






"""simple docstring"""
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCamelCase_							      = random.Random()

def 							snake_case (  A__      ,A__=1.0      ,A__=None      ,A__=None		):
			if rng is None:
						UpperCAmelCase_	:					str     	=   global_rng

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

			return values







class      UpperCamelCase_			(unittest.TestCase	):





	def __init__( self   :					List[Any]      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					Any=7      ,      lowerCAmelCase_   :					Dict=400      ,      lowerCAmelCase_   :					int=2_000      ,      lowerCAmelCase_   :					int=1      ,      lowerCAmelCase_   :					str=0.0      ,      lowerCAmelCase_   :					Optional[Any]=16_000      ,      lowerCAmelCase_   :					Dict=True      ,      lowerCAmelCase_   :					Tuple=True      ,      )		->     Any:
				UpperCAmelCase_	:					str     	=   parent
				UpperCAmelCase_	:					Tuple     	=   batch_size
				UpperCAmelCase_	:					Tuple     	=   min_seq_length
				UpperCAmelCase_	:					Any     	=   max_seq_length
				UpperCAmelCase_	:					Optional[Any]     	=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				UpperCAmelCase_	:					List[Any]     	=   feature_size
				UpperCAmelCase_	:					List[Any]     	=   padding_value
				UpperCAmelCase_	:					Union[str, Any]     	=   sampling_rate
				UpperCAmelCase_	:					Union[str, Any]     	=   return_attention_mask
				UpperCAmelCase_	:					List[Any]     	=   do_normalize





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Optional[Any]:
				return {
				    "feature_size": self.feature_size,
				    "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   :					Optional[int]      ,      lowerCAmelCase_   :					List[Any]=False      ,      lowerCAmelCase_   :					Optional[int]=False		)		->     Union[str, Any]:
				def _flatten(lowerCAmelCase_   :					List[str]		):
							return list(itertools.chain(*lowerCAmelCase_		)		)

				if equal_length:
							UpperCAmelCase_	:					Tuple     	=   floats_list((self.batch_size, self.max_seq_length)		)
				else:
							# make sure that inputs increase in size
							UpperCAmelCase_	:					List[Any]     	=   [
							    _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:
							UpperCAmelCase_	:					str     	=   [np.asarray(lowerCAmelCase_		) for x in speech_inputs]

				return speech_inputs







class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							WavaVecaFeatureExtractor





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Any:
				UpperCAmelCase_	:					List[str]     	=   WavaVecaFeatureExtractionTester(self		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]      ,      lowerCAmelCase_   :					int		)		->     List[str]:
				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   :					List[str]		)		->     int:
				# Tests that all call wrap to encode_plus and batch_encode_plus
				UpperCAmelCase_	:					List[str]     	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				# create three inputs of length 800, 1000, and 1200
				UpperCAmelCase_	:					List[Any]     	=   [floats_list((1, x)		)[0] for x in range(800      ,      1_400      ,      200		)]
				UpperCAmelCase_	:					Dict     	=   [np.asarray(lowerCAmelCase_		) for speech_input in speech_inputs]

				# Test not batched input
				UpperCAmelCase_	:					List[Any]     	=   feat_extract(speech_inputs[0]      ,      return_tensors="np"		).input_values
				UpperCAmelCase_	:					int     	=   feat_extract(np_speech_inputs[0]      ,      return_tensors="np"		).input_values
				self.assertTrue(np.allclose(lowerCAmelCase_      ,      lowerCAmelCase_      ,      atol=1e-3		)		)

				# Test batched
				UpperCAmelCase_	:					Optional[int]     	=   feat_extract(lowerCAmelCase_      ,      return_tensors="np"		).input_values
				UpperCAmelCase_	:					List[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		)		)

				# Test 2-D numpy arrays are batched.
				UpperCAmelCase_	:					Tuple     	=   [floats_list((1, x)		)[0] for x in (800, 800, 800)]
				UpperCAmelCase_	:					str     	=   np.asarray(lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   feat_extract(lowerCAmelCase_      ,      return_tensors="np"		).input_values
				UpperCAmelCase_	:					Optional[int]     	=   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   :					int		)		->     Dict:
				UpperCAmelCase_	:					Optional[Any]     	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				UpperCAmelCase_	:					Dict     	=   [floats_list((1, x)		)[0] for x in range(800      ,      1_400      ,      200		)]

				UpperCAmelCase_	:					Optional[int]     	=   ["longest", "max_length", "do_not_pad"]
				UpperCAmelCase_	:					Union[str, Any]     	=   [None, 1_600, None]
				for max_length, padding in zip(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					str     	=   feat_extract(lowerCAmelCase_      ,      padding=lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      return_tensors="np"		)
							UpperCAmelCase_	:					Optional[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][:1_000]		)
							self.assertTrue(input_values[0][1_000:].sum() < 1e-6		)
							self._check_zero_mean_unit_variance(input_values[2][:1_200]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Optional[int]:
				UpperCAmelCase_	:					str     	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				UpperCAmelCase_	:					Dict     	=   range(800      ,      1_400      ,      200		)
				UpperCAmelCase_	:					Any     	=   [floats_list((1, x)		)[0] for x in lengths]

				UpperCAmelCase_	:					Tuple     	=   ["longest", "max_length", "do_not_pad"]
				UpperCAmelCase_	:					Optional[Any]     	=   [None, 1_600, None]

				for max_length, padding in zip(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					List[Any]     	=   feat_extract(lowerCAmelCase_      ,      max_length=lowerCAmelCase_      ,      padding=lowerCAmelCase_		)
							UpperCAmelCase_	:					Dict     	=   processed.input_values

							self._check_zero_mean_unit_variance(input_values[0][:800]		)
							self._check_zero_mean_unit_variance(input_values[1][:1_000]		)
							self._check_zero_mean_unit_variance(input_values[2][:1_200]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     List[Any]:
				UpperCAmelCase_	:					Tuple     	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				UpperCAmelCase_	:					Tuple     	=   [floats_list((1, x)		)[0] for x in range(800      ,      1_400      ,      200		)]
				UpperCAmelCase_	:					Optional[Any]     	=   feat_extract(
				    lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      max_length=1_000      ,      padding="max_length"      ,      return_tensors="np"		)
				UpperCAmelCase_	:					Any     	=   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   :					int		)		->     Optional[Any]:
				UpperCAmelCase_	:					int     	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				UpperCAmelCase_	:					int     	=   [floats_list((1, x)		)[0] for x in range(800      ,      1_400      ,      200		)]
				UpperCAmelCase_	:					Optional[int]     	=   feat_extract(
				    lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      max_length=1_000      ,      padding="longest"      ,      return_tensors="np"		)
				UpperCAmelCase_	:					List[str]     	=   processed.input_values

				self._check_zero_mean_unit_variance(input_values[0, :800]		)
				self._check_zero_mean_unit_variance(input_values[1, :1_000]		)
				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, 1_000)		)

				UpperCAmelCase_	:					str     	=   [floats_list((1, x)		)[0] for x in range(800      ,      1_400      ,      200		)]
				UpperCAmelCase_	:					List[str]     	=   feat_extract(
				    lowerCAmelCase_      ,      truncation=lowerCAmelCase_      ,      max_length=2_000      ,      padding="longest"      ,      return_tensors="np"		)
				UpperCAmelCase_	:					Optional[Any]     	=   processed.input_values

				self._check_zero_mean_unit_variance(input_values[0, :800]		)
				self._check_zero_mean_unit_variance(input_values[1, :1_000]		)
				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, 1_200)		)





	@require_torch
	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[Any]:
				import torch

				UpperCAmelCase_	:					List[Any]     	=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
				UpperCAmelCase_	:					Optional[int]     	=   np.random.rand(100		).astype(np.floataa		)
				UpperCAmelCase_	:					Union[str, Any]     	=   np_speech_inputs.tolist()

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





	@slow
	@require_torch
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     List[str]:
				# this test makes sure that models that are using
				# group norm don't have their feature extractor return the
				# attention_mask
				for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
							UpperCAmelCase_	:					Optional[int]     	=   WavaVecaConfig.from_pretrained(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[Any]     	=   WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase_		)

							# only "layer" feature extraction norm should make use of
							# attention_mask
							self.assertEqual(feat_extract.return_attention_mask      ,      config.feat_extract_norm == "layer"		)











"""simple docstring"""
import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = torch.device('''cpu''')

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   "http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCAmelCase_	:					str     	=   Image.open(requests.get(A__      ,stream=A__		).raw		)
			return im

def 							snake_case (  A__		):
			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]		)

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]		)

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]		)

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]		)

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Tuple     	=   dct.pop(A__		)
			UpperCAmelCase_	:					Optional[Any]     	=   val

def 							snake_case (  A__		):
			UpperCAmelCase_	:					List[str]     	=   []
			for k in state_dict.keys():
						UpperCAmelCase_	:					Union[str, Any]     	=   k
						if ".pwconv" in k:
									UpperCAmelCase_	:					Dict     	=   k_new.replace(".pwconv"      ,".point_wise_conv"		)
						if ".dwconv" in k:
									UpperCAmelCase_	:					Any     	=   k_new.replace(".dwconv"      ,".depth_wise_conv"		)
						if ".Proj." in k:
									UpperCAmelCase_	:					Dict     	=   k_new.replace(".Proj."      ,".proj."		)
						if "patch_embed" in k_new:
									UpperCAmelCase_	:					Tuple     	=   k_new.replace("patch_embed"      ,"swiftformer.patch_embed.patch_embedding"		)
						if "network" in k_new:
									UpperCAmelCase_	:					List[Any]     	=   k_new.split("."		)
									if ls[2].isdigit():
												UpperCAmelCase_	:					Tuple     	=   "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]		)
									else:
												UpperCAmelCase_	:					Optional[Any]     	=   k_new.replace("network"      ,"swiftformer.encoder.network"		)
						rename_keys.append((k, k_new)		)
			return rename_keys

@torch.no_grad()
def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			UpperCAmelCase_	:					Optional[Any]     	=   10_00
			UpperCAmelCase_	:					str     	=   "huggingface/label-files"
			UpperCAmelCase_	:					str     	=   "imagenet-1k-id2label.json"
			UpperCAmelCase_	:					List[str]     	=   json.load(open(hf_hub_download(A__      ,A__      ,repo_type="dataset"		)      ,"r"		)		)
			UpperCAmelCase_	:					Tuple     	=   {int(A__		): v for k, v in idalabel.items()}
			UpperCAmelCase_	:					List[Any]     	=   idalabel
			UpperCAmelCase_	:					Optional[Any]     	=   {v: k for k, v in idalabel.items()}

			# size of the architecture
			if swiftformer_name == "swiftformer_xs":
						UpperCAmelCase_	:					Tuple     	=   [3, 3, 6, 4]
						UpperCAmelCase_	:					str     	=   [48, 56, 1_12, 2_20]

			elif swiftformer_name == "swiftformer_s":
						UpperCAmelCase_	:					Optional[Any]     	=   [3, 3, 9, 6]
						UpperCAmelCase_	:					Optional[Any]     	=   [48, 64, 1_68, 2_24]

			elif swiftformer_name == "swiftformer_l1":
						UpperCAmelCase_	:					int     	=   [4, 3, 10, 5]
						UpperCAmelCase_	:					Union[str, Any]     	=   [48, 96, 1_92, 3_84]

			elif swiftformer_name == "swiftformer_l3":
						UpperCAmelCase_	:					Dict     	=   [4, 4, 12, 6]
						UpperCAmelCase_	:					Optional[int]     	=   [64, 1_28, 3_20, 5_12]

			# load state_dict of original model, remove and rename some keys
			if original_ckpt:
						if original_ckpt.startswith("https"		):
									UpperCAmelCase_	:					List[Any]     	=   torch.hub.load_state_dict_from_url(A__      ,map_location="cpu"      ,check_hash=A__		)
						else:
									UpperCAmelCase_	:					Any     	=   torch.load(A__      ,map_location="cpu"		)
			UpperCAmelCase_	:					List[str]     	=   checkpoint

			UpperCAmelCase_	:					Dict     	=   create_rename_keys(A__		)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(A__      ,A__      ,A__		)

			# load HuggingFace model
			UpperCAmelCase_	:					Optional[int]     	=   SwiftFormerForImageClassification(A__		).eval()
			hf_model.load_state_dict(A__		)

			# prepare test inputs
			UpperCAmelCase_	:					Tuple     	=   prepare_img()
			UpperCAmelCase_	:					int     	=   ViTImageProcessor.from_pretrained("preprocessor_config"		)
			UpperCAmelCase_	:					int     	=   processor(images=A__      ,return_tensors="pt"		)

			# compare outputs from both models
			UpperCAmelCase_	:					List[Any]     	=   get_expected_output(A__		)
			UpperCAmelCase_	:					int     	=   hf_model(inputs["pixel_values"]		).logits

			assert hf_logits.shape == torch.Size([1, 10_00]		)
			assert torch.allclose(hf_logits[0, 0:5]      ,A__      ,atol=1e-3		)

			Path(A__		).mkdir(exist_ok=A__		)
			print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""		)
			hf_model.save_pretrained(A__		)


if __name__ == "__main__":
			lowerCamelCase_							      = argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--swiftformer_name''',
			    default='''swiftformer_xs''',
			    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
			    type=str,
			    help='''Name of the SwiftFormer model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default='''./converted_outputs/''',
			    type=str,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

			lowerCamelCase_							      = parser.parse_args()
			convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)



