

"""simple docstring"""





import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class       _lowerCAmelCase						(			lowercase  ,unittest.TestCase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Optional[int]     	=			GPTNeoXJapaneseTokenizer
 __UpperCAmelCase			:      List[Any]     	=			False
 __UpperCAmelCase			:      Optional[Any]     	=			{"do_clean_text": False, "add_prefix_space": False}


 def       _lowercase				(    self							:    List[str]			):
    super().setUp()

    __lowercase			=		[
        "こん",
        "こんに",
        "にちは",
        "ばんは",
        "世界,㔺界",
        "、",
        "。",
        "<BR>",
        "<SP>",
        "<TAB>",
        "<URL>",
        "<EMAIL>",
        "<TEL>",
        "<DATE>",
        "<PRICE>",
        "<BLOCK>",
        "<KIGOU>",
        "<U2000U2BFF>",
        "<|emoji1|>",
        "<unk>",
        "<|startoftext|>",
        "<|endoftext|>",
    ]
    __lowercase			=		{"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
    __lowercase			=		{"unk_token": "<unk>"}

    __lowercase			=		os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES["vocab_file"]			)
    __lowercase			=		os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES["emoji_file"]			)
    with open(self.vocab_file,      "w",      encoding="utf-8"			) as vocab_writer:
       vocab_writer.write("".join([x + "\n" for x in vocab_tokens]			)			)
    with open(self.emoji_file,      "w"			) as emoji_writer:
       emoji_writer.write(json.dumps(UpperCAmelCase__			)			)


 def       _lowercase				(    self							:    str,      **UpperCAmelCase__							:    List[str]			):
    kwargs.update(self.special_tokens_map			)
    return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname,      **UpperCAmelCase__			)


 def       _lowercase				(    self							:    Dict,      UpperCAmelCase__							:    Any			):
    __lowercase			=		"こんにちは、世界。 \nこんばんは、㔺界。😀"
    __lowercase			=		"こんにちは、世界。 \nこんばんは、世界。😀"
    return input_text, output_text


 def       _lowercase				(    self							:    str,      UpperCAmelCase__							:    Dict			):
    __lowercase	,__lowercase			=		self.get_input_output_texts(UpperCAmelCase__			)
    __lowercase			=		tokenizer.encode(UpperCAmelCase__,      add_special_tokens=UpperCAmelCase__			)
    __lowercase			=		tokenizer.decode(UpperCAmelCase__,      clean_up_tokenization_spaces=UpperCAmelCase__			)
    return text, ids


 def       _lowercase				(    self							:    Any			):
    pass  # TODO add if relevant


 def       _lowercase				(    self							:    Optional[Any]			):
    pass  # TODO add if relevant


 def       _lowercase				(    self							:    List[str]			):
    pass  # TODO add if relevant


 def       _lowercase				(    self							:    str			):
    __lowercase			=		self.get_tokenizer()

    # Testing tokenization
    __lowercase			=		"こんにちは、世界。　こんばんは、㔺界。"
    __lowercase			=		["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
    __lowercase			=		tokenizer.tokenize(UpperCAmelCase__			)
    self.assertListEqual(UpperCAmelCase__,      UpperCAmelCase__			)

    # Testing conversion to ids without special tokens
    __lowercase			=		[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
    __lowercase			=		tokenizer.convert_tokens_to_ids(UpperCAmelCase__			)
    self.assertListEqual(UpperCAmelCase__,      UpperCAmelCase__			)

    # Testing conversion to ids with special tokens
    __lowercase			=		tokens + [tokenizer.unk_token]
    __lowercase			=		[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
    __lowercase			=		tokenizer.convert_tokens_to_ids(UpperCAmelCase__			)
    self.assertListEqual(UpperCAmelCase__,      UpperCAmelCase__			)


 @slow
 def       _lowercase				(    self							:    str			):
    __lowercase			=		self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"			)

    __lowercase			=		tokenizer.encode("ありがとう。",      add_special_tokens=UpperCAmelCase__			)
    __lowercase			=		tokenizer.encode("どういたしまして。",      add_special_tokens=UpperCAmelCase__			)

    __lowercase			=		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__			)
    __lowercase			=		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__,      UpperCAmelCase__			)

    assert encoded_sentence == ids_a
    assert encoded_pair == ids_a + ids_a


 def       _lowercase				(    self							:    Tuple			):
    # Intentionally convert some words to accommodate character fluctuations unique to Japanese
    pass



 def       _lowercase				(    self							:    Any			):
    # tokenizer has no padding token
    pass







"""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 						_A						(						UpperCamelCase_  : str)			->		Optional[Any]:
   '''simple docstring'''


   __lowercase			=		"https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"

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




def 						_A						(						)			->		VivitConfig:
   '''simple docstring'''


   __lowercase			=		VivitConfig()

   __lowercase			=		400
   __lowercase			=		"huggingface/label-files"
   __lowercase			=		"kinetics400-id2label.json"

   __lowercase			=		json.load(open(hf_hub_download(UpperCamelCase_,    UpperCamelCase_,    repo_type="dataset"),    "r"))
   __lowercase			=		{int(UpperCamelCase_): v for k, v in idalabel.items()}
   __lowercase			=		idalabel
   __lowercase			=		{v: k for k, v in idalabel.items()}
   return config




def 						_A						(						)			->		List[Any]:
   '''simple docstring'''


   __lowercase			=		hf_hub_download(
       repo_id="hf-internal-testing/spaghetti-video",    filename="eating_spaghetti_32_frames.npy",    repo_type="dataset")
   __lowercase			=		np.load(UpperCamelCase_)
   return list(UpperCamelCase_)




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Optional[int]:
   '''simple docstring'''


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

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

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




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Optional[Any]:
   '''simple docstring'''


   return current.flatten()




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Any:
   '''simple docstring'''


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




def 						_A						(						UpperCamelCase_  : np.ndarray)			->		Any:
   '''simple docstring'''


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




def 						_A						(						UpperCamelCase_  : Tuple,    UpperCamelCase_  : Optional[Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"][F"""encoderblock_{i}"""]

   __lowercase			=		F"""encoder.layer.{i}."""
   __lowercase			=		{
       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 						_A						(						UpperCamelCase_  : str)			->		List[Any]:
   '''simple docstring'''


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




def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : Any=False)			->		Any:
   '''simple docstring'''


   __lowercase			=		get_n_layers(UpperCamelCase_)

   __lowercase			=		OrderedDict()

   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

   __lowercase			=		np.transpose(
       state_dict["optimizer"]["target"]["embedding"]["kernel"],    (4, 3, 0, 1, 2))
   __lowercase			=		state_dict["optimizer"]["target"]["embedding"]["bias"]

   __lowercase			=		state_dict["optimizer"]["target"]["cls"]
   __lowercase			=		state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][
       "pos_embedding"
   ]

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

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

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




def 						_A						(						)			->		VivitImageProcessor:
   '''simple docstring'''


   __lowercase			=		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 						_A						(						UpperCamelCase_  : str)			->		List[str]:
   '''simple docstring'''


   __lowercase			=		"checkpoint"

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

   __lowercase			=		restore_checkpoint(UpperCamelCase_,    UpperCamelCase_)
   __lowercase			=		transform_state(UpperCamelCase_,    classification_head=UpperCamelCase_)

   __lowercase			=		get_vivit_config()

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

   __lowercase			=		VivitForVideoClassification(UpperCamelCase_)
   model.load_state_dict(UpperCamelCase_)
   model.eval()

   __lowercase			=		get_processor()

   __lowercase			=		prepare_video()
   __lowercase			=		extractor(UpperCamelCase_,    return_tensors="pt")

   __lowercase			=		model(**UpperCamelCase_)

   __lowercase			=		torch.Size([1, 400])
   __lowercase			=		torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658])

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

   model.save_pretrained(UpperCamelCase_)
   extractor.save_pretrained(UpperCamelCase_)


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)




