import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_lowerCamelCase					:   List[str]							=		random.Random()

if is_torch_available():
       import torch





def 	__lowerCamelCase							(UpperCAmelCase__     :							Dict      , UpperCAmelCase__     :							str=1.0      , UpperCAmelCase__     :							Optional[int]=None      , UpperCAmelCase__     :							Tuple=None  ):
     if rng is None:
          SCREAMING_SNAKE_CASE    			=       global_rng

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

     return values


class 						lowercase    (     unittest.TestCase     ):




       def __init__(						self   :					int	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					int=7	,							_UpperCamelCase   :					Optional[Any]=400	,							_UpperCamelCase   :					List[str]=2_000	,							_UpperCamelCase   :					List[str]=1	,							_UpperCamelCase   :					List[str]=0.0	,							_UpperCamelCase   :					Optional[Any]=16_000	,							_UpperCamelCase   :					str=True	,							_UpperCamelCase   :					int=True	,							)					->				Dict:



            '''simple docstring'''

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




       def 	__snake_case(						self   :					Dict      )					->				str:



            '''simple docstring'''

            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 	__snake_case(						self   :					int	,							_UpperCamelCase   :					Union[str, Any]=False	,							_UpperCamelCase   :					Dict=False      )					->				Tuple:



            '''simple docstring'''

            def _flatten(_UpperCamelCase   :					int      ):
                 return list(itertools.chain(*_UpperCamelCase      )      )

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

            return speech_inputs



@require_torch
@require_torchaudio
class 						lowercase    (     a				,       unittest.TestCase     ):
       lowercase__						:       List[Any]								=							ASTFeatureExtractor




       def 	__snake_case(						self   :					Dict      )					->				Optional[Any]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       ASTFeatureExtractionTester(self      )




       def 	__snake_case(						self   :					Optional[Any]      )					->				Tuple:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
            # create three inputs of length 800, 1000, and 1200
            SCREAMING_SNAKE_CASE    			=       [floats_list((1, x)      )[0] for x in range(800	,							1_400	,							200      )]
            SCREAMING_SNAKE_CASE    			=       [np.asarray(_UpperCamelCase      ) for speech_input in speech_inputs]

            # Test not batched input
            SCREAMING_SNAKE_CASE    			=       feat_extract(speech_inputs[0]	,							return_tensors="np"      ).input_values
            SCREAMING_SNAKE_CASE    			=       feat_extract(np_speech_inputs[0]	,							return_tensors="np"      ).input_values
            self.assertTrue(np.allclose(_UpperCamelCase	,							_UpperCamelCase	,							atol=1e-3      )      )

            # Test batched
            SCREAMING_SNAKE_CASE    			=       feat_extract(_UpperCamelCase	,							padding=_UpperCamelCase	,							return_tensors="np"      ).input_values
            SCREAMING_SNAKE_CASE    			=       feat_extract(_UpperCamelCase	,							padding=_UpperCamelCase	,							return_tensors="np"      ).input_values
            for enc_seq_a, enc_seq_a in zip(_UpperCamelCase	,							_UpperCamelCase      ):
                 self.assertTrue(np.allclose(_UpperCamelCase	,							_UpperCamelCase	,							atol=1e-3      )      )

            # Test 2-D numpy arrays are batched.
            SCREAMING_SNAKE_CASE    			=       [floats_list((1, x)      )[0] for x in (800, 800, 800)]
            SCREAMING_SNAKE_CASE    			=       np.asarray(_UpperCamelCase      )
            SCREAMING_SNAKE_CASE    			=       feat_extract(_UpperCamelCase	,							return_tensors="np"      ).input_values
            SCREAMING_SNAKE_CASE    			=       feat_extract(_UpperCamelCase	,							return_tensors="np"      ).input_values
            for enc_seq_a, enc_seq_a in zip(_UpperCamelCase	,							_UpperCamelCase      ):
                 self.assertTrue(np.allclose(_UpperCamelCase	,							_UpperCamelCase	,							atol=1e-3      )      )




       @require_torch
       def 	__snake_case(						self   :					str      )					->				Dict:



            '''simple docstring'''

            import torch

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

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




       def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					Union[str, Any]      )					->				List[Any]:



            '''simple docstring'''

            from datasets import load_dataset

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

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





       @require_torch
       def 	__snake_case(						self   :					List[str]      )					->				Optional[int]:



            '''simple docstring'''

            SCREAMING_SNAKE_CASE    			=       torch.tensor(
                [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
                 -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
                 -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
                 -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]      )
            # fmt: on

            SCREAMING_SNAKE_CASE    			=       self._load_datasamples(1      )
            SCREAMING_SNAKE_CASE    			=       ASTFeatureExtractor()
            SCREAMING_SNAKE_CASE    			=       feature_extractor(_UpperCamelCase	,							return_tensors="pt"      ).input_values
            self.assertEquals(input_values.shape	,							(1, 1_024, 128)      )
            self.assertTrue(torch.allclose(input_values[0, 0, :30]	,							_UpperCamelCase	,							atol=1e-4      )      )





import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							Dict=False  ):
					SCREAMING_SNAKE_CASE    			=       []
					for i in range(config.num_hidden_layers  ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F"module.blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")  )
										rename_keys.append((F"module.blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")  )
										rename_keys.append(
										    (F"module.blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")  )
										rename_keys.append((F"module.blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")  )
										rename_keys.append((F"module.blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")  )
										rename_keys.append((F"module.blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")  )
										rename_keys.append((F"module.blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")  )

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

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

										# if just the base model, we should remove "vit" from all keys that start with "vit"
										SCREAMING_SNAKE_CASE    			=       [(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 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							int      , UpperCAmelCase__     :							str=False  ):
					for i in range(config.num_hidden_layers  ):
										if base_model:
															SCREAMING_SNAKE_CASE    			=       ""
										else:
															SCREAMING_SNAKE_CASE    			=       "vit."
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"module.blocks.{i}.attn.qkv.weight"  )
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"module.blocks.{i}.attn.qkv.bias"  )
										# next, add query, keys and values (in that order) to the state dict
										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    : config.hidden_size, :
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_bias[: config.hidden_size]
										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    -config.hidden_size :, :
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_bias[-config.hidden_size :]





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple  ):
					SCREAMING_SNAKE_CASE    			=       ["head.weight", "head.bias"]
					for k in ignore_keys:
										state_dict.pop(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any  ):
					# projection head is used in the self-supervised pre-training in MSN,
					# for downstream task it's not needed.
					SCREAMING_SNAKE_CASE    			=       [
					    "module.fc.fc1.weight",
					    "module.fc.fc1.bias",
					    "module.fc.bn1.weight",
					    "module.fc.bn1.bias",
					    "module.fc.bn1.running_mean",
					    "module.fc.bn1.running_var",
					    "module.fc.bn1.num_batches_tracked",
					    "module.fc.fc2.weight",
					    "module.fc.fc2.bias",
					    "module.fc.bn2.weight",
					    "module.fc.bn2.bias",
					    "module.fc.bn2.running_mean",
					    "module.fc.bn2.running_var",
					    "module.fc.bn2.num_batches_tracked",
					    "module.fc.fc3.weight",
					    "module.fc.fc3.bias",
					]
					for k in ignore_keys:
										state_dict.pop(UpperCAmelCase__      , UpperCAmelCase__  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Dict  ):
					SCREAMING_SNAKE_CASE    			=       dct.pop(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       val





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							str  ):
					SCREAMING_SNAKE_CASE    			=       ViTMSNConfig()
					SCREAMING_SNAKE_CASE    			=       1_0_0_0

					SCREAMING_SNAKE_CASE    			=       "datasets/huggingface/label-files"
					SCREAMING_SNAKE_CASE    			=       "imagenet-1k-id2label.json"
					SCREAMING_SNAKE_CASE    			=       json.load(open(hf_hub_download(UpperCAmelCase__      , UpperCAmelCase__  )      , "r"  )  )
					SCREAMING_SNAKE_CASE    			=       {int(UpperCAmelCase__  ): v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE    			=       idalabel
					SCREAMING_SNAKE_CASE    			=       {v: k for k, v in idalabel.items()}

					if "s16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       3_8_4
										SCREAMING_SNAKE_CASE    			=       1_5_3_6
										SCREAMING_SNAKE_CASE    			=       6
					elif "l16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       0.1
					elif "b4" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       4
					elif "l7" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       7
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       0.1

					SCREAMING_SNAKE_CASE    			=       ViTMSNModel(UpperCAmelCase__  )

					SCREAMING_SNAKE_CASE    			=       torch.hub.load_state_dict_from_url(UpperCAmelCase__      , map_location="cpu"  )["target_encoder"]

					SCREAMING_SNAKE_CASE    			=       ViTImageProcessor(size=config.image_size  )

					remove_projection_head(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       create_rename_keys(UpperCAmelCase__      , base_model=UpperCAmelCase__  )

					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )
					read_in_q_k_v(UpperCAmelCase__      , UpperCAmelCase__      , base_model=UpperCAmelCase__  )

					model.load_state_dict(UpperCAmelCase__  )
					model.eval()

					SCREAMING_SNAKE_CASE    			=       "http://images.cocodataset.org/val2017/000000039769.jpg"

					SCREAMING_SNAKE_CASE    			=       Image.open(requests.get(UpperCAmelCase__      , stream=UpperCAmelCase__  ).raw  )
					SCREAMING_SNAKE_CASE    			=       ViTImageProcessor(
					    size=config.image_size      , image_mean=UpperCAmelCase__      , image_std=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       image_processor(images=UpperCAmelCase__      , return_tensors="pt"  )

					# forward pass
					torch.manual_seed(2  )
					SCREAMING_SNAKE_CASE    			=       model(**UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       outputs.last_hidden_state

					# The following Colab Notebook was used to generate these outputs:
					# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
					if "s16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-1.0915, -1.4876, -1.1809]]  )
					elif "b16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[14.2889, -18.9045, 11.7281]]  )
					elif "l16" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[41.5028, -22.8681, 45.6475]]  )
					elif "b4" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-4.3868, 5.2932, -0.4137]]  )
					else:
										SCREAMING_SNAKE_CASE    			=       torch.tensor([[-0.1792, -0.6465, 2.4263]]  )

					# verify logits
					assert torch.allclose(last_hidden_state[:, 0, :3]      , UpperCAmelCase__      , atol=1e-4  )

					print(F"Saving model 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					:   Optional[Any]							=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--checkpoint_url''',
							    default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
							    type=str,
							    help='''URL of the checkpoint 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					:   Optional[Any]							=		parser.parse_args()
							convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




