

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a    :Dict      =			logging.get_logger(__name__)

def       __snake_case						( __UpperCamelCase	: Dict				,__UpperCamelCase	: Tuple=False    ):



      """simple docstring"""

      A_						  =				[]
      for i in range(config.num_hidden_layers    ):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')    )
            rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')    )
            rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')    )
            rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')    )
            rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')    )
            rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')    )
            rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')    )
            rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')    )
            rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')    )
            rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')    )

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("cls_token", "vit.embeddings.cls_token"),
              ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
              ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
              ("pos_embed", "vit.embeddings.position_embeddings"),
          ]    )

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ("norm.weight", "layernorm.weight"),
                    ("norm.bias", "layernorm.bias"),
                    ("pre_logits.fc.weight", "pooler.dense.weight"),
                    ("pre_logits.fc.bias", "pooler.dense.bias"),
                ]    )

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            A_						  =				[(pair[0], pair[1][4:]) if pair[1].startswith("vit"    ) else pair for pair in rename_keys]
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ("norm.weight", "vit.layernorm.weight"),
                    ("norm.bias", "vit.layernorm.bias"),
                    ("head.weight", "classifier.weight"),
                    ("head.bias", "classifier.bias"),
                ]    )

      return rename_keys

def       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: Tuple				,__UpperCamelCase	: Any=False    ):



      """simple docstring"""

      for i in range(config.num_hidden_layers    ):
            if base_model:
                  A_						  =				""
            else:
                  A_						  =				"vit."
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            A_						  =				state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''    )
            A_						  =				state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''    )
            # next, add query, keys and values (in that order) to the state dict
            A_						  =				in_proj_weight[
                : config.hidden_size, :
            ]
            A_						  =				in_proj_bias[: config.hidden_size]
            A_						  =				in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            A_						  =				in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            A_						  =				in_proj_weight[
                -config.hidden_size :, :
            ]
            A_						  =				in_proj_bias[-config.hidden_size :]

def       __snake_case						( __UpperCamelCase	: List[Any]    ):



      """simple docstring"""

      A_						  =				["head.weight", "head.bias"]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase				,__UpperCamelCase    )

def       __snake_case						( __UpperCamelCase	: Any				,__UpperCamelCase	: Optional[int]				,__UpperCamelCase	: List[str]    ):



      """simple docstring"""

      A_						  =				dct.pop(__UpperCamelCase    )
      A_						  =				val

def       __snake_case						( ):



      """simple docstring"""

      A_						  =				"http://images.cocodataset.org/val2017/000000039769.jpg"
      A_						  =				Image.open(requests.get(__UpperCamelCase				,stream=__UpperCamelCase    ).raw    )
      return im

@torch.no_grad()
def       __snake_case						( __UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: Optional[int]    ):



      """simple docstring"""

      A_						  =				ViTConfig()
      A_						  =				False
      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      if vit_name[-5:] == "in21k":
            A_						  =				True
            A_						  =				int(vit_name[-12:-10]    )
            A_						  =				int(vit_name[-9:-6]    )
      else:
            A_						  =				1000
            A_						  =				"huggingface/label-files"
            A_						  =				"imagenet-1k-id2label.json"
            A_						  =				json.load(open(hf_hub_download(__UpperCamelCase				,__UpperCamelCase				,repo_type="dataset"    )				,"r"    )    )
            A_						  =				{int(__UpperCamelCase    ): v for k, v in idalabel.items()}
            A_						  =				idalabel
            A_						  =				{v: k for k, v in idalabel.items()}
            A_						  =				int(vit_name[-6:-4]    )
            A_						  =				int(vit_name[-3:]    )
      # size of the architecture
      if "deit" in vit_name:
            if vit_name[9:].startswith("tiny"    ):
                  A_						  =				192
                  A_						  =				768
                  A_						  =				12
                  A_						  =				3
            elif vit_name[9:].startswith("small"    ):
                  A_						  =				384
                  A_						  =				1536
                  A_						  =				12
                  A_						  =				6
            else:
                  pass
      else:
            if vit_name[4:].startswith("small"    ):
                  A_						  =				768
                  A_						  =				2304
                  A_						  =				8
                  A_						  =				8
            elif vit_name[4:].startswith("base"    ):
                  pass
            elif vit_name[4:].startswith("large"    ):
                  A_						  =				1024
                  A_						  =				4096
                  A_						  =				24
                  A_						  =				16
            elif vit_name[4:].startswith("huge"    ):
                  A_						  =				1280
                  A_						  =				5120
                  A_						  =				32
                  A_						  =				16

    # load original model from timm
      A_						  =				timm.create_model(__UpperCamelCase				,pretrained=__UpperCamelCase    )
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      A_						  =				timm_model.state_dict()
      if base_model:
            remove_classification_head_(__UpperCamelCase    )
      A_						  =				create_rename_keys(__UpperCamelCase				,__UpperCamelCase    )
      for src, dest in rename_keys:
            rename_key(__UpperCamelCase				,__UpperCamelCase				,__UpperCamelCase    )
      read_in_q_k_v(__UpperCamelCase				,__UpperCamelCase				,__UpperCamelCase    )

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            A_						  =				ViTModel(__UpperCamelCase    ).eval()
      else:
            A_						  =				ViTForImageClassification(__UpperCamelCase    ).eval()
      model.load_state_dict(__UpperCamelCase    )

      # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
      if "deit" in vit_name:
            A_						  =				DeiTImageProcessor(size=config.image_size    )
      else:
            A_						  =				ViTImageProcessor(size=config.image_size    )
      A_						  =				image_processor(images=prepare_img()				,return_tensors="pt"    )
      A_						  =				encoding["pixel_values"]
      A_						  =				model(__UpperCamelCase    )

      if base_model:
            A_						  =				timm_model.forward_features(__UpperCamelCase    )
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(__UpperCamelCase				,outputs.pooler_output				,atol=1E-3    )
      else:
            A_						  =				timm_model(__UpperCamelCase    )
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(__UpperCamelCase				,outputs.logits				,atol=1E-3    )

      Path(__UpperCamelCase    ).mkdir(exist_ok=__UpperCamelCase    )
      print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''    )
      model.save_pretrained(__UpperCamelCase    )
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''    )
      image_processor.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
      __a    :str      =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--vit_name',
          default='vit_base_patch16_224',
          type=str,
          help='Name of the ViT timm model you\'d like to convert.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
      )

      __a    :Optional[int]      =			parser.parse_args()
      convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
     import torch


snake_case							:       Dict										=	random.Random()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Dict=1.0					,    UpperCAmelCase_		:						Optional[Any]=None					,    UpperCAmelCase_		:						Optional[Any]=None		):




      """simple docstring"""





      if rng is None:
            a		:str									=			global_rng

      a		:List[Any]									=			[]
      for batch_idx in range(shape[0]		):
            values.append([]		)
            for _ in range(shape[1]		):
                  values[-1].append(rng.random() * scale		)

      return values







@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=7   ,    _lowerCamelCase=400   ,    _lowerCamelCase=2000   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=1_6000   ,    _lowerCamelCase=True   ,    _lowerCamelCase=80   ,    _lowerCamelCase=16   ,    _lowerCamelCase=64   ,    _lowerCamelCase="hann_window"   ,    _lowerCamelCase=80   ,    _lowerCamelCase=7600   ,    _lowerCamelCase=1e-10   ,    _lowerCamelCase=True   ,    ):
       a		:Tuple									=			parent
       a		:Optional[int]									=			batch_size
       a		:Tuple									=			min_seq_length
       a		:List[Any]									=			max_seq_length
       a		:str									=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       a		:Optional[int]									=			feature_size
       a		:List[Any]									=			padding_value
       a		:Dict									=			sampling_rate
       a		:Union[str, Any]									=			do_normalize
       a		:str									=			num_mel_bins
       a		:Tuple									=			hop_length
       a		:Optional[int]									=			win_length
       a		:Any									=			win_function
       a		:Dict									=			fmin
       a		:Optional[int]									=			fmax
       a		:Optional[Any]									=			mel_floor
       a		:Dict									=			return_attention_mask




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return {
           "feature_size": self.feature_size,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "do_normalize": self.do_normalize,
           "num_mel_bins": self.num_mel_bins,
           "hop_length": self.hop_length,
           "win_length": self.win_length,
           "win_function": self.win_function,
           "fmin": self.fmin,
           "fmax": self.fmax,
           "mel_floor": self.mel_floor,
           "return_attention_mask": self.return_attention_mask,
       }




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase=False   ,    _lowerCamelCase=False       ):
       def _flatten(_lowerCamelCase       ):
             return list(itertools.chain(*_lowerCamelCase       )       )

       if equal_length:
             a		:List[Any]									=			floats_list((self.batch_size, self.max_seq_length)       )
       else:
             # make sure that inputs increase in size
             a		:str									=			[
                 _flatten(floats_list((x, self.feature_size)       )       )
                 for x in range(self.min_seq_length   ,    self.max_seq_length   ,    self.seq_length_diff       )
             ]

       if numpify:
             a		:Dict									=			[np.asarray(_lowerCamelCase       ) for x in speech_inputs]

       return speech_inputs




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase=False   ,    _lowerCamelCase=False       ):
       if equal_length:
             a		:Union[str, Any]									=			[floats_list((self.max_seq_length, self.num_mel_bins)       ) for _ in range(self.batch_size       )]
       else:
             # make sure that inputs increase in size
             a		:Optional[int]									=			[
                 floats_list((x, self.num_mel_bins)       )
                 for x in range(self.min_seq_length   ,    self.max_seq_length   ,    self.seq_length_diff       )
             ]

       if numpify:
             a		:Optional[int]									=			[np.asarray(_lowerCamelCase       ) for x in speech_inputs]

       return speech_inputs







@require_torch
class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			SpeechTaFeatureExtractor




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			SpeechTaFeatureExtractionTester(self       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       self.assertTrue(np.all(np.mean(_lowerCamelCase   ,    axis=0       ) < 1e-3       )       )
       self.assertTrue(np.all(np.abs(np.var(_lowerCamelCase   ,    axis=0       ) - 1       ) < 1e-3       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       a		:str									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
       # create three inputs of length 800, 1000, and 1200
       a		:Optional[Any]									=			[floats_list((1, x)       )[0] for x in range(800   ,    1400   ,    200       )]
       a		:Union[str, Any]									=			[np.asarray(_lowerCamelCase       ) for speech_input in speech_inputs]

       # Test not batched input
       a		:List[str]									=			feat_extract(speech_inputs[0]   ,    return_tensors='''np'''       ).input_values
       a		:int									=			feat_extract(np_speech_inputs[0]   ,    return_tensors='''np'''       ).input_values
       self.assertTrue(np.allclose(_lowerCamelCase   ,    _lowerCamelCase   ,    atol=1e-3       )       )

       # Test batched
       a		:Tuple									=			feat_extract(_lowerCamelCase   ,    return_tensors='''np'''       ).input_values
       a		:str									=			feat_extract(_lowerCamelCase   ,    return_tensors='''np'''       ).input_values
       for enc_seq_a, enc_seq_a in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertTrue(np.allclose(_lowerCamelCase   ,    _lowerCamelCase   ,    atol=1e-3       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
       a		:Optional[Any]									=			[floats_list((1, x)       )[0] for x in range(800   ,    1400   ,    200       )]

       a		:Union[str, Any]									=			['''longest''', '''max_length''', '''do_not_pad''']
       a		:List[Any]									=			[None, 1600, None]
       for max_length, padding in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             a		:int									=			feat_extract(_lowerCamelCase   ,    padding=_lowerCamelCase   ,    max_length=_lowerCamelCase   ,    return_tensors='''np'''       )
             a		:List[Any]									=			processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:800]       )
             self.assertTrue(input_values[0][800:].sum() < 1e-6       )
             self._check_zero_mean_unit_variance(input_values[1][:1000]       )
             self.assertTrue(input_values[0][1000:].sum() < 1e-6       )
             self._check_zero_mean_unit_variance(input_values[2][:1200]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:str									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
       a		:Tuple									=			range(800   ,    1400   ,    200       )
       a		:Dict									=			[floats_list((1, x)       )[0] for x in lengths]

       a		:List[Any]									=			['''longest''', '''max_length''', '''do_not_pad''']
       a		:Any									=			[None, 1600, None]

       for max_length, padding in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             a		:Tuple									=			feat_extract(_lowerCamelCase   ,    max_length=_lowerCamelCase   ,    padding=_lowerCamelCase       )
             a		:Dict									=			processed.input_values

             self._check_zero_mean_unit_variance(input_values[0][:800]       )
             self._check_zero_mean_unit_variance(input_values[1][:1000]       )
             self._check_zero_mean_unit_variance(input_values[2][:1200]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
       a		:Any									=			[floats_list((1, x)       )[0] for x in range(800   ,    1400   ,    200       )]
       a		:Optional[int]									=			feat_extract(
           _lowerCamelCase   ,    truncation=_lowerCamelCase   ,    max_length=1000   ,    padding='''max_length'''   ,    return_tensors='''np'''       )
       a		:str									=			processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]       )
       self._check_zero_mean_unit_variance(input_values[1]       )
       self._check_zero_mean_unit_variance(input_values[2]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
       a		:Optional[int]									=			[floats_list((1, x)       )[0] for x in range(800   ,    1400   ,    200       )]
       a		:Any									=			feat_extract(
           _lowerCamelCase   ,    truncation=_lowerCamelCase   ,    max_length=1000   ,    padding='''longest'''   ,    return_tensors='''np'''       )
       a		:List[str]									=			processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]       )
       self._check_zero_mean_unit_variance(input_values[1, :1000]       )
       self._check_zero_mean_unit_variance(input_values[2]       )

       # make sure that if max_length < longest -> then pad to max_length
       self.assertTrue(input_values.shape == (3, 1000)       )

       a		:List[str]									=			[floats_list((1, x)       )[0] for x in range(800   ,    1400   ,    200       )]
       a		:str									=			feat_extract(
           _lowerCamelCase   ,    truncation=_lowerCamelCase   ,    max_length=2000   ,    padding='''longest'''   ,    return_tensors='''np'''       )
       a		:Tuple									=			processed.input_values

       self._check_zero_mean_unit_variance(input_values[0, :800]       )
       self._check_zero_mean_unit_variance(input_values[1, :1000]       )
       self._check_zero_mean_unit_variance(input_values[2]       )

       # make sure that if max_length > longest -> then pad to longest
       self.assertTrue(input_values.shape == (3, 1200)       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
       a		:Any									=			np.random.rand(100       ).astype(np.floataa       )
       a		:Optional[int]									=			np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
             a		:Optional[Any]									=			feature_extractor.pad([{'''input_values''': inputs}]   ,    return_tensors='''np'''       )
             self.assertTrue(np_processed.input_values.dtype == np.floataa       )
             a		:str									=			feature_extractor.pad([{'''input_values''': inputs}]   ,    return_tensors='''pt'''       )
             self.assertTrue(pt_processed.input_values.dtype == torch.floataa       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # Tests that all call wrap to encode_plus and batch_encode_plus
       a		:Union[str, Any]									=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
       # create three inputs of length 800, 1000, and 1200
       a		:Any									=			[floats_list((1, x)       )[0] for x in range(800   ,    1400   ,    200       )]
       a		:Tuple									=			[np.asarray(_lowerCamelCase       ) for speech_input in speech_inputs]

       # Test feature size
       a		:List[Any]									=			feature_extractor(audio_target=_lowerCamelCase   ,    padding=_lowerCamelCase   ,    return_tensors='''np'''       ).input_values
       self.assertTrue(input_values.ndim == 3       )
       self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins       )

       # Test not batched input
       a		:List[Any]									=			feature_extractor(speech_inputs[0]   ,    return_tensors='''np'''       ).input_values
       a		:Any									=			feature_extractor(np_speech_inputs[0]   ,    return_tensors='''np'''       ).input_values
       self.assertTrue(np.allclose(_lowerCamelCase   ,    _lowerCamelCase   ,    atol=1e-3       )       )

       # Test batched
       a		:str									=			feature_extractor(_lowerCamelCase   ,    return_tensors='''np'''       ).input_values
       a		:Union[str, Any]									=			feature_extractor(_lowerCamelCase   ,    return_tensors='''np'''       ).input_values
       for enc_seq_a, enc_seq_a in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertTrue(np.allclose(_lowerCamelCase   ,    _lowerCamelCase   ,    atol=1e-3       )       )

       # Test 2-D numpy arrays are batched.
       a		:Union[str, Any]									=			[floats_list((1, x)       )[0] for x in (800, 800, 800)]
       a		:Optional[int]									=			np.asarray(_lowerCamelCase       )
       a		:List[Any]									=			feature_extractor(_lowerCamelCase   ,    return_tensors='''np'''       ).input_values
       a		:List[Any]									=			feature_extractor(_lowerCamelCase   ,    return_tensors='''np'''       ).input_values
       for enc_seq_a, enc_seq_a in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertTrue(np.allclose(_lowerCamelCase   ,    _lowerCamelCase   ,    atol=1e-3       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			self.feat_extract_tester.prepare_inputs_for_target()
       a		:List[Any]									=			self.feature_extraction_class(**self.feat_extract_dict       )
       a		:Any									=			feat_extract.model_input_names[0]

       a		:List[str]									=			BatchFeature({input_name: speech_inputs}       )

       self.assertTrue(all(len(_lowerCamelCase       ) == len(_lowerCamelCase       ) for x, y in zip(_lowerCamelCase   ,    processed_features[input_name]       )       )       )

       a		:Any									=			self.feat_extract_tester.prepare_inputs_for_target(equal_length=_lowerCamelCase       )
       a		:Optional[int]									=			BatchFeature({input_name: speech_inputs}   ,    tensor_type='''np'''       )

       a		:Dict									=			processed_features[input_name]

       if len(batch_features_input.shape       ) < 3:
             a		:Any									=			batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.num_mel_bins)       )




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			self.feat_extract_tester.prepare_inputs_for_target(equal_length=_lowerCamelCase       )
       a		:Tuple									=			self.feature_extraction_class(**self.feat_extract_dict       )
       a		:List[Any]									=			feat_extract.model_input_names[0]

       a		:List[str]									=			BatchFeature({input_name: speech_inputs}   ,    tensor_type='''pt'''       )

       a		:Union[str, Any]									=			processed_features[input_name]

       if len(batch_features_input.shape       ) < 3:
             a		:List[str]									=			batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.num_mel_bins)       )




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Dict									=			self.feature_extraction_class(**self.feat_extract_dict       )
       a		:str									=			self.feat_extract_tester.prepare_inputs_for_target()
       a		:Optional[int]									=			feat_extract.model_input_names[0]

       a		:str									=			BatchFeature({input_name: speech_inputs}       )

       a		:Dict									=			feat_extract.num_mel_bins  # hack!

       a		:Optional[Any]									=			feat_extract.pad(_lowerCamelCase   ,    padding='''longest'''   ,    return_tensors='''np'''       )[input_name]
       a		:List[str]									=			feat_extract.pad(_lowerCamelCase   ,    padding='''longest'''   ,    return_tensors='''pt'''       )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa       ).sum() - input_pt.numpy().astype(np.floataa       ).sum()       ) < 1e-2       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			self.feat_extract_dict
       a		:Any									=			True
       a		:Union[str, Any]									=			self.feature_extraction_class(**_lowerCamelCase       )
       a		:int									=			self.feat_extract_tester.prepare_inputs_for_target()
       a		:Dict									=			[len(_lowerCamelCase       ) for x in speech_inputs]
       a		:List[Any]									=			feat_extract.model_input_names[0]

       a		:Optional[int]									=			BatchFeature({input_name: speech_inputs}       )

       a		:List[Any]									=			feat_extract.num_mel_bins  # hack!

       a		:Optional[Any]									=			feat_extract.pad(_lowerCamelCase   ,    padding='''longest'''   ,    return_tensors='''np'''       )
       self.assertIn('''attention_mask'''   ,    _lowerCamelCase       )
       self.assertListEqual(list(processed.attention_mask.shape       )   ,    list(processed[input_name].shape[:2]       )       )
       self.assertListEqual(processed.attention_mask.sum(-1       ).tolist()   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.feat_extract_dict
       a		:str									=			True
       a		:Any									=			self.feature_extraction_class(**_lowerCamelCase       )
       a		:Any									=			self.feat_extract_tester.prepare_inputs_for_target()
       a		:Dict									=			[len(_lowerCamelCase       ) for x in speech_inputs]
       a		:Tuple									=			feat_extract.model_input_names[0]

       a		:int									=			BatchFeature({input_name: speech_inputs}       )
       a		:Optional[Any]									=			min(_lowerCamelCase       )

       a		:Dict									=			feat_extract.num_mel_bins  # hack!

       a		:Dict									=			feat_extract.pad(
           _lowerCamelCase   ,    padding='''max_length'''   ,    max_length=_lowerCamelCase   ,    truncation=_lowerCamelCase   ,    return_tensors='''np'''       )
       self.assertIn('''attention_mask'''   ,    _lowerCamelCase       )
       self.assertListEqual(
           list(processed_pad.attention_mask.shape       )   ,    [processed_pad[input_name].shape[0], max_length]       )
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1       ).tolist()   ,    [max_length for x in speech_inputs]       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       from datasets import load_dataset

       a		:List[str]									=			load_dataset('''hf-internal-testing/librispeech_asr_dummy'''   ,    '''clean'''   ,    split='''validation'''       )
       # automatic decoding with librispeech
       a		:List[str]									=			ds.sort('''id'''       ).select(range(_lowerCamelCase       )       )[:num_samples]['''audio''']

       return [x["array"] for x in speech_samples]




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # fmt: off
       a		:Dict									=			torch.tensor(
           [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
            3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
            2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
            4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
            7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
            4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]       )
       # fmt: on

       a		:List[Any]									=			self._load_datasamples(1       )
       a		:Any									=			SpeechTaFeatureExtractor()
       a		:Optional[int]									=			feature_extractor(_lowerCamelCase   ,    return_tensors='''pt'''       ).input_values
       self.assertEquals(input_values.shape   ,    (1, 9_3680)       )
       self.assertTrue(torch.allclose(input_values[0, :30]   ,    _lowerCamelCase   ,    atol=1e-6       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # fmt: off
       a		:str									=			torch.tensor(
           [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
            -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
            -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
            -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]       )
       # fmt: on

       a		:int									=			self._load_datasamples(1       )
       a		:int									=			SpeechTaFeatureExtractor()
       a		:List[Any]									=			feature_extractor(audio_target=_lowerCamelCase   ,    return_tensors='''pt'''       ).input_values
       self.assertEquals(input_values.shape   ,    (1, 366, 80)       )
       self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,    _lowerCamelCase   ,    atol=1e-4       )       )





