






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__    (	A       ):
   lowercase__    :						List[str]       =			"""https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"""

   with open(A		,					"""wb"""       ) as f:
      with requests.get(A		,					stream=A       ) as req:
         for chunk in req.iter_content(chunk_size=2_048       ):
            f.write(A       )
def    a__    (	):
   lowercase__    :						Dict       =			VivitConfig()

   lowercase__    :						List[Any]       =			400
   lowercase__    :						Tuple       =			"""huggingface/label-files"""
   lowercase__    :						Any       =			"""kinetics400-id2label.json"""

   lowercase__    :						Optional[int]       =			json.load(open(hf_hub_download(A		,					A		,					repo_type="""dataset"""       )		,					"""r"""       )       )
   lowercase__    :						Tuple       =			{int(A       ): v for k, v in idalabel.items()}
   lowercase__    :						Tuple       =			idalabel
   lowercase__    :						List[str]       =			{v: k for k, v in idalabel.items()}
   return config
def    a__    (	):
   lowercase__    :						Any       =			hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video"""		,					filename="""eating_spaghetti_32_frames.npy"""		,					repo_type="""dataset"""       )
   lowercase__    :						List[Any]       =			np.load(A       )
   return list(A       )
def    a__    (	A       ):
   if np.ndim(A       ) == 2:
      return transform_attention_bias(A       )

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

   else:
      raise Exception(F'''Invalid number of dimesions: {np.ndim(A       )}'''       )
def    a__    (	A       ):
   return current.flatten()
def    a__    (	A       ):
   return np.reshape(A		,					(current.shape[0], current.shape[1] * current.shape[2])       ).T
def    a__    (	A       ):
   return np.reshape(A		,					(current.shape[0] * current.shape[1], current.shape[2])       ).T
def    a__    (	A		,					A       ):
   lowercase__    :						Optional[int]       =			state_dict["""optimizer"""]["""target"""]["""Transformer"""][F'''encoderblock_{i}''']

   lowercase__    :						str       =			F'''encoder.layer.{i}.'''
   lowercase__    :						str       =			{
       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__    (	A       ):
   return sum([1 if """encoderblock_""" in k else 0 for k in state_dict["""optimizer"""]["""target"""]["""Transformer"""].keys()]       )
def    a__    (	A		,					A=False       ):
   lowercase__    :						Union[str, Any]       =			get_n_layers(A       )

   lowercase__    :						Optional[Any]       =			OrderedDict()

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

   lowercase__    :						List[Any]       =			np.transpose(
       state_dict["""optimizer"""]["""target"""]["""embedding"""]["""kernel"""]		,					(4, 3, 0, 1, 2)       )
   lowercase__    :						Dict       =			state_dict["""optimizer"""]["""target"""]["""embedding"""]["""bias"""]

   lowercase__    :						str       =			state_dict["""optimizer"""]["""target"""]["""cls"""]
   lowercase__    :						List[str]       =			state_dict["""optimizer"""]["""target"""]["""Transformer"""]["""posembed_input"""][
       """pos_embedding"""
   ]

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

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

   return {k: torch.tensor(A       ) for k, v in new_state.items()}
def    a__    (	):
   lowercase__    :						List[Any]       =			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__    (	A       ):
   lowercase__    :						Dict       =			"""checkpoint"""

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

   lowercase__    :						Optional[Any]       =			restore_checkpoint(A		,					A       )
   lowercase__    :						int       =			transform_state(A		,					classification_head=A       )

   lowercase__    :						List[str]       =			get_vivit_config()

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

   lowercase__    :						Optional[int]       =			VivitForVideoClassification(A       )
   model.load_state_dict(A       )
   model.eval()

   lowercase__    :						int       =			get_processor()

   lowercase__    :						List[str]       =			prepare_video()
   lowercase__    :						Optional[int]       =			extractor(A		,					return_tensors="""pt"""       )

   lowercase__    :						Optional[int]       =			model(**A       )

   lowercase__    :						Optional[int]       =			torch.Size([1, 400]       )
   lowercase__    :						Optional[int]       =			torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]       )

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

   model.save_pretrained(A       )
   extractor.save_pretrained(A       )


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)










from typing import Any

import numpy as np
class 							__snake_case:



 def __init__(						self : str			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    if np.any(tableau[:, -1]			,		where=tableau[:, -1] < 0						):
       raise ValueError("""RHS must be > 0"""						)

    lowercase__    :						List[str]       =			tableau
    lowercase__  ,       lowercase__    :						Optional[Any]       =			tableau.shape

    # Number of decision variables x1, x2, x3...
    lowercase__    :						List[str]       =			n_vars

    # Number of artificial variables to be minimised
    lowercase__    :						str       =			len(np.where(tableau[self.n_vars : -1] == -1						)[0]						)

    # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
    lowercase__    :						Optional[Any]       =			(self.n_art_vars > 0) + 1

    # Number of slack variables added to make inequalities into equalities
    lowercase__    :						Optional[int]       =			self.n_rows - self.n_stages

    # Objectives for each stage
    lowercase__    :						Any       =			["""max"""]

    # In two stage simplex, first minimise then maximise
    if self.n_art_vars:
       self.objectives.append("""min"""						)

    lowercase__    :						Union[str, Any]       =			[""""""]

    # Index of current pivot row and column
    lowercase__    :						List[str]       =			None
    lowercase__    :						List[str]       =			None

    # Does objective row only contain (non)-negative values?
    lowercase__    :						str       =			False



 @staticmethod
 def     UpperCAmelCase     (						*UpperCAmelCase__ : int						):

    """simple docstring"""






    if len(UpperCAmelCase__						) != 3:
       raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""						)

    if not all(x >= 0 and isinstance(UpperCAmelCase__			,		UpperCAmelCase__						) for x in args						):
       raise ValueError("""All arguments must be non-negative integers"""						)

    # decision | slack | artificial
    lowercase__    :						Optional[int]       =			["""x""", """s""", """a"""]
    lowercase__    :						int       =			[]
    for i in range(3						):
       for j in range(args[i]						):
          titles.append(string_starts[i] + str(j + 1						)						)
    titles.append("""RHS"""						)
    return titles



 def     UpperCAmelCase     (						self : List[str]			,		UpperCAmelCase__ : np.ndarray						):

    """simple docstring"""






    lowercase__    :						Optional[Any]       =			self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    lowercase__    :						Union[str, Any]       =			(objective == """min""") - (objective == """max""")
    lowercase__    :						Optional[int]       =			np.argmax(sign * tableau[0, : self.n_vars]						)

    # Choice is only valid if below 0 for maximise, and above for minimise
    if sign * self.tableau[0, col_idx] <= 0:
       lowercase__    :						Optional[int]       =			True
       return 0, 0

    # Pivot row is chosen as having the lowest quotient when elements of
    # the pivot column divide the right-hand side

    # Slice excluding the objective rows
    lowercase__    :						Any       =			slice(self.n_stages			,		self.n_rows						)

    # RHS
    lowercase__    :						Union[str, Any]       =			tableau[s, -1]

    # Elements of pivot column within slice
    lowercase__    :						Optional[int]       =			tableau[s, col_idx]

    # Array filled with nans
    lowercase__    :						Optional[int]       =			np.full(self.n_rows - self.n_stages			,		np.nan						)

    # If element in pivot column is greater than zeron_stages, return
    # quotient or nan otherwise
    lowercase__    :						Optional[int]       =			np.divide(UpperCAmelCase__			,		UpperCAmelCase__			,		out=UpperCAmelCase__			,		where=divisor > 0						)

    # Arg of minimum quotient excluding the nan values. n_stages is added
    # to compensate for earlier exclusion of objective columns
    lowercase__    :						Dict       =			np.nanargmin(UpperCAmelCase__						) + self.n_stages
    return row_idx, col_idx



 def     UpperCAmelCase     (						self : Union[str, Any]			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : int			,		UpperCAmelCase__ : int						):

    """simple docstring"""






    lowercase__    :						int       =			tableau[row_idx].copy()

    lowercase__    :						Optional[Any]       =			piv_row[col_idx]

    # Entry becomes 1
    piv_row *= 1 / piv_val

    # Variable in pivot column becomes basic, ie the only non-zero entry
    for idx, coeff in enumerate(tableau[:, col_idx]						):
       tableau[idx] += -coeff * piv_row
    lowercase__    :						Any       =			piv_row
    return tableau



 def     UpperCAmelCase     (						self : Optional[Any]			,		UpperCAmelCase__ : np.ndarray						):

    """simple docstring"""






    self.objectives.pop()

    if not self.objectives:
       return tableau

    # Slice containing ids for artificial columns
    lowercase__    :						Optional[int]       =			slice(-self.n_art_vars - 1			,		-1						)

    # Delete the artificial variable columns
    lowercase__    :						Dict       =			np.delete(UpperCAmelCase__			,		UpperCAmelCase__			,		axis=1						)

    # Delete the objective row of the first stage
    lowercase__    :						Optional[Any]       =			np.delete(UpperCAmelCase__			,		0			,		axis=0						)

    lowercase__    :						str       =			1
    self.n_rows -= 1
    lowercase__    :						List[str]       =			0
    lowercase__    :						List[Any]       =			False
    return tableau



 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    for _ in range(100						):
       # Completion of each stage removes an objective. If both stages
       # are complete, then no objectives are left
       if not self.objectives:
          lowercase__    :						List[Any]       =			self.generate_col_titles(
              self.n_vars			,		self.n_slack			,		self.n_art_vars						)

          # Find the values of each variable at optimal solution
          return self.interpret_tableau(self.tableau			,		self.col_titles						)

       lowercase__  ,       lowercase__    :						Optional[Any]       =			self.find_pivot(self.tableau						)

       # If there are no more negative values in objective row
       if self.stop_iter:
          # Delete artificial variable columns and rows. Update attributes
          lowercase__    :						Any       =			self.change_stage(self.tableau						)
       else:
          lowercase__    :						str       =			self.pivot(self.tableau			,		UpperCAmelCase__			,		UpperCAmelCase__						)
    return {}







 def     UpperCAmelCase     (						self : str			,		UpperCAmelCase__ : np.ndarray			,		UpperCAmelCase__ : list[str]						):

    """simple docstring"""






    lowercase__    :						List[str]       =			{"""P""": abs(tableau[0, -1]						)}

    for i in range(self.n_vars						):
       # Gives ids of nonzero entries in the ith column
       lowercase__    :						int       =			np.nonzero(tableau[:, i]						)
       lowercase__    :						Tuple       =			len(nonzero[0]						)

       # First entry in the nonzero ids
       lowercase__    :						int       =			nonzero[0][0]
       lowercase__    :						Tuple       =			tableau[nonzero_rowidx, i]

       # If there is only one nonzero value in column, which is one
       if n_nonzero == nonzero_val == 1:
          lowercase__    :						Tuple       =			tableau[nonzero_rowidx, -1]
          lowercase__    :						Union[str, Any]       =			rhs_val

        # Check for basic variables
    for title in col_titles:
       # Don't add RHS or slack variables to output dict
       if title[0] not in "R-s-a":
          output_dict.setdefault(UpperCAmelCase__			,		0						)
    return output_dict


if __name__ == "__main__":
 import doctest

 doctest.testmod()


