

"""simple docstring"""




import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
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_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_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    EfficientFormerForImageClassification,
						    EfficientFormerForImageClassificationWithTeacher,
						    EfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_efficientformer import (
						    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor





class 				lowerCamelCase  :

			def __init__(   self			:  Dict      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  int = 1_3      ,			__UpperCAmelCase			:  int = 6_4      ,			__UpperCAmelCase			:  int = 2      ,			__UpperCAmelCase			:  int = 3      ,			__UpperCAmelCase			:  int = 3      ,			__UpperCAmelCase			:  bool = True      ,			__UpperCAmelCase			:  bool = True      ,			__UpperCAmelCase			:  int = 1_2_8      ,			__UpperCAmelCase			:  Optional[int]=[1_6, 3_2, 6_4, 1_2_8]      ,			__UpperCAmelCase			:  int = 7      ,			__UpperCAmelCase			:  int = 4      ,			__UpperCAmelCase			:  int = 3_7      ,			__UpperCAmelCase			:  str = "gelu"      ,			__UpperCAmelCase			:  float = 0.1      ,			__UpperCAmelCase			:  float = 0.1      ,			__UpperCAmelCase			:  int = 1_0      ,			__UpperCAmelCase			:  float = 0.02      ,			__UpperCAmelCase			:  int = 2      ,			__UpperCAmelCase			:  int = 1      ,			__UpperCAmelCase			:  int = 1_2_8      ,			__UpperCAmelCase			:  List[int] = [2, 2, 2, 2]      ,			__UpperCAmelCase			:  int = 2      ,			__UpperCAmelCase			:  int = 2      ,			)				->      Dict:
						SCREAMING_SNAKE_CASE__									= parent
						SCREAMING_SNAKE_CASE__									= batch_size
						SCREAMING_SNAKE_CASE__									= image_size
						SCREAMING_SNAKE_CASE__									= patch_size
						SCREAMING_SNAKE_CASE__									= num_channels
						SCREAMING_SNAKE_CASE__									= is_training
						SCREAMING_SNAKE_CASE__									= use_labels
						SCREAMING_SNAKE_CASE__									= hidden_size
						SCREAMING_SNAKE_CASE__									= num_hidden_layers
						SCREAMING_SNAKE_CASE__									= num_attention_heads
						SCREAMING_SNAKE_CASE__									= intermediate_size
						SCREAMING_SNAKE_CASE__									= hidden_act
						SCREAMING_SNAKE_CASE__									= hidden_dropout_prob
						SCREAMING_SNAKE_CASE__									= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__									= type_sequence_label_size
						SCREAMING_SNAKE_CASE__									= initializer_range
						SCREAMING_SNAKE_CASE__									= encoder_stride
						SCREAMING_SNAKE_CASE__									= num_attention_outputs
						SCREAMING_SNAKE_CASE__									= embed_dim
						SCREAMING_SNAKE_CASE__									= embed_dim + 1
						SCREAMING_SNAKE_CASE__									= resolution
						SCREAMING_SNAKE_CASE__									= depths
						SCREAMING_SNAKE_CASE__									= hidden_sizes
						SCREAMING_SNAKE_CASE__									= dim
						SCREAMING_SNAKE_CASE__									= mlp_expansion_ratio

			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Optional[int]:
						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.type_sequence_label_size    )

						SCREAMING_SNAKE_CASE__									= self.get_config()
						return config, pixel_values, labels

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      List[Any]:
						return EfficientFormerConfig(
						    image_size=self.image_size      ,			patch_size=self.patch_size      ,			num_channels=self.num_channels      ,			hidden_size=self.hidden_size      ,			num_hidden_layers=self.num_hidden_layers      ,			num_attention_heads=self.num_attention_heads      ,			intermediate_size=self.intermediate_size      ,			hidden_act=self.hidden_act      ,			hidden_dropout_prob=self.hidden_dropout_prob      ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,			is_decoder=__UpperCAmelCase      ,			initializer_range=self.initializer_range      ,			encoder_stride=self.encoder_stride      ,			resolution=self.resolution      ,			depths=self.depths      ,			hidden_sizes=self.hidden_sizes      ,			dim=self.dim      ,			mlp_expansion_ratio=self.mlp_expansion_ratio      ,			)

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  List[str]    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= EfficientFormerModel(config=__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )
						self.parent.assertEqual(result.last_hidden_state.shape      ,			(self.batch_size, self.seq_length, self.hidden_size)    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  Optional[int]      ,			__UpperCAmelCase			:  Optional[Any]    )				->      int:
						SCREAMING_SNAKE_CASE__									= self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__									= EfficientFormerForImageClassification(__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase      ,			labels=__UpperCAmelCase    )
						self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.type_sequence_label_size)    )

						# test greyscale images
						SCREAMING_SNAKE_CASE__									= 1
						SCREAMING_SNAKE_CASE__									= EfficientFormerForImageClassification(__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()

						SCREAMING_SNAKE_CASE__									= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )
						self.parent.assertEqual(result.logits.shape      ,			(self.batch_size, self.type_sequence_label_size)    )





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]    )				->      Optional[int]:
						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 				lowerCamelCase  (A__     ,A__     ,unittest.TestCase     ):
			lowerCamelCase__				:						Any   						=							(
			    (
			        EfficientFormerModel,
			        EfficientFormerForImageClassificationWithTeacher,
			        EfficientFormerForImageClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__				:						int   						=							(
			    {
			        'feature-extraction': EfficientFormerModel,
			        'image-classification': (
			            EfficientFormerForImageClassification,
			            EfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__				:						Optional[Any]   						=							False

			lowerCamelCase__				:						List[str]   						=							False
			lowerCamelCase__				:						Dict   						=							False
			lowerCamelCase__				:						Optional[int]   						=							False

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= EfficientFormerModelTester(self    )
						SCREAMING_SNAKE_CASE__									= ConfigTester(
						    self      ,			config_class=__UpperCAmelCase      ,			has_text_modality=__UpperCAmelCase      ,			hidden_size=3_7    )

			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      List[Any]:
						self.config_tester.run_common_tests()

			@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""    )
			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Tuple:
						pass

			@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""    )
			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Optional[int]:
						pass

			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      List[str]:
						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(__UpperCAmelCase    )
									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]      ,			__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Optional[Any]:
						def check_hidden_states_output(__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Optional[Any]    ):
									SCREAMING_SNAKE_CASE__									= model_class(__UpperCAmelCase    )
									model.to(__UpperCAmelCase    )
									model.eval()

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

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

									SCREAMING_SNAKE_CASE__									= getattr(
									    self.model_tester      ,			"""expected_num_hidden_layers"""      ,			self.model_tester.num_hidden_layers + 1    )
									self.assertEqual(len(__UpperCAmelCase    )      ,			__UpperCAmelCase    )

									if hasattr(self.model_tester      ,			"""encoder_seq_length"""    ):
												SCREAMING_SNAKE_CASE__									= self.model_tester.encoder_seq_length
												if hasattr(self.model_tester      ,			"""chunk_length"""    ) and self.model_tester.chunk_length > 1:
															SCREAMING_SNAKE_CASE__									= seq_length * self.model_tester.chunk_length
									else:
												SCREAMING_SNAKE_CASE__									= self.model_tester.seq_length

									self.assertListEqual(
									    list(hidden_states[-1].shape[-2:]    )      ,			[seq_length, self.model_tester.hidden_size]      ,			)

									if config.is_encoder_decoder:
												SCREAMING_SNAKE_CASE__									= outputs.decoder_hidden_states

												self.assertIsInstance(__UpperCAmelCase      ,			(list, tuple)    )
												self.assertEqual(len(__UpperCAmelCase    )      ,			__UpperCAmelCase    )
												SCREAMING_SNAKE_CASE__									= getattr(self.model_tester      ,			"""seq_length"""      ,			__UpperCAmelCase    )
												SCREAMING_SNAKE_CASE__									= getattr(self.model_tester      ,			"""decoder_seq_length"""      ,			__UpperCAmelCase    )

												self.assertListEqual(
												    list(hidden_states[-1].shape[-2:]    )      ,			[decoder_seq_length, self.model_tester.hidden_size]      ,			)

						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(__UpperCAmelCase      ,			__UpperCAmelCase      ,			__UpperCAmelCase    )

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

									check_hidden_states_output(__UpperCAmelCase      ,			__UpperCAmelCase      ,			__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Optional[Any]=False    )				->      str:
						SCREAMING_SNAKE_CASE__									= super()._prepare_for_class(__UpperCAmelCase      ,			__UpperCAmelCase      ,			return_labels=__UpperCAmelCase    )

						if return_labels:
									if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
												del inputs_dict["labels"]

						return inputs_dict

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase    )

			@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""    )
			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      List[str]:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  int    )				->      List[str]:
						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:
									# EfficientFormerForImageClassificationWithTeacher supports inference-only
									if (
									    model_class in get_values(__UpperCAmelCase    )
									    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
									):
												continue
									SCREAMING_SNAKE_CASE__									= model_class(__UpperCAmelCase    )
									model.to(__UpperCAmelCase    )
									model.train()
									SCREAMING_SNAKE_CASE__									= self._prepare_for_class(__UpperCAmelCase      ,			__UpperCAmelCase      ,			return_labels=__UpperCAmelCase    )
									SCREAMING_SNAKE_CASE__									= model(**__UpperCAmelCase    ).loss
									loss.backward()

			def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      List[str]:
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs_for_common()

						SCREAMING_SNAKE_CASE__									= [
						    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
						    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
						    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
						]

						for model_class in self.all_model_classes:
									if (
									    model_class
									    not in [
									        *get_values(__UpperCAmelCase    ),
									    ]
									    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
									):
												continue

									for problem_type in problem_types:
												with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""    ):
															SCREAMING_SNAKE_CASE__									= problem_type["""title"""]
															SCREAMING_SNAKE_CASE__									= problem_type["""num_labels"""]

															SCREAMING_SNAKE_CASE__									= model_class(__UpperCAmelCase    )
															model.to(__UpperCAmelCase    )
															model.train()

															SCREAMING_SNAKE_CASE__									= self._prepare_for_class(__UpperCAmelCase      ,			__UpperCAmelCase      ,			return_labels=__UpperCAmelCase    )

															if problem_type["num_labels"] > 1:
																		SCREAMING_SNAKE_CASE__									= inputs["""labels"""].unsqueeze(1    ).repeat(1      ,			problem_type["""num_labels"""]    )

															SCREAMING_SNAKE_CASE__									= inputs["""labels"""].to(problem_type["""dtype"""]    )

															# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
															# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
															# they have the same size." which is a symptom something in wrong for the regression problem.
															# See https://github.com/huggingface/transformers/issues/11780
															with warnings.catch_warnings(record=__UpperCAmelCase    ) as warning_list:
																		SCREAMING_SNAKE_CASE__									= model(**__UpperCAmelCase    ).loss
															for w in warning_list:
																		if "Using a target size that is different to the input size" in str(w.message    ):
																					raise ValueError(
																					    F"""Something is going wrong in the regression problem: intercepted {w.message}"""    )

															loss.backward()

			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      str:
						for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__									= EfficientFormerModel.from_pretrained(__UpperCAmelCase    )
									self.assertIsNotNone(__UpperCAmelCase    )





			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__									= True

						SCREAMING_SNAKE_CASE__									= getattr(self.model_tester      ,			"""seq_length"""      ,			__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= getattr(self.model_tester      ,			"""encoder_seq_length"""      ,			__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= getattr(self.model_tester      ,			"""key_length"""      ,			__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= getattr(self.model_tester      ,			"""chunk_length"""      ,			__UpperCAmelCase    )

						if chunk_length is not None and hasattr(self.model_tester      ,			"""num_hashes"""    ):
									SCREAMING_SNAKE_CASE__									= encoder_seq_length * self.model_tester.num_hashes

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__									= True
									SCREAMING_SNAKE_CASE__									= False
									SCREAMING_SNAKE_CASE__									= True
									SCREAMING_SNAKE_CASE__									= model_class(__UpperCAmelCase    )
									model.to(__UpperCAmelCase    )
									model.eval()
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__									= model(**self._prepare_for_class(__UpperCAmelCase      ,			__UpperCAmelCase    )    )
									SCREAMING_SNAKE_CASE__									= outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
									self.assertEqual(len(__UpperCAmelCase    )      ,			self.model_tester.num_attention_outputs    )

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE__									= True
									SCREAMING_SNAKE_CASE__									= model_class(__UpperCAmelCase    )
									model.to(__UpperCAmelCase    )
									model.eval()
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__									= model(**self._prepare_for_class(__UpperCAmelCase      ,			__UpperCAmelCase    )    )
									SCREAMING_SNAKE_CASE__									= outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
									self.assertEqual(len(__UpperCAmelCase    )      ,			self.model_tester.num_attention_outputs    )

									if chunk_length is not None:
												self.assertListEqual(
												    list(attentions[0].shape[-4:]    )      ,			[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]      ,			)
									else:
												self.assertListEqual(
												    list(attentions[0].shape[-3:]    )      ,			[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]      ,			)

def 		A    (       ):




			'''simple docstring'''


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





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

			@cached_property
			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]    )				->      int:
						return (
						    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""    )
						    if is_vision_available()
						    else None
						)

			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      List[Any]:
						SCREAMING_SNAKE_CASE__									= EfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""    ).to(
						    __UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= self.default_image_processor
						SCREAMING_SNAKE_CASE__									= prepare_img()
						SCREAMING_SNAKE_CASE__									= image_processor(images=__UpperCAmelCase      ,			return_tensors="""pt"""    ).to(__UpperCAmelCase    )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__									= model(**__UpperCAmelCase    )

						# verify the logits
						SCREAMING_SNAKE_CASE__									= (1, 1_0_0_0)
						self.assertEqual(outputs.logits.shape      ,			__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= torch.tensor([-0.0_555, 0.4_825, -0.0_852]    ).to(__UpperCAmelCase    )
						self.assertTrue(torch.allclose(outputs.logits[0][:3]      ,			__UpperCAmelCase      ,			atol=1e-4    )    )





			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Dict:
						SCREAMING_SNAKE_CASE__									= EfficientFormerForImageClassificationWithTeacher.from_pretrained(
						    """snap-research/efficientformer-l1-300"""    ).to(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= self.default_image_processor
						SCREAMING_SNAKE_CASE__									= prepare_img()
						SCREAMING_SNAKE_CASE__									= image_processor(images=__UpperCAmelCase      ,			return_tensors="""pt"""    ).to(__UpperCAmelCase    )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__									= model(**__UpperCAmelCase    )

						# verify the logits
						SCREAMING_SNAKE_CASE__									= (1, 1_0_0_0)
						self.assertEqual(outputs.logits.shape      ,			__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= torch.tensor([-0.1_312, 0.4_353, -1.0_499]    ).to(__UpperCAmelCase    )
						self.assertTrue(torch.allclose(outputs.logits[0][:3]      ,			__UpperCAmelCase      ,			atol=1e-4    )    )





"""simple docstring"""




import copy
from typing import Dict

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


A_							:     str     =	logging.get_logger(__name__)





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Any   						=							'semantic'

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  List[str]=True      ,			__UpperCAmelCase			:  Tuple=7_6_8      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Optional[int]=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Any=0.7      ,			__UpperCAmelCase			:  Optional[int]=True      ,			__UpperCAmelCase			:  str=1_0_0_4_8      ,			__UpperCAmelCase			:  Tuple=1_2_9_5_9_5      ,			__UpperCAmelCase			:  List[Any]=1_2_9_5_9_9      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  Dict=2_5_6      ,			__UpperCAmelCase			:  int=49.9      ,			**__UpperCAmelCase			:  Any      ,			)				->      List[Any]:
						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      ,			)

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





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'coarse_acoustics'

			def __init__(   self			:  Optional[int]      ,			__UpperCAmelCase			:  Tuple=True      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  List[Any]=False      ,			__UpperCAmelCase			:  Tuple=False      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  Dict=0.7      ,			__UpperCAmelCase			:  Dict=True      ,			__UpperCAmelCase			:  List[str]=1_2_0_4_8      ,			__UpperCAmelCase			:  Optional[Any]=7_5      ,			__UpperCAmelCase			:  List[str]=2      ,			__UpperCAmelCase			:  List[str]=1_2_0_5_0      ,			__UpperCAmelCase			:  Optional[Any]=2_5_6      ,			__UpperCAmelCase			:  int = 6_3_0      ,			__UpperCAmelCase			:  int = 6_0      ,			**__UpperCAmelCase			:  Any      ,			)				->      Dict:
						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      ,			)

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





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'fine_acoustics'

			def __init__(   self			:  List[Any]      ,			__UpperCAmelCase			:  Union[str, Any]=0.5      ,			__UpperCAmelCase			:  List[Any]=5_1_2      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_2_4      ,			__UpperCAmelCase			:  Optional[Any]=8      ,			**__UpperCAmelCase			:  Tuple      ,			)				->      Optional[Any]:
						super().__init__(temperature=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= max_fine_history_length
						SCREAMING_SNAKE_CASE__									= max_fine_input_length
						SCREAMING_SNAKE_CASE__									= n_fine_codebooks





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Tuple   						=							'bark'
			lowerCamelCase__				:						Dict   						=							True

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  List[Any]=2_4_0_0_0      ,			__UpperCAmelCase			:  Any=1_0_2_4      ,			**__UpperCAmelCase			:  int      ,			)				->      Any:
						if semantic_config is None:
									SCREAMING_SNAKE_CASE__									= {}
									logger.info("""semantic_config is None. initializing the semantic model with default values."""    )

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

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

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

						SCREAMING_SNAKE_CASE__									= sample_rate
						SCREAMING_SNAKE_CASE__									= codebook_size

			@classmethod
			def    SCREAMING_SNAKE_CASE			(   cls			:  int      ,			__UpperCAmelCase			:  BarkSemanticGenerationConfig      ,			__UpperCAmelCase			:  BarkCoarseGenerationConfig      ,			__UpperCAmelCase			:  BarkFineGenerationConfig      ,			**__UpperCAmelCase			:  Optional[int]      ,			)				->      Tuple:
						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			:  Any    )				->      Any:
						SCREAMING_SNAKE_CASE__									= copy.deepcopy(self.__dict__    )

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

						SCREAMING_SNAKE_CASE__									= self.__class__.model_type
						return output


