'''simple docstring'''

import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RwkvForCausalLM,
						    RwkvModel,
						)
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
						A_        =   False




class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			def __init__(       self							, snake_case_							, snake_case_=14							, snake_case_=7							, snake_case_=True							, snake_case_=False							, snake_case_=True							, snake_case_=True							, snake_case_=True							, snake_case_=99							, snake_case_=32							, snake_case_=5							, snake_case_=37							, snake_case_="gelu"							, snake_case_=0.1							, snake_case_=0.1							, snake_case_=512							, snake_case_=16							, snake_case_=2							, snake_case_=3							, snake_case_=4							, snake_case_=None							, )     ->   Any:
									snake_case   :					str							=						parent
									snake_case   :					Optional[Any]							=						batch_size
									snake_case   :					List[Any]							=						seq_length
									snake_case   :					Dict							=						is_training
									snake_case   :					Dict							=						use_token_type_ids
									snake_case   :					List[Any]							=						use_input_mask
									snake_case   :					int							=						use_labels
									snake_case   :					List[Any]							=						use_mc_token_ids
									snake_case   :					Any							=						vocab_size
									snake_case   :					Dict							=						hidden_size
									snake_case   :					int							=						num_hidden_layers
									snake_case   :					int							=						intermediate_size
									snake_case   :					List[Any]							=						hidden_act
									snake_case   :					List[str]							=						hidden_dropout_prob
									snake_case   :					Any							=						attention_probs_dropout_prob
									snake_case   :					List[str]							=						max_position_embeddings
									snake_case   :					int							=						type_vocab_size
									snake_case   :					str							=						type_sequence_label_size
									snake_case   :					Optional[int]							=						num_labels
									snake_case   :					Dict							=						num_choices
									snake_case   :					Union[str, Any]							=						scope
									snake_case   :					Optional[int]							=						vocab_size - 1
									snake_case   :					int							=						vocab_size - 1
									snake_case   :					Dict							=						vocab_size - 1



			def      lowerCamelCase   (       self				)     ->   Any:
									return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"				)



			def      lowerCamelCase   (       self							, snake_case_=False							, snake_case_=False							, snake_case_=False				)     ->   Dict:
									snake_case   :					Optional[int]							=						ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size				)

									snake_case   :					str							=						None
									if self.use_input_mask:
															snake_case   :					Optional[Any]							=						random_attention_mask([self.batch_size, self.seq_length]				)

									snake_case   :					Dict							=						None
									if self.use_token_type_ids:
															snake_case   :					str							=						ids_tensor([self.batch_size, self.seq_length]							, self.type_vocab_size				)

									snake_case   :					Tuple							=						None
									if self.use_mc_token_ids:
															snake_case   :					Tuple							=						ids_tensor([self.batch_size, self.num_choices]							, self.seq_length				)

									snake_case   :					Dict							=						None
									snake_case   :					Union[str, Any]							=						None
									snake_case   :					Union[str, Any]							=						None
									if self.use_labels:
															snake_case   :					str							=						ids_tensor([self.batch_size]							, self.type_sequence_label_size				)
															snake_case   :					Tuple							=						ids_tensor([self.batch_size, self.seq_length]							, self.num_labels				)
															snake_case   :					Dict							=						ids_tensor([self.batch_size]							, self.num_choices				)

									snake_case   :					Dict							=						self.get_config(
									    gradient_checkpointing=snake_case_							, scale_attn_by_inverse_layer_idx=snake_case_							, reorder_and_upcast_attn=snake_case_							, )

									return (
									    config,
									    input_ids,
									    input_mask,
									    None,
									    token_type_ids,
									    mc_token_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									)



			def      lowerCamelCase   (       self							, snake_case_=False							, snake_case_=False							, snake_case_=False				)     ->   Any:
									return RwkvConfig(
									    vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, intermediate_size=self.intermediate_size							, activation_function=self.hidden_act							, resid_pdrop=self.hidden_dropout_prob							, attn_pdrop=self.attention_probs_dropout_prob							, n_positions=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, use_cache=snake_case_							, bos_token_id=self.bos_token_id							, eos_token_id=self.eos_token_id							, pad_token_id=self.pad_token_id							, gradient_checkpointing=snake_case_							, scale_attn_by_inverse_layer_idx=snake_case_							, reorder_and_upcast_attn=snake_case_							, )



			def      lowerCamelCase   (       self				)     ->   str:
									snake_case   :					List[str]							=						self.get_config()
									snake_case   :					int							=						300
									return config



			def      lowerCamelCase   (       self				)     ->   Optional[int]:
									(
									    snake_case				
									)   :					Tuple							=						self.prepare_config_and_inputs()

									snake_case   :					Tuple							=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
									snake_case   :					Optional[Any]							=						ids_tensor([self.batch_size, self.seq_length]							, vocab_size=2				)

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									    token_type_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, *snake_case_				)     ->   Optional[Any]:
									snake_case   :					Dict							=						True
									snake_case   :					List[str]							=						RwkvModel(config=snake_case_				)
									model.to(snake_case_				)
									model.eval()

									snake_case   :					List[str]							=						model(snake_case_				)

									self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)				)
									self.parent.assertEqual(len(result.hidden_states				)							, config.num_hidden_layers + 1				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, *snake_case_				)     ->   Union[str, Any]:
									snake_case   :					int							=						RwkvForCausalLM(snake_case_				)
									model.to(snake_case_				)
									model.eval()

									snake_case   :					Any							=						model(snake_case_							, labels=snake_case_				)
									self.parent.assertEqual(result.loss.shape							, ()				)
									self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, *snake_case_				)     ->   Union[str, Any]:
									snake_case   :					List[str]							=						RwkvModel(config=snake_case_				)
									model.to(snake_case_				)
									model.eval()

									snake_case   :					List[Any]							=						model(snake_case_				)
									snake_case   :					Optional[Any]							=						outputs.last_hidden_state

									snake_case   :					str							=						model(input_ids[:, :2]				)
									snake_case   :					Tuple							=						outputs.last_hidden_state

									# Using the state computed on the first inputs, we will get the same output
									snake_case   :					Union[str, Any]							=						model(input_ids[:, 2:]							, state=outputs.state				)
									snake_case   :					Optional[int]							=						outputs.last_hidden_state

									self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]							, dim=1				)							, snake_case_							, atol=1e-5				)				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, *snake_case_							, snake_case_=False				)     ->   Tuple:
									snake_case   :					Optional[int]							=						RwkvForCausalLM(snake_case_				)
									model.to(snake_case_				)
									if gradient_checkpointing:
															model.gradient_checkpointing_enable()

									snake_case   :					Optional[Any]							=						model(snake_case_							, labels=snake_case_				)
									self.parent.assertEqual(result.loss.shape							, ()				)
									self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)				)
									result.loss.backward()






			def      lowerCamelCase   (       self				)     ->   Optional[int]:
									snake_case   :					int							=						self.prepare_config_and_inputs()

									(
									    snake_case				
									)   :					Tuple							=						config_and_inputs

									snake_case   :					Dict							=						{"input_ids": input_ids}

									return config, inputs_dict







@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0       ,							reason="""See https://github.com/huggingface/transformers/pull/24204"""			)
@require_torch
class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase       ,							_UpperCAmelCase       ,							_UpperCAmelCase       ,							unittest.TestCase			):



			"""simple docstring"""






			__magic_name__   :							int             =					(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
			__magic_name__   :							Optional[Any]             =					(
			    {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
			)
			# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
			__magic_name__   :							List[Any]             =					False
			__magic_name__   :							Dict             =					False
			__magic_name__   :							Optional[Any]             =					False
			__magic_name__   :							List[str]             =					False
			__magic_name__   :							List[str]             =					False  # Rwkv does not support head masking



			def      lowerCamelCase   (       self				)     ->   int:
									snake_case   :					Any							=						RwkvModelTester(self				)
									snake_case   :					List[Any]							=						ConfigTester(
									    self							, config_class=snake_case_							, n_embd=37							, common_properties=["hidden_size", "num_hidden_layers"]				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_=None				)     ->   Any:
									if isinstance(snake_case_							, torch.Tensor				):
															snake_case   :					Optional[int]							=						member.max().item(), member.min().item()
									elif isinstance(snake_case_							, snake_case_				) or isinstance(snake_case_							, snake_case_				):
															snake_case   :					Dict							=						max(snake_case_				), min(snake_case_				)

									if not isinstance(snake_case_							, snake_case_				):
															raise TypeError("container should be a list or tuple"				)
									elif len(snake_case_				) != 2:
															raise ValueError("container should have 2 elements"				)

									snake_case   :					Tuple							=						container

									snake_case   :					Tuple							=						(min_value >= expected_min) and (max_value <= expected_max)

									if not is_inside_interval:
															snake_case   :					Optional[int]							=						"%s not found in %s" % (safe_repr(snake_case_				), safe_repr(snake_case_				))
															self.fail(self._formatMessage(snake_case_							, snake_case_				)				)



			def      lowerCamelCase   (       self				)     ->   Tuple:
									self.config_tester.run_common_tests()



			def      lowerCamelCase   (       self				)     ->   str:
									snake_case   :					int							=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_rwkv_model(*snake_case_				)



			def      lowerCamelCase   (       self				)     ->   Optional[Any]:
									snake_case   :					str							=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_causl_lm(*snake_case_				)



			def      lowerCamelCase   (       self				)     ->   Optional[Any]:
									snake_case   :					List[Any]							=						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_state_equivalency(*snake_case_				)



			def      lowerCamelCase   (       self				)     ->   List[Any]:
									snake_case   :					List[Any]							=						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															snake_case   :					Union[str, Any]							=						model_class(config=snake_case_				)
															for name, param in model.named_parameters():
																					if "time_decay" in name:
																											if param.requires_grad:
																																	self.assertTrue(param.data.max().item() == 3.0				)
																																	self.assertTrue(param.data.min().item() == -5.0				)
																					elif "time_first" in name:
																											if param.requires_grad:
																																	# check if it's a ones like
																																	self.assertTrue(torch.allclose(param.data							, torch.ones_like(param.data				)							, atol=1e-5							, rtol=1e-5				)				)
																					elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]				):
																											if param.requires_grad:
																																	self.assertInterval(
																																	    param.data							, [0.0, 1.0]							, msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							, )
																					elif "time_mix_value" in name:
																											if param.requires_grad:
																																	self.assertInterval(
																																	    param.data							, [0.0, 1.3]							, msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""							, )



			def      lowerCamelCase   (       self				)     ->   Optional[Any]:
									snake_case   :					Optional[int]							=						self.model_tester.prepare_config_and_inputs_for_common()
									snake_case   :					Tuple							=						True

									snake_case   :					Optional[int]							=						getattr(self.model_tester							, "seq_length"							, snake_case_				)

									for model_class in self.all_model_classes:
															snake_case   :					str							=						True
															snake_case   :					Any							=						False
															snake_case   :					List[Any]							=						True
															snake_case   :					List[str]							=						model_class(snake_case_				)
															model.to(snake_case_				)
															model.eval()

															snake_case   :					Union[str, Any]							=						self._prepare_for_class(snake_case_							, snake_case_				)
															snake_case   :					List[Any]							=						inputs["input_ids"].shape[0]
															with torch.no_grad():
																					snake_case   :					Tuple							=						model(**snake_case_				)
															snake_case   :					Dict							=						outputs.attentions
															self.assertEqual(len(snake_case_				)							, self.model_tester.num_hidden_layers				)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															snake_case   :					List[Any]							=						True
															snake_case   :					Dict							=						model_class(snake_case_				)
															model.to(snake_case_				)
															model.eval()

															snake_case   :					Tuple							=						self._prepare_for_class(snake_case_							, snake_case_				)
															snake_case   :					str							=						inputs["input_ids"].shape[0]
															with torch.no_grad():
																					snake_case   :					List[str]							=						model(**snake_case_				)
															snake_case   :					Any							=						outputs.attentions
															self.assertEqual(len(snake_case_				)							, self.model_tester.num_hidden_layers				)

															self.assertListEqual(
															    list(attentions[0].shape[-3:]				)							, [batch_size, seq_len, config.hidden_size]							, )
															snake_case   :					str							=						len(snake_case_				)

															# Check attention is always last and order is fine
															snake_case   :					List[str]							=						True
															snake_case   :					int							=						True
															snake_case   :					List[str]							=						model_class(snake_case_				)
															model.to(snake_case_				)
															model.eval()

															snake_case   :					List[str]							=						self._prepare_for_class(snake_case_							, snake_case_				)
															snake_case   :					List[str]							=						inputs["input_ids"].shape[0]
															with torch.no_grad():
																					snake_case   :					Tuple							=						model(**snake_case_				)

															snake_case   :					Tuple							=						1
															self.assertEqual(out_len + added_hidden_states							, len(snake_case_				)				)

															snake_case   :					Tuple							=						outputs.attentions

															self.assertEqual(len(snake_case_				)							, self.model_tester.num_hidden_layers				)
															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]				)							, [batch_size, seq_len, config.hidden_size]							, )






			@slow
			def      lowerCamelCase   (       self				)     ->   Dict:
									for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case   :					int							=						RwkvModel.from_pretrained(snake_case_				)
															self.assertIsNotNone(snake_case_				)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0       ,							reason="""See https://github.com/huggingface/transformers/pull/24204"""			)
@slow
class 		SCREAMING_SNAKE_CASE_				(unittest.TestCase			):



			"""simple docstring"""






			def      lowerCamelCase   (       self				)     ->   Tuple:
									snake_case   :					Optional[Any]							=						"RWKV/rwkv-4-169m-pile"
									snake_case   :					Any							=						AutoTokenizer.from_pretrained(self.model_id				)



			def      lowerCamelCase   (       self				)     ->   List[str]:
									snake_case   :					str							=						"Hello my name is Jasmine and I am a newbie to the"
									snake_case   :					Any							=						RwkvForCausalLM.from_pretrained(self.model_id				).to(snake_case_				)

									snake_case   :					Dict							=						self.tokenizer("Hello my name is"							, return_tensors="pt"				).input_ids.to(snake_case_				)
									snake_case   :					List[str]							=						model.generate(snake_case_							, max_new_tokens=10				)
									snake_case   :					Optional[int]							=						self.tokenizer.decode(output[0].tolist()				)

									self.assertEqual(snake_case_							, snake_case_				)






			def      lowerCamelCase   (       self				)     ->   Any:
									snake_case   :					Tuple							=						"Hello my name is Jasmine and I am a newbie to the"

									snake_case   :					Optional[int]							=						self.tokenizer("Hello my name is"							, return_tensors="pt"				).input_ids.to(snake_case_				)
									snake_case   :					Optional[Any]							=						RwkvForCausalLM.from_pretrained(self.model_id							, torch_dtype=torch.bfloataa				).to(snake_case_				)

									snake_case   :					int							=						model.generate(snake_case_							, max_new_tokens=10				)
									snake_case   :					List[str]							=						self.tokenizer.decode(output[0].tolist()				)

									self.assertEqual(snake_case_							, snake_case_				)






'''simple docstring'''

import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Optional[Any])						->		List[Any]:



						"""simple docstring"""

						snake_case   :					List[str]							=						"https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"

						with open(_lowerCAmelCase,			"wb") as f:
												with requests.get(_lowerCAmelCase,			stream=_lowerCAmelCase) as req:
																		for chunk in req.iter_content(chunk_size=2048):
																								f.write(_lowerCAmelCase)


def 					UpperCAmelCase__      (							)						->		VivitConfig:



						"""simple docstring"""

						snake_case   :					Any							=						VivitConfig()

						snake_case   :					Optional[int]							=						400
						snake_case   :					Tuple							=						"huggingface/label-files"
						snake_case   :					Tuple							=						"kinetics400-id2label.json"

						snake_case   :					str							=						json.load(open(hf_hub_download(_lowerCAmelCase,			_lowerCAmelCase,			repo_type="dataset"),			"r"))
						snake_case   :					Optional[int]							=						{int(_lowerCAmelCase): v for k, v in idalabel.items()}
						snake_case   :					Union[str, Any]							=						idalabel
						snake_case   :					Tuple							=						{v: k for k, v in idalabel.items()}
						return config


def 					UpperCAmelCase__      (							)						->		int:



						"""simple docstring"""

						snake_case   :					List[Any]							=						hf_hub_download(
						    repo_id="hf-internal-testing/spaghetti-video",			filename="eating_spaghetti_32_frames.npy",			repo_type="dataset")
						snake_case   :					int							=						np.load(_lowerCAmelCase)
						return list(_lowerCAmelCase)


def 					UpperCAmelCase__      (							_lowerCAmelCase						: np.ndarray)						->		str:



						"""simple docstring"""

						if np.ndim(_lowerCAmelCase) == 2:
												return transform_attention_bias(_lowerCAmelCase)

						elif np.ndim(_lowerCAmelCase) == 3:
												return transform_attention_kernel(_lowerCAmelCase)

						else:
												raise Exception(f"""Invalid number of dimesions: {np.ndim(_lowerCAmelCase)}""")


def 					UpperCAmelCase__      (							_lowerCAmelCase						: np.ndarray)						->		Optional[Any]:



						"""simple docstring"""

						return current.flatten()


def 					UpperCAmelCase__      (							_lowerCAmelCase						: np.ndarray)						->		List[Any]:



						"""simple docstring"""

						return np.reshape(_lowerCAmelCase,			(current.shape[0], current.shape[1] * current.shape[2])).T


def 					UpperCAmelCase__      (							_lowerCAmelCase						: np.ndarray)						->		Optional[Any]:



						"""simple docstring"""

						return np.reshape(_lowerCAmelCase,			(current.shape[0] * current.shape[1], current.shape[2])).T


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Union[str, Any],			_lowerCAmelCase						: List[Any])						->		Union[str, Any]:



						"""simple docstring"""

						snake_case   :					Union[str, Any]							=						state_dict["optimizer"]["target"]["Transformer"][f"""encoderblock_{i}"""]

						snake_case   :					Any							=						f"""encoder.layer.{i}."""
						snake_case   :					Optional[Any]							=						{
						    prefix + "intermediate.dense.bias": state["MlpBlock_0"]["Dense_0"]["bias"],
						    prefix + "intermediate.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_0"]["kernel"]),
						    prefix + "output.dense.bias": state["MlpBlock_0"]["Dense_1"]["bias"],
						    prefix + "output.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_1"]["kernel"]),
						    prefix + "layernorm_before.bias": state["LayerNorm_0"]["bias"],
						    prefix + "layernorm_before.weight": state["LayerNorm_0"]["scale"],
						    prefix + "layernorm_after.bias": state["LayerNorm_1"]["bias"],
						    prefix + "layernorm_after.weight": state["LayerNorm_1"]["scale"],
						    prefix
						    + "attention.attention.query.bias": transform_attention(
						        state["MultiHeadDotProductAttention_0"]["query"]["bias"]),
						    prefix
						    + "attention.attention.query.weight": transform_attention(
						        state["MultiHeadDotProductAttention_0"]["query"]["kernel"]),
						    prefix
						    + "attention.attention.key.bias": transform_attention(state["MultiHeadDotProductAttention_0"]["key"]["bias"]),
						    prefix
						    + "attention.attention.key.weight": transform_attention(
						        state["MultiHeadDotProductAttention_0"]["key"]["kernel"]),
						    prefix
						    + "attention.attention.value.bias": transform_attention(
						        state["MultiHeadDotProductAttention_0"]["value"]["bias"]),
						    prefix
						    + "attention.attention.value.weight": transform_attention(
						        state["MultiHeadDotProductAttention_0"]["value"]["kernel"]),
						    prefix + "attention.output.dense.bias": state["MultiHeadDotProductAttention_0"]["out"]["bias"],
						    prefix
						    + "attention.output.dense.weight": transform_attention_output_weight(
						        state["MultiHeadDotProductAttention_0"]["out"]["kernel"]),
						}

						return new_state


def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[Any])						->		List[Any]:



						"""simple docstring"""

						return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()])


def 					UpperCAmelCase__      (							_lowerCAmelCase						: List[Any],			_lowerCAmelCase						: List[Any]=False)						->		List[Any]:



						"""simple docstring"""

						snake_case   :					Dict							=						get_n_layers(_lowerCAmelCase)

						snake_case   :					Tuple							=						OrderedDict()

						snake_case   :					List[str]							=						state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
						snake_case   :					List[str]							=						state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

						snake_case   :					Tuple							=						np.transpose(
						    state_dict["optimizer"]["target"]["embedding"]["kernel"],			(4, 3, 0, 1, 2))
						snake_case   :					Optional[Any]							=						state_dict["optimizer"]["target"]["embedding"]["bias"]

						snake_case   :					List[Any]							=						state_dict["optimizer"]["target"]["cls"]
						snake_case   :					Tuple							=						state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][
						    "pos_embedding"
						]

						for i in range(_lowerCAmelCase):
												new_state.update(transform_state_encoder_block(_lowerCAmelCase,			_lowerCAmelCase))

						if classification_head:
												snake_case   :					Tuple							=						{"vivit." + k: v for k, v in new_state.items()}
												snake_case   :					Union[str, Any]							=						np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"])
												snake_case   :					Optional[Any]							=						np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"])

						return {k: torch.tensor(_lowerCAmelCase) for k, v in new_state.items()}


def 					UpperCAmelCase__      (							)						->		VivitImageProcessor:



						"""simple docstring"""

						snake_case   :					int							=						VivitImageProcessor()

						assert extractor.do_resize is True
						assert extractor.size == {"shortest_edge": 256}
						assert extractor.do_center_crop is True
						assert extractor.crop_size == {"width": 224, "height": 224}
						assert extractor.resample == PILImageResampling.BILINEAR

						# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
						# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
						# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
						assert extractor.do_normalize is False
						assert extractor.do_rescale is True
						assert extractor.rescale_factor == 1 / 255

						# zero-centering = True in original implementation
						assert extractor.do_zero_centering is True

						return extractor


def 					UpperCAmelCase__      (							_lowerCAmelCase						: str)						->		Union[str, Any]:



						"""simple docstring"""

						snake_case   :					str							=						"checkpoint"

						if not os.path.exists(_lowerCAmelCase):
												download_checkpoint(_lowerCAmelCase)

						snake_case   :					Union[str, Any]							=						restore_checkpoint(_lowerCAmelCase,			_lowerCAmelCase)
						snake_case   :					Optional[Any]							=						transform_state(_lowerCAmelCase,			classification_head=_lowerCAmelCase)

						snake_case   :					List[Any]							=						get_vivit_config()

						assert config.image_size == 224
						assert config.num_frames == 32

						snake_case   :					Union[str, Any]							=						VivitForVideoClassification(_lowerCAmelCase)
						model.load_state_dict(_lowerCAmelCase)
						model.eval()

						snake_case   :					Any							=						get_processor()

						snake_case   :					Dict							=						prepare_video()
						snake_case   :					Union[str, Any]							=						extractor(_lowerCAmelCase,			return_tensors="pt")

						snake_case   :					Tuple							=						model(**_lowerCAmelCase)

						snake_case   :					Any							=						torch.Size([1, 400])
						snake_case   :					int							=						torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658])

						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, :5],			_lowerCAmelCase,			atol=1E-4), outputs.logits[0, :5]

						model.save_pretrained(_lowerCAmelCase)
						extractor.save_pretrained(_lowerCAmelCase)


if __name__ == "__main__":
						A_        =   argparse.ArgumentParser()

						parser.add_argument("--output_model_name", "-o", type=str, help="Output path for the converted HuggingFace model")

						A_        =   parser.parse_args()
						convert(args.output_model_name)





