
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__	=							random.Random()


def 							__A(lowerCAmelCase						,  lowerCAmelCase=1.0						,  lowerCAmelCase=None						,  lowerCAmelCase=None )		->				Tuple:

		"""simple docstring"""






		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	):

						def __init__(			self       ,							a       ,							a=7       ,							a=4_00       ,							a=20_00       ,							a=10       ,							a=1_60       ,							a=8       ,							a=0.0       ,							a=40_00       ,							a=False       ,							a=True       ,							)					->      List[str]:


								'''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  A_				(			self				)					->      Tuple:


								'''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  A_				(			self       ,							a=False       ,							a=False				)					->      List[Any]:


								'''simple docstring'''

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

								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(a				) for x in speech_inputs]
								return speech_inputs







@require_torch
@require_torchaudio
class        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: Optional[Any]	=		WhisperFeatureExtractor if is_speech_available() else None

						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	WhisperFeatureExtractionTester(self				)

						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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

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

								_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(a       ,							a				)				)
								self.assertEqual(a       ,							a				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

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

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

								_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(a       ,							a				)				)
								self.assertEqual(a       ,							a				)

						def  A_				(			self				)					->      str:


								'''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(8_00       ,							14_00       ,							2_00				)]
								_UpperCamelCase						    =	[np.asarray(a				) for speech_input in speech_inputs]

								# Test feature size
								_UpperCamelCase						    =	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
								_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(a       ,							a       ,							atol=1e-3				)				)

								# Test batched
								_UpperCamelCase						    =	feature_extractor(a       ,							return_tensors="""np"""				).input_features
								_UpperCamelCase						    =	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.
								_UpperCamelCase						    =	[floats_list((1, x)				)[0] for x in (8_00, 8_00, 8_00)]
								_UpperCamelCase						    =	np.asarray(a				)
								_UpperCamelCase						    =	feature_extractor(a       ,							return_tensors="""np"""				).input_features
								_UpperCamelCase						    =	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
								_UpperCamelCase						    =	[floats_list((1, x)				)[0] for x in range(2_00       ,							(feature_extractor.n_samples + 5_00)       ,							2_00				)]
								_UpperCamelCase						    =	[np.asarray(a				) for speech_input in speech_inputs]

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

								_UpperCamelCase						    =	feature_extractor(a       ,							return_tensors="""np"""				).input_features
								_UpperCamelCase						    =	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  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								import torch

								_UpperCamelCase						    =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
								_UpperCamelCase						    =	np.random.rand(1_00       ,							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  A_				(			self       ,							a				)					->      Tuple:


								'''simple docstring'''

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

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

						def  A_				(			self				)					->      str:


								'''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(a       ,							return_tensors="""pt"""				).input_features
								self.assertEqual(input_features.shape       ,							(1, 80, 30_00)				)
								self.assertTrue(torch.allclose(input_features[0, 0, :30]       ,							a       ,							atol=1e-4				)				)







						def  A_				(			self				)					->      Tuple:


								'''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())) * 6_55_35  # Rescale to [0, 65535] to show issue
								_UpperCamelCase						    =	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				)				)





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__	=							logging.get_logger()







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: List[nn.Module]	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: list	=		field(default_factory=__lowercase	)

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	len(list(m.modules()				)				) == 1 or isinstance(a       ,							nn.Convad				) or isinstance(a       ,							nn.BatchNormad				)
								if has_not_submodules:
										self.traced.append(a				)

						def __call__(			self       ,							a				)					->      Optional[int]:


								'''simple docstring'''

								for m in self.module.modules():
										self.handles.append(m.register_forward_hook(self._forward_hook				)				)
								self.module(a				)
								[x.remove() for x in self.handles]
								return self







						@property
						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								return list(filter(lambda a				: len(list(x.state_dict().keys()				)				) > 0       ,							self.traced				)				)







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: int	=		0
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)

						def __call__(			self       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	Tracker(self.dest				)(a				).parametrized
								_UpperCamelCase						    =	Tracker(self.src				)(a				).parametrized

								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.src_skip       ,							a				)				)
								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.dest_skip       ,							a				)				)

								if len(a				) != len(a				):
										raise Exception(
										    F'Numbers of operations are different. Source module has {len(a				)} operations while'
										    F' destination module has {len(a				)}.'				)

								for dest_m, src_m in zip(a       ,							a				):
										dest_m.load_state_dict(src_m.state_dict()				)
										if self.verbose == 1:
												print(F'Transfered from={src_m} to={dest_m}'				)




def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = True )		->				Optional[Any]:

		"""simple docstring"""






		print(F'Converting {name}...' )
		with torch.no_grad():
				_UpperCamelCase						    =	timm.create_model(lowerCAmelCase						,  pretrained=lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ResNetForImageClassification(lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ModuleTransfer(src=lowerCAmelCase						,  dest=lowerCAmelCase )
				_UpperCamelCase						    =	torch.randn((1, 3, 2_2_4, 2_2_4) )
				module_transfer(lowerCAmelCase )

		assert torch.allclose(from_model(lowerCAmelCase )						,  our_model(lowerCAmelCase ).logits ), "The model logits don't match the original one."

		_UpperCamelCase						    =	F'resnet{"-".join(name.split("resnet" ) )}'
		print(lowerCAmelCase )

		if push_to_hub:
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name						,  commit_message="""Add model"""						,  use_temp_dir=lowerCAmelCase						,  )

				# we can use the convnext one
				_UpperCamelCase						    =	AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name						,  commit_message="""Add image processor"""						,  use_temp_dir=lowerCAmelCase						,  )

				print(F'Pushed {checkpoint_name}' )


def 							__A(lowerCAmelCase						,  lowerCAmelCase = None						,  lowerCAmelCase = True )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	"""imagenet-1k-id2label.json"""
		_UpperCamelCase						    =	1_0_0_0
		_UpperCamelCase						    =	(1, num_labels)

		_UpperCamelCase						    =	"""huggingface/label-files"""
		_UpperCamelCase						    =	num_labels
		_UpperCamelCase						    =	json.load(open(hf_hub_download(lowerCAmelCase						,  lowerCAmelCase						,  repo_type="""dataset""" )						,  """r""" ) )
		_UpperCamelCase						    =	{int(lowerCAmelCase ): v for k, v in idalabel.items()}

		_UpperCamelCase						    =	idalabel
		_UpperCamelCase						    =	{v: k for k, v in idalabel.items()}

		_UpperCamelCase						    =	partial(lowerCAmelCase						,  num_labels=lowerCAmelCase						,  idalabel=lowerCAmelCase						,  labelaid=lowerCAmelCase )

		_UpperCamelCase						    =	{
		    """resnet18""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet26""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet34""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet50""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet101""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 2_3, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet152""": ImageNetPreTrainedConfig(
		        depths=[3, 8, 3_6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		}

		if model_name:
				convert_weight_and_push(lowerCAmelCase						,  names_to_config[model_name]						,  lowerCAmelCase						,  lowerCAmelCase )
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
		return config, expected_shape


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--model_name",
		    default=None,
		    type=str,
		    help=(
		        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
		        " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path",
		    default=None,
		    type=Path,
		    required=True,
		    help="Path to the output PyTorch model directory.",
		)
		parser.add_argument(
		    "--push_to_hub",
		    default=True,
		    type=bool,
		    required=False,
		    help="If True, push model and image processor to the hub.",
		)

		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



