




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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase    		=       logging.get_logger(__name__)
def        UpperCamelCase_(				_A   :str				,     _A   :Dict=False  )->		str:
			UpperCamelCase__					=       []
			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'''deit.encoder.layer.{i}.layernorm_before.weight''')  )
						rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')  )
						rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')  )
						rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')  )
						rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')  )
						rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')  )
						rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')  )
						rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')  )
						rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')  )
						rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')  )

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ("cls_token", "deit.embeddings.cls_token"),
			        ("dist_token", "deit.embeddings.distillation_token"),
			        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
			        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
			        ("pos_embed", "deit.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 "deit" from all keys that start with "deit"
						UpperCamelCase__					=       [(pair[0], pair[1][4:]) if pair[1].startswith("deit"  ) else pair for pair in rename_keys]
			else:
						# layernorm + classification heads
						rename_keys.extend(
						    [
						        ("norm.weight", "deit.layernorm.weight"),
						        ("norm.bias", "deit.layernorm.bias"),
						        ("head.weight", "cls_classifier.weight"),
						        ("head.bias", "cls_classifier.bias"),
						        ("head_dist.weight", "distillation_classifier.weight"),
						        ("head_dist.bias", "distillation_classifier.bias"),
						    ]  )

			return rename_keys
def        UpperCamelCase_(				_A   :Dict				,     _A   :Optional[Any]				,     _A   :Tuple=False  )->		Optional[int]:






			for i in range(config.num_hidden_layers  ):
						if base_model:
									UpperCamelCase__					=       ''''''
						else:
									UpperCamelCase__					=       '''deit.'''
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						UpperCamelCase__					=       state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''  )
						UpperCamelCase__					=       state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''  )
						# next, add query, keys and values (in that order) to the state dict
						UpperCamelCase__					=       in_proj_weight[
						    : config.hidden_size, :
						]
						UpperCamelCase__					=       in_proj_bias[: config.hidden_size]
						UpperCamelCase__					=       in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCamelCase__					=       in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCamelCase__					=       in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCamelCase__					=       in_proj_bias[-config.hidden_size :]
def        UpperCamelCase_(				_A   :Optional[int]				,     _A   :List[str]				,     _A   :List[str]  )->		str:
			UpperCamelCase__					=       dct.pop(a_  )
			UpperCamelCase__					=       val
def        UpperCamelCase_(				)->		List[str]:
			UpperCamelCase__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			UpperCamelCase__					=       Image.open(requests.get(a_				,     stream=a_  ).raw  )
			return im
@torch.no_grad()
def        UpperCamelCase_(				_A   :Tuple				,     _A   :int  )->		List[Any]:
			UpperCamelCase__					=       DeiTConfig()
			# all deit models have fine-tuned heads
			UpperCamelCase__					=       False
			# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
			UpperCamelCase__					=       10_00
			UpperCamelCase__					=       '''huggingface/label-files'''
			UpperCamelCase__					=       '''imagenet-1k-id2label.json'''
			UpperCamelCase__					=       json.load(open(hf_hub_download(a_				,     a_				,     repo_type="dataset"  )				,     "r"  )  )
			UpperCamelCase__					=       {int(a_  ): v for k, v in idalabel.items()}
			UpperCamelCase__					=       idalabel
			UpperCamelCase__					=       {v: k for k, v in idalabel.items()}
			UpperCamelCase__					=       int(deit_name[-6:-4]  )
			UpperCamelCase__					=       int(deit_name[-3:]  )
			# size of the architecture
			if deit_name[9:].startswith("tiny"  ):
						UpperCamelCase__					=       1_92
						UpperCamelCase__					=       7_68
						UpperCamelCase__					=       12
						UpperCamelCase__					=       3
			elif deit_name[9:].startswith("small"  ):
						UpperCamelCase__					=       3_84
						UpperCamelCase__					=       15_36
						UpperCamelCase__					=       12
						UpperCamelCase__					=       6
			if deit_name[9:].startswith("base"  ):
						pass
			elif deit_name[4:].startswith("large"  ):
						UpperCamelCase__					=       10_24
						UpperCamelCase__					=       40_96
						UpperCamelCase__					=       24
						UpperCamelCase__					=       16

			# load original model from timm
			UpperCamelCase__					=       timm.create_model(a_				,     pretrained=a_  )
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			UpperCamelCase__					=       timm_model.state_dict()
			UpperCamelCase__					=       create_rename_keys(a_				,     a_  )
			for src, dest in rename_keys:
						rename_key(a_				,     a_				,     a_  )
			read_in_q_k_v(a_				,     a_				,     a_  )

			# load HuggingFace model
			UpperCamelCase__					=       DeiTForImageClassificationWithTeacher(a_  ).eval()
			model.load_state_dict(a_  )

			# Check outputs on an image, prepared by DeiTImageProcessor
			UpperCamelCase__					=       int(
			    (2_56 / 2_24) * config.image_size  )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
			UpperCamelCase__					=       DeiTImageProcessor(size=a_				,     crop_size=config.image_size  )
			UpperCamelCase__					=       image_processor(images=prepare_img()				,     return_tensors="pt"  )
			UpperCamelCase__					=       encoding['''pixel_values''']
			UpperCamelCase__					=       model(a_  )

			UpperCamelCase__					=       timm_model(a_  )
			assert timm_logits.shape == outputs.logits.shape
			assert torch.allclose(a_				,     outputs.logits				,     atol=1e-3  )

			Path(a_  ).mkdir(exist_ok=a_  )
			print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''  )
			model.save_pretrained(a_  )
			print(F'''Saving image processor to {pytorch_dump_folder_path}'''  )
			image_processor.save_pretrained(a_  )


if __name__ == "__main__":
						__UpperCamelCase    		=       argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--deit_name',
						    default='vit_deit_base_distilled_patch16_224',
						    type=str,
						    help='Name of the DeiT 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.'
						)

						__UpperCamelCase    		=       parser.parse_args()
						convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






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

__UpperCamelCase    		=       random.Random()
def        UpperCamelCase_(				_A   :Tuple				,     _A   :str=1.0				,     _A   :int=None				,     _A   :Dict=None  )->		str:
			if rng is None:
						UpperCamelCase__					=       global_rng

			UpperCamelCase__					=       []
			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        lowerCamelCase__	(							unittest.TestCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=7      ,						snake_case=400      ,						snake_case=2000      ,						snake_case=10      ,						snake_case=160      ,						snake_case=8      ,						snake_case=0.0      ,						snake_case=4000      ,						snake_case=False      ,						snake_case=True      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       parent
						UpperCamelCase__					=       batch_size
						UpperCamelCase__					=       min_seq_length
						UpperCamelCase__					=       max_seq_length
						UpperCamelCase__					=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						UpperCamelCase__					=       padding_value
						UpperCamelCase__					=       sampling_rate
						UpperCamelCase__					=       return_attention_mask
						UpperCamelCase__					=       do_normalize
						UpperCamelCase__					=       feature_size
						UpperCamelCase__					=       chunk_length
						UpperCamelCase__					=       hop_length




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						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 				snake_case__				(							self      ,						snake_case=False      ,						snake_case=False     ):

						'''simple docstring'''


						def _flatten(snake_case     ):
									return list(itertools.chain(*snake_case     )     )

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







@require_torch
@require_torchaudio
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Any    =		WhisperFeatureExtractor if is_speech_available() else None




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       WhisperFeatureExtractionTester(self     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.feature_extraction_class(**self.feat_extract_dict     )

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

						UpperCamelCase__					=       feat_extract_first.to_dict()
						UpperCamelCase__					=       feat_extract_second.to_dict()
						UpperCamelCase__					=       feat_extract_first.mel_filters
						UpperCamelCase__					=       feat_extract_second.mel_filters
						self.assertTrue(np.allclose(snake_case      ,						snake_case     )     )
						self.assertEqual(snake_case      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.feature_extraction_class(**self.feat_extract_dict     )

						with tempfile.TemporaryDirectory() as tmpdirname:
									UpperCamelCase__					=       os.path.join(snake_case      ,						"feat_extract.json"     )
									feat_extract_first.to_json_file(snake_case     )
									UpperCamelCase__					=       self.feature_extraction_class.from_json_file(snake_case     )

						UpperCamelCase__					=       feat_extract_first.to_dict()
						UpperCamelCase__					=       feat_extract_second.to_dict()
						UpperCamelCase__					=       feat_extract_first.mel_filters
						UpperCamelCase__					=       feat_extract_second.mel_filters
						self.assertTrue(np.allclose(snake_case      ,						snake_case     )     )
						self.assertEqual(snake_case      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
						# create three inputs of length 800, 1000, and 1200
						UpperCamelCase__					=       [floats_list((1, x)     )[0] for x in range(800      ,						1400      ,						200     )]
						UpperCamelCase__					=       [np.asarray(snake_case     ) for speech_input in speech_inputs]

						# Test feature size
						UpperCamelCase__					=       feature_extractor(snake_case      ,						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
						UpperCamelCase__					=       feature_extractor(speech_inputs[0]      ,						return_tensors="np"     ).input_features
						UpperCamelCase__					=       feature_extractor(np_speech_inputs[0]      ,						return_tensors="np"     ).input_features
						self.assertTrue(np.allclose(snake_case      ,						snake_case      ,						atol=1E-3     )     )

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

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

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

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

						UpperCamelCase__					=       feature_extractor(snake_case      ,						return_tensors="np"     ).input_features
						UpperCamelCase__					=       feature_extractor(snake_case      ,						return_tensors="np"     ).input_features
						for enc_seq_a, enc_seq_a in zip(snake_case      ,						snake_case     ):
									self.assertTrue(np.allclose(snake_case      ,						snake_case      ,						atol=1E-3     )     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						import torch

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

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




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       load_dataset("hf-internal-testing/librispeech_asr_dummy"      ,						"clean"      ,						split="validation"     )
						# automatic decoding with librispeech
						UpperCamelCase__					=       ds.sort("id"     ).select(range(snake_case     )     )[:num_samples]["audio"]

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




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       torch.tensor(
						    [
						        0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
						        0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
						        0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
						        -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
						    ]     )
						# fmt: on

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







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
						UpperCamelCase__					=       self._load_datasamples(1     )[0]
						UpperCamelCase__					=       ((audio - audio.min()) / (audio.max() - audio.min())) * 65535  # Rescale to [0, 65535] to show issue
						UpperCamelCase__					=       feat_extract.zero_mean_unit_var_norm([audio]      ,						attention_mask=snake_case     )[0]

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


