






"""simple docstring"""







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()
lowerCamelCase_       :       Tuple      						=  logging.get_logger(__name__)
def _A						(						lowercase	,   lowercase=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 _A						(						lowercase	,   lowercase	,   lowercase=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 _A						(						lowercase		):







    """simple docstring"""
    a					=['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(UpperCamelCase__	,   UpperCamelCase__		)
def _A						(						lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    a					=dct.pop(UpperCamelCase__		)
    a					=val
def _A						(						):







    """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 _A						(						lowercase	,   lowercase		):







    """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					=10_00
        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					=1_92
            a					=7_68
            a					=12
            a					=3
        elif vit_name[9:].startswith('''small'''		):
            a					=3_84
            a					=15_36
            a					=12
            a					=6
        else:
            pass
    else:
        if vit_name[4:].startswith('''small'''		):
            a					=7_68
            a					=23_04
            a					=8
            a					=8
        elif vit_name[4:].startswith('''base'''		):
            pass
        elif vit_name[4:].startswith('''large'''		):
            a					=10_24
            a					=40_96
            a					=24
            a					=16
        elif vit_name[4:].startswith('''huge'''		):
            a					=12_80
            a					=51_20
            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__":
       lowerCamelCase_       :       List[Any]      						=  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."""
       )

       lowerCamelCase_       :       Dict      						=  parser.parse_args()
       convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



"""simple docstring"""







import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
       from transformers import WhisperFeatureExtractor

if is_torch_available():
       import torch

lowerCamelCase_       :       Any      						=  random.Random()
def _A						(						lowercase	,   lowercase=1.0	,   lowercase=None	,   lowercase=None		):







    """simple docstring"""
    if rng is None:
        a					=global_rng

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

    return values






@require_torch
@require_torchaudio
class 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 def __init__(		self			,   __A			,   __A=7			,   __A=400			,   __A=2000			,   __A=10			,   __A=160			,   __A=8			,   __A=0.0			,   __A=4000			,   __A=False			,   __A=True			,   ) ->					Optional[Any]:
     a					=parent
     a					=batch_size
     a					=min_seq_length
     a					=max_seq_length
     a					=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     a					=padding_value
     a					=sampling_rate
     a					=return_attention_mask
     a					=do_normalize
     a					=feature_size
     a					=chunk_length
     a					=hop_length


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     return {
         "feature_size": self.feature_size,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=False			,   __A=False						) ->					str:
     def _flatten(__A						):
         return list(itertools.chain(*__A						)						)

     if equal_length:
         a					=[floats_list((self.max_seq_length, self.feature_size)						) for _ in range(self.batch_size						)]
     else:
         # make sure that inputs increase in size
         a					=[
             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					=[np.asarray(__A						) for x in speech_inputs]
     return speech_inputs






@require_torch
@require_torchaudio
class 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  WhisperFeatureExtractor if is_speech_available() else None


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=WhisperFeatureExtractionTester(self						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=self.feature_extraction_class(**self.feat_extract_dict						)

     with tempfile.TemporaryDirectory() as tmpdirname:
         a					=feat_extract_first.save_pretrained(__A						)[0]
         check_json_file_has_correct_format(__A						)
         a					=self.feature_extraction_class.from_pretrained(__A						)

     a					=feat_extract_first.to_dict()
     a					=feat_extract_second.to_dict()
     a					=feat_extract_first.mel_filters
     a					=feat_extract_second.mel_filters
     self.assertTrue(np.allclose(__A			,   __A						)						)
     self.assertEqual(__A			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=self.feature_extraction_class(**self.feat_extract_dict						)

     with tempfile.TemporaryDirectory() as tmpdirname:
         a					=os.path.join(__A			,   '''feat_extract.json'''						)
         feat_extract_first.to_json_file(__A						)
         a					=self.feature_extraction_class.from_json_file(__A						)

     a					=feat_extract_first.to_dict()
     a					=feat_extract_second.to_dict()
     a					=feat_extract_first.mel_filters
     a					=feat_extract_second.mel_filters
     self.assertTrue(np.allclose(__A			,   __A						)						)
     self.assertEqual(__A			,   __A						)


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

     # Test feature size
     a					=feature_extractor(__A			,   padding='''max_length'''			,   return_tensors='''np'''						).input_features
     self.assertTrue(input_features.ndim == 3						)
     self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames						)
     self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size						)

     # Test not batched input
     a					=feature_extractor(speech_inputs[0]			,   return_tensors='''np'''						).input_features
     a					=feature_extractor(np_speech_inputs[0]			,   return_tensors='''np'''						).input_features
     self.assertTrue(np.allclose(__A			,   __A			,   atol=1E-3						)						)

     # Test batched
     a					=feature_extractor(__A			,   return_tensors='''np'''						).input_features
     a					=feature_extractor(__A			,   return_tensors='''np'''						).input_features
     for enc_seq_a, enc_seq_a in zip(__A			,   __A						):
         self.assertTrue(np.allclose(__A			,   __A			,   atol=1E-3						)						)

     # Test 2-D numpy arrays are batched.
     a					=[floats_list((1, x)						)[0] for x in (800, 800, 800)]
     a					=np.asarray(__A						)
     a					=feature_extractor(__A			,   return_tensors='''np'''						).input_features
     a					=feature_extractor(__A			,   return_tensors='''np'''						).input_features
     for enc_seq_a, enc_seq_a in zip(__A			,   __A						):
         self.assertTrue(np.allclose(__A			,   __A			,   atol=1E-3						)						)

     # Test truncation required
     a					=[floats_list((1, x)						)[0] for x in range(200			,   (feature_extractor.n_samples + 500)			,   200						)]
     a					=[np.asarray(__A						) for speech_input in speech_inputs]

     a					=[x[: feature_extractor.n_samples] for x in speech_inputs]
     a					=[np.asarray(__A						) for speech_input in speech_inputs_truncated]

     a					=feature_extractor(__A			,   return_tensors='''np'''						).input_features
     a					=feature_extractor(__A			,   return_tensors='''np'''						).input_features
     for enc_seq_a, enc_seq_a in zip(__A			,   __A						):
         self.assertTrue(np.allclose(__A			,   __A			,   atol=1E-3						)						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     import torch

     a					=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
     a					=np.random.rand(100			,   32						).astype(np.floataa						)
     a					=np_speech_inputs.tolist()

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Dict:
     a					=load_dataset('''hf-internal-testing/librispeech_asr_dummy'''			,   '''clean'''			,   split='''validation'''						)
     # automatic decoding with librispeech
     a					=ds.sort('''id'''						).select(range(__A						)						)[:num_samples]['''audio''']

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     # fmt: off
     a					=torch.tensor(
         [
             0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
             0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
             0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
             -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
         ]						)
     # fmt: on

     a					=self._load_datasamples(1						)
     a					=WhisperFeatureExtractor()
     a					=feature_extractor(__A			,   return_tensors='''pt'''						).input_features
     self.assertEqual(input_features.shape			,   (1, 80, 3000)						)
     self.assertTrue(torch.allclose(input_features[0, 0, :30]			,   __A			,   atol=1E-4						)						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
     a					=self._load_datasamples(1						)[0]
     a					=((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
     a					=feat_extract.zero_mean_unit_var_norm([audio]			,   attention_mask=__A						)[0]

     self.assertTrue(np.all(np.mean(__A						) < 1E-3						)						)
     self.assertTrue(np.all(np.abs(np.var(__A						) - 1						) < 1E-3						)						)