       UpperCAmelCase_=     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
             UpperCAmelCase_=     True
             self.check_hidden_states_output(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						(padded_height, padded_width)  )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             UpperCAmelCase_=     True
             self.check_hidden_states_output(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						(padded_height, padded_width)  )




 @slow
 def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				str:
       for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase_=     DonutSwinModel.from_pretrained(__UpperCAmelCase  )
             self.assertIsNotNone(__UpperCAmelCase  )




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				str:
       UpperCAmelCase_,     UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()

       UpperCAmelCase_=     _config_zero_init(__UpperCAmelCase  )
       for model_class in self.all_model_classes:
             UpperCAmelCase_=     model_class(config=__UpperCAmelCase  )
             for name, param in model.named_parameters():
                   if "embeddings" not in name and param.requires_grad:
                         self.assertIn(
                             ((param.data.mean() * 1E9).round() / 1E9).item()		,						[0.0, 1.0]		,						msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""		,						)









import copy
from typing import Dict

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


__A       					=		logging.get_logger(__name__)






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Optional[int]	     =							"semantic"




	def __init__(			self			:		Any		,						__UpperCAmelCase			:		List[str]=10_000		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		int=768		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=False		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		int=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		int=10_048		,						__UpperCAmelCase			:		Dict=129_595		,						__UpperCAmelCase			:		Optional[Any]=129_599		,						__UpperCAmelCase			:		Tuple=10_000		,						__UpperCAmelCase			:		List[Any]=256		,						__UpperCAmelCase			:		Union[str, Any]=49.9		,						**__UpperCAmelCase			:		Optional[Any]		,						)    ->				Tuple:
							super().__init__(
							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						eos_token_id=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						max_new_tokens=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)

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






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Any	     =							"coarse_acoustics"




	def __init__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		Union[str, Any]=False		,						__UpperCAmelCase			:		List[str]=False		,						__UpperCAmelCase			:		Any=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Optional[int]=12_048		,						__UpperCAmelCase			:		Optional[Any]=75		,						__UpperCAmelCase			:		str=2		,						__UpperCAmelCase			:		Optional[int]=12_050		,						__UpperCAmelCase			:		Union[str, Any]=256		,						__UpperCAmelCase			:		int = 630		,						__UpperCAmelCase			:		int = 60		,						**__UpperCAmelCase			:		int		,						)    ->				Union[str, Any]:
							super().__init__(
							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)

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






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Tuple	     =							"fine_acoustics"




	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		Tuple=0.5		,						__UpperCAmelCase			:		Optional[Any]=512		,						__UpperCAmelCase			:		Optional[Any]=1_024		,						__UpperCAmelCase			:		List[str]=8		,						**__UpperCAmelCase			:		str		,						)    ->				Tuple:
							super().__init__(temperature=__UpperCAmelCase  )

							UpperCAmelCase_=     max_fine_history_length
							UpperCAmelCase_=     max_fine_input_length
							UpperCAmelCase_=     n_fine_codebooks






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Any	     =							"bark"
	a__   :      List[str]	     =							True




	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		str=24_000		,						__UpperCAmelCase			:		List[str]=1_024		,						**__UpperCAmelCase			:		Optional[int]		,						)    ->				List[str]:
							if semantic_config is None:
													UpperCAmelCase_=     {}
													logger.info("""semantic_config is None. initializing the semantic model with default values."""  )

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

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

							UpperCAmelCase_=     BarkSemanticGenerationConfig(**__UpperCAmelCase  )
							UpperCAmelCase_=     BarkCoarseGenerationConfig(**__UpperCAmelCase  )
							UpperCAmelCase_=     BarkFineGenerationConfig(**__UpperCAmelCase  )

							UpperCAmelCase_=     sample_rate
							UpperCAmelCase_=     codebook_size




	@classmethod
	def 						_SCREAMING_SNAKE_CASE	(			cls			:		str		,						__UpperCAmelCase			:		BarkSemanticGenerationConfig		,						__UpperCAmelCase			:		BarkCoarseGenerationConfig		,						__UpperCAmelCase			:		BarkFineGenerationConfig		,						**__UpperCAmelCase			:		Any		,						)    ->				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()		,						**__UpperCAmelCase		,						)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Optional[Any]:
							UpperCAmelCase_=     copy.deepcopy(self.__dict__  )

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

							UpperCAmelCase_=     self.__class__.model_type
							return output




