
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 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  Optional[int] 			=   """https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"""

       with open(SCREAMING_SNAKE_CASE__      ,			"""wb"""       ) as f:
              with requests.get(SCREAMING_SNAKE_CASE__      ,			stream=SCREAMING_SNAKE_CASE__       ) as req:
                     for chunk in req.iter_content(chunk_size=2_048       ):
                            f.write(SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						)				->					VivitConfig:
       lowercase							:  Optional[Any] 			=   VivitConfig()

       lowercase							:  Tuple 			=   400
       lowercase							:  int 			=   """huggingface/label-files"""
       lowercase							:  Any 			=   """kinetics400-id2label.json"""

       lowercase							:  Optional[int] 			=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			repo_type="""dataset"""       )      ,			"""r"""       )       )
       lowercase							:  Dict 			=   {int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
       lowercase							:  Optional[Any] 			=   idalabel
       lowercase							:  Any 			=   {v: k for k, v in idalabel.items()}
       return config







def 		_snake_case(						)				->					Tuple:
       lowercase							:  int 			=   hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video"""      ,			filename="""eating_spaghetti_32_frames.npy"""      ,			repo_type="""dataset"""       )
       lowercase							:  int 			=   np.load(SCREAMING_SNAKE_CASE__       )
       return list(SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       if np.ndim(SCREAMING_SNAKE_CASE__       ) == 2:
              return transform_attention_bias(SCREAMING_SNAKE_CASE__       )

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

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







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Any:
       return current.flatten()







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[str]:
       return np.reshape(SCREAMING_SNAKE_CASE__      ,			(current.shape[0], current.shape[1] * current.shape[2])       ).T







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					int:
       return np.reshape(SCREAMING_SNAKE_CASE__      ,			(current.shape[0] * current.shape[1], current.shape[2])       ).T







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  int 			=   state_dict["""optimizer"""]["""target"""]["""Transformer"""][f"encoderblock_{i}"]

       lowercase							:  List[Any] 			=   f"encoder.layer.{i}."
       lowercase							:  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 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Union[str, Any]:
       return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=False       )				->					List[Any]:
       lowercase							:  List[str] 			=   get_n_layers(SCREAMING_SNAKE_CASE__       )

       lowercase							:  Any 			=   OrderedDict()

       lowercase							:  int 			=   state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""bias"""]
       lowercase							:  Optional[Any] 			=   state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""encoder_norm"""]["""scale"""]

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

       lowercase							:  Dict 			=   state_dict["""optimizer"""]["""target"""]["""cls"""]
       lowercase							:  str 			=   state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""posembed_input"""][
           """pos_embedding"""
       ]

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

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

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







def 		_snake_case(						)				->					VivitImageProcessor:
       lowercase							:  Tuple 			=   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 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       lowercase							:  Optional[int] 			=   """checkpoint"""

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

       lowercase							:  List[Any] 			=   restore_checkpoint(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
       lowercase							:  List[Any] 			=   transform_state(SCREAMING_SNAKE_CASE__      ,			classification_head=SCREAMING_SNAKE_CASE__       )

       lowercase							:  str 			=   get_vivit_config()

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

       lowercase							:  Tuple 			=   VivitForVideoClassification(SCREAMING_SNAKE_CASE__       )
       model.load_state_dict(SCREAMING_SNAKE_CASE__       )
       model.eval()

       lowercase							:  Any 			=   get_processor()

       lowercase							:  Optional[int] 			=   prepare_video()
       lowercase							:  str 			=   extractor(SCREAMING_SNAKE_CASE__      ,			return_tensors="""pt"""       )

       lowercase							:  str 			=   model(**SCREAMING_SNAKE_CASE__       )

       lowercase							:  Optional[Any] 			=   torch.Size([1, 400]       )
       lowercase							:  List[Any] 			=   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]      ,			SCREAMING_SNAKE_CASE__      ,			atol=1e-4       ), outputs.logits[0, :5]

       model.save_pretrained(SCREAMING_SNAKE_CASE__       )
       extractor.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
  lowercase						:      Tuple							=							argparse.ArgumentParser()

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

  lowercase						:      Tuple							=							parser.parse_args()
  convert(args.output_model_name)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase						:      Any							=							{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[Any]							=							["""GPT2TokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							[
      """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPT2DoubleHeadsModel""",
      """GPT2ForQuestionAnswering""",
      """GPT2ForSequenceClassification""",
      """GPT2ForTokenClassification""",
      """GPT2LMHeadModel""",
      """GPT2Model""",
      """GPT2PreTrainedModel""",
      """load_tf_weights_in_gpt2""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      Tuple							=							[
      """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFGPT2DoubleHeadsModel""",
      """TFGPT2ForSequenceClassification""",
      """TFGPT2LMHeadModel""",
      """TFGPT2MainLayer""",
      """TFGPT2Model""",
      """TFGPT2PreTrainedModel""",
  ]

try:
  if not is_keras_nlp_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[str]							=							["""TFGPT2Tokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
  from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
  from .tokenization_gpta import GPTaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_fast import GPTaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpta import (
        GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTaDoubleHeadsModel,
        GPTaForQuestionAnswering,
        GPTaForSequenceClassification,
        GPTaForTokenClassification,
        GPTaLMHeadModel,
        GPTaModel,
        GPTaPreTrainedModel,
        load_tf_weights_in_gpta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_gpta import (
        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGPTaDoubleHeadsModel,
        TFGPTaForSequenceClassification,
        TFGPTaLMHeadModel,
        TFGPTaMainLayer,
        TFGPTaModel,
        TFGPTaPreTrainedModel,
    )

  try:
    if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_tf import TFGPTaTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
  import sys

  lowercase						:      Union[str, Any]							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
