


'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging


if TYPE_CHECKING:
     from ...feature_extraction_utils import FeatureExtractionMixin
     from ...tokenization_utils_base import PreTrainedTokenizerBase
     from ...utils import TensorType

a_						:       Optional[int]      			=				logging.get_logger(__name__)

a_						:       Optional[int]      			=				{
    """openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}

# fmt: off
a_						:       List[str]      			=				[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
    705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
    1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
    4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 10563, 10786,
    11420, 11709, 11907, 13163, 13697, 13700, 14808, 15306, 16410, 16791,
    17992, 19203, 19510, 20724, 22305, 22935, 27007, 30109, 30420, 33409,
    34949, 40283, 40493, 40549, 47282, 49146, 50257, 50359, 50360, 50361
]
a_						:       Tuple      			=				[
    1, 2, 7, 8, 9, 10, 14, 25,
    26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
    63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
    893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
    3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
    7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793,
    14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675,
    22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865,
    42863, 47425, 49870, 50254, 50258, 50360, 50361, 50362
]





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"whisper"
  _lowerCamelCase						=						["past_key_values"]
  _lowerCamelCase						=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






  def __init__(					self    ,						UpperCamelCase=5_1865    ,						UpperCamelCase=80    ,						UpperCamelCase=6    ,						UpperCamelCase=4    ,						UpperCamelCase=6    ,						UpperCamelCase=4    ,						UpperCamelCase=1536    ,						UpperCamelCase=1536    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=5_0257    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=256    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.02    ,						UpperCamelCase=False    ,						UpperCamelCase=1500    ,						UpperCamelCase=448    ,						UpperCamelCase=5_0256    ,						UpperCamelCase=5_0256    ,						UpperCamelCase=5_0256    ,						UpperCamelCase=None    ,						UpperCamelCase=[220, 5_0256]    ,						UpperCamelCase=False    ,						UpperCamelCase=256    ,						UpperCamelCase=False    ,						UpperCamelCase=0.05    ,						UpperCamelCase=10    ,						UpperCamelCase=2    ,						UpperCamelCase=0.0    ,						UpperCamelCase=10    ,						UpperCamelCase=0    ,						UpperCamelCase=7    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						num_mel_bins
   lowerCamelCase_  				=						d_model
   lowerCamelCase_  				=						encoder_layers
   lowerCamelCase_  				=						encoder_attention_heads
   lowerCamelCase_  				=						decoder_layers
   lowerCamelCase_  				=						decoder_attention_heads
   lowerCamelCase_  				=						decoder_ffn_dim
   lowerCamelCase_  				=						encoder_ffn_dim
   lowerCamelCase_  				=						dropout
   lowerCamelCase_  				=						attention_dropout
   lowerCamelCase_  				=						activation_dropout
   lowerCamelCase_  				=						activation_function
   lowerCamelCase_  				=						init_std
   lowerCamelCase_  				=						encoder_layerdrop
   lowerCamelCase_  				=						decoder_layerdrop
   lowerCamelCase_  				=						use_cache
   lowerCamelCase_  				=						encoder_layers
   lowerCamelCase_  				=						scale_embedding  # scale factor will be sqrt(d_model) if True
   lowerCamelCase_  				=						max_source_positions
   lowerCamelCase_  				=						max_target_positions

   # Audio Classification-specific parameters. Feel free to ignore for other classes.
   lowerCamelCase_  				=						classifier_proj_size
   lowerCamelCase_  				=						use_weighted_layer_sum

   # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
   lowerCamelCase_  				=						apply_spec_augment
   lowerCamelCase_  				=						mask_time_prob
   lowerCamelCase_  				=						mask_time_length
   lowerCamelCase_  				=						mask_time_min_masks
   lowerCamelCase_  				=						mask_feature_prob
   lowerCamelCase_  				=						mask_feature_length
   lowerCamelCase_  				=						mask_feature_min_masks

   lowerCamelCase_  				=						median_filter_width

   super().__init__(
       pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase    ,						is_encoder_decoder=UpperCamelCase    ,						decoder_start_token_id=UpperCamelCase    ,						suppress_tokens=UpperCamelCase    ,						begin_suppress_tokens=UpperCamelCase    ,						**UpperCamelCase    ,						)





class   snake_case					(					lowercase    ):





  """simple docstring"""




  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						OrderedDict(
       [
           ("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
       ]  )
   if self.use_past:
    lowerCamelCase_  				=						{0: "batch"}
   else:
    lowerCamelCase_  				=						{0: "batch", 1: "decoder_sequence"}

   if self.use_past:
    self.fill_with_past_key_values_(UpperCamelCase    ,						direction="inputs"  )

   return common_inputs






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = -1    ,						UpperCamelCase = -1    ,						UpperCamelCase = False    ,						UpperCamelCase = None    ,						UpperCamelCase = 2_2050    ,						UpperCamelCase = 5.0    ,						UpperCamelCase = 220    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						OrderedDict()
   lowerCamelCase_  				=						OnnxConfig.generate_dummy_inputs(
       self    ,						preprocessor=preprocessor.feature_extractor    ,						batch_size=UpperCamelCase    ,						framework=UpperCamelCase    ,						sampling_rate=UpperCamelCase    ,						time_duration=UpperCamelCase    ,						frequency=UpperCamelCase    ,						)
   lowerCamelCase_  				=						encoder_inputs["input_features"].shape[2]
   lowerCamelCase_  				=						encoder_sequence_length // 2 if self.use_past else seq_length

   lowerCamelCase_  				=						super().generate_dummy_inputs(
       preprocessor.tokenizer    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )

   lowerCamelCase_  				=						encoder_inputs.pop("input_features"  )
   lowerCamelCase_  				=						decoder_inputs.pop("decoder_input_ids"  )

   if "past_key_values" in decoder_inputs:
    lowerCamelCase_  				=						decoder_inputs.pop("past_key_values"  )

   return dummy_inputs







  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return 1e-3







'''simple docstring'''

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
     import torch

     from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin





class   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase="resnet50"    ,						UpperCamelCase=3    ,						UpperCamelCase=32    ,						UpperCamelCase=3    ,						UpperCamelCase=True    ,						UpperCamelCase=True    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						out_indices if out_indices is not None else [4]
   lowerCamelCase_  				=						stage_names
   lowerCamelCase_  				=						out_features
   lowerCamelCase_  				=						backbone
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						image_size
   lowerCamelCase_  				=						num_channels
   lowerCamelCase_  				=						use_pretrained_backbone
   lowerCamelCase_  				=						is_training






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
   lowerCamelCase_  				=						self.get_config()

   return config, pixel_values






  def 						snake_case	(					self  ):







   """simple docstring"""
   return TimmBackboneConfig(
       image_size=self.image_size    ,						num_channels=self.num_channels    ,						out_features=self.out_features    ,						out_indices=self.out_indices    ,						stage_names=self.stage_names    ,						use_pretrained_backbone=self.use_pretrained_backbone    ,						backbone=self.backbone    ,						)






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						TimmBackbone(config=UpperCamelCase  )
   model.to(UpperCamelCase  )
   model.eval()
   with torch.no_grad():
    lowerCamelCase_  				=						model(UpperCamelCase  )
   self.parent.assertEqual(
       result.feature_map[-1].shape    ,						(self.batch_size, model.channels[-1], 14, 14)    ,						)







  def 						snake_case	(					self  ):







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





@require_torch
@require_timm
class   snake_case					(					lowercase    ,		lowercase    ,		lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						(TimmBackbone,) if is_torch_available() else ()
  _lowerCamelCase						=						{"feature-extraction": TimmBackbone} if is_torch_available() else {}
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						TimmBackboneModelTester(self  )
   lowerCamelCase_  				=						ConfigTester(self    ,						config_class=UpperCamelCase    ,						has_text_modality=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   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 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"resnet18"
   lowerCamelCase_  				=						"microsoft/resnet-18"

   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						use_timm_backbone=UpperCamelCase  )
   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase  )

   self.assertEqual(len(timm_model.out_features  )    ,						len(transformers_model.out_features  )  )
   self.assertEqual(len(timm_model.stage_names  )    ,						len(transformers_model.stage_names  )  )
   self.assertEqual(timm_model.channels    ,						transformers_model.channels  )
   # Out indices are set to the last layer by default. For timm models, we don't know
   # the number of layers in advance, so we set it to (-1,), whereas for transformers
   # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
   self.assertEqual(timm_model.out_indices    ,						(-1,)  )
   self.assertEqual(transformers_model.out_indices    ,						[len(timm_model.stage_names  ) - 1]  )

   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						use_timm_backbone=UpperCamelCase    ,						out_indices=[1, 2, 3]  )
   lowerCamelCase_  				=						AutoBackbone.from_pretrained(UpperCamelCase    ,						out_indices=[1, 2, 3]  )

   self.assertEqual(timm_model.out_indices    ,						transformers_model.out_indices  )
   self.assertEqual(len(timm_model.out_features  )    ,						len(transformers_model.out_features  )  )
   self.assertEqual(timm_model.channels    ,						transformers_model.channels  )






  @unittest.skip("TimmBackbone doesn't support feed forward chunking"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone initialization is managed on the timm side"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone models doesn't have inputs_embeds"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone models doesn't have inputs_embeds"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("model weights aren't tied in TimmBackbone."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("model weights aren't tied in TimmBackbone."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("TimmBackbone doesn't support output_attentions."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Safetensors is not supported by timm."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    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]    ,						UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase_  				=						True
   lowerCamelCase_  				=						self.has_attentions

   # no need to test all models as different heads yield the same functionality
   lowerCamelCase_  				=						self.all_model_classes[0]
   lowerCamelCase_  				=						model_class(UpperCamelCase  )
   model.to(UpperCamelCase  )

   lowerCamelCase_  				=						self._prepare_for_class(UpperCamelCase    ,						UpperCamelCase  )
   lowerCamelCase_  				=						model(**UpperCamelCase  )
   lowerCamelCase_  				=						outputs[0][-1]

   # Encoder-/Decoder-only models
   lowerCamelCase_  				=						outputs.hidden_states[0]
   hidden_states.retain_grad()

   if self.has_attentions:
    lowerCamelCase_  				=						outputs.attentions[0]
    attentions.retain_grad()

   output.flatten()[0].backward(retain_graph=UpperCamelCase  )

   self.assertIsNotNone(hidden_states.grad  )

   if self.has_attentions:
    self.assertIsNotNone(attentions.grad  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )

    self.assertEqual(len(result.feature_maps  )    ,						len(config.out_indices  )  )
    self.assertEqual(len(model.channels  )    ,						len(config.out_indices  )  )

    # Check output of last stage is taken if out_features=None, out_indices=None
    lowerCamelCase_  				=						copy.deepcopy(UpperCamelCase  )
    lowerCamelCase_  				=						None
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )

    self.assertEqual(len(result.feature_maps  )    ,						1  )
    self.assertEqual(len(model.channels  )    ,						1  )

    # Check backbone can be initialized with fresh weights
    lowerCamelCase_  				=						copy.deepcopy(UpperCamelCase  )
    lowerCamelCase_  				=						False
    lowerCamelCase_  				=						model_class(UpperCamelCase  )
    model.to(UpperCamelCase  )
    model.eval()
    lowerCamelCase_  				=						model(**UpperCamelCase  )



