



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  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= "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=20_48):
																				f.write(_lowerCAmelCase)



def  _lowerCAmelCase     ():
					UpperCamelCase_			= VivitConfig()

					UpperCamelCase_			= 4_00
					UpperCamelCase_			= "huggingface/label-files"
					UpperCamelCase_			= "kinetics400-id2label.json"

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



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



def  _lowerCAmelCase     (_lowerCAmelCase):
					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  _lowerCAmelCase     (_lowerCAmelCase):
					return current.flatten()



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



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



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= state_dict["optimizer"]["target"]["Transformer"][f"""encoderblock_{i}"""]

					UpperCamelCase_			= f"""encoder.layer.{i}."""
					UpperCamelCase_			= {
					    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  _lowerCAmelCase     (_lowerCAmelCase):
					return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()])



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase=False):
					UpperCamelCase_			= get_n_layers(_lowerCAmelCase)

					UpperCamelCase_			= OrderedDict()

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

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

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

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



def  _lowerCAmelCase     ():
					UpperCamelCase_			= VivitImageProcessor()

					assert extractor.do_resize is True
					assert extractor.size == {"shortest_edge": 2_56}
					assert extractor.do_center_crop is True
					assert extractor.crop_size == {"width": 2_24, "height": 2_24}
					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 / 2_55

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

					return extractor



def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= "checkpoint"

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

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

					UpperCamelCase_			= get_vivit_config()

					assert config.image_size == 2_24
					assert config.num_frames == 32

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

					UpperCamelCase_			= get_processor()

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

					UpperCamelCase_			= model(**_lowerCAmelCase)

					UpperCamelCase_			= torch.Size([1, 4_00])
					UpperCamelCase_			= 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__":
	UpperCAmelCase :				Optional[int]							=argparse.ArgumentParser()

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

	UpperCAmelCase :				Dict							=parser.parse_args()
	convert(args.output_model_name)





from __future__ import annotations

import random
from collections.abc import Iterable


class    _lowercase :
				'''simple docstring'''







				def __init__(			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= {literal: None for literal in literals}

				def __str__(			self     ):

									'''simple docstring'''


									return "{" + " , ".join(self.literals     ) + "}"

				def __len__(			self     ):

									'''simple docstring'''


									return len(self.literals     )

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									for literal in self.literals:
														UpperCamelCase_			= literal[:2]
														if symbol in model:
																			UpperCamelCase_			= model[symbol]
														else:
																			continue
														if value is not None:
																			# Complement assignment if literal is in complemented form
																			if literal.endswith("'"     ):
																								UpperCamelCase_			= not value
														UpperCamelCase_			= value

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									for literal in self.literals:
														UpperCamelCase_			= literal.rstrip("'"     ) if literal.endswith("'"     ) else literal + "'"
														if symbol in self.literals:
																			return True

									self.assign(snake_case__     )
									for value in self.literals.values():
														if value in (True, None):
																			return value
									return any(self.literals.values()     )




class    _lowercase :
				'''simple docstring'''







				def __init__(			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= list(snake_case__     )

				def __str__(			self     ):

									'''simple docstring'''


									return "{" + " , ".join(str(snake_case__     ) for clause in self.clauses     ) + "}"




def  _lowerCAmelCase     ():
					UpperCamelCase_			= []
					UpperCamelCase_			= random.randint(1 ,						5)
					UpperCamelCase_			= "A"
					UpperCamelCase_			= 0
					while i < no_of_literals:
										UpperCamelCase_			= random.randint(1 ,						5)
										UpperCamelCase_			= base_var + str(_lowerCAmelCase)
										UpperCamelCase_			= random.randint(0 ,						1)
										if var_complement == 1:
															var_name += "'"
										if var_name in literals:
															i -= 1
										else:
															literals.append(_lowerCAmelCase)
										i += 1
					return Clause(_lowerCAmelCase)



def  _lowerCAmelCase     ():
					UpperCamelCase_			= set()
					UpperCamelCase_			= random.randint(1 ,						10)
					while len(_lowerCAmelCase) < no_of_clauses:
										clauses.add(generate_clause())
					return Formula(_lowerCAmelCase)



def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= formula.clauses
					UpperCamelCase_			= []
					for clause in formula.clauses:
										for literal in clause.literals:
															UpperCamelCase_			= literal[:2]
															if symbol not in symbols_set:
																				symbols_set.append(_lowerCAmelCase)
					return clauses, symbols_set



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= []
					UpperCamelCase_			= {}
					UpperCamelCase_			= []

					for clause in clauses:
										if clause.evaluate(_lowerCAmelCase):
															continue
										for literal in clause.literals:
															literals.append(_lowerCAmelCase)

					for s in symbols:
										UpperCamelCase_			= s + "'"
										if (s in literals and sym not in literals) or (
										    s not in literals and sym in literals
										):
															pure_symbols.append(_lowerCAmelCase)
					for p in pure_symbols:
										UpperCamelCase_			= None
					for s in pure_symbols:
										UpperCamelCase_			= s + "'"
										if s in literals:
															UpperCamelCase_			= True
										elif sym in literals:
															UpperCamelCase_			= False
					return pure_symbols, assignment



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= []
					for clause in clauses:
										if len(_lowerCAmelCase) == 1:
															unit_symbols.append(list(clause.literals.keys())[0])
										else:
															UpperCamelCase_	,     UpperCamelCase_			= 0, 0
															for literal, value in clause.literals.items():
																				if value is False:
																									f_count += 1
																				elif value is None:
																									UpperCamelCase_			= literal
																									n_count += 1
															if f_count == len(_lowerCAmelCase) - 1 and n_count == 1:
																				unit_symbols.append(_lowerCAmelCase)
					UpperCamelCase_			= {}
					for i in unit_symbols:
										UpperCamelCase_			= i[:2]
										UpperCamelCase_			= len(_lowerCAmelCase) == 2
					UpperCamelCase_			= [i[:2] for i in unit_symbols]

					return unit_symbols, assignment



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= True
					for clause in clauses:
										UpperCamelCase_			= clause.evaluate(_lowerCAmelCase)
										if clause_check is False:
															return False, None
										elif clause_check is None:
															UpperCamelCase_			= False
															continue

					if check_clause_all_true:
										return True, model

					try:
										UpperCamelCase_	,     UpperCamelCase_			= find_pure_symbols(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)
					except RecursionError:
										print("raises a RecursionError and is")
										return None, {}
					UpperCamelCase_			= None
					if len(_lowerCAmelCase) > 0:
										UpperCamelCase_	,     UpperCamelCase_			= pure_symbols[0], assignment[pure_symbols[0]]

					if p:
										UpperCamelCase_			= model
										UpperCamelCase_			= value
										UpperCamelCase_			= list(_lowerCAmelCase)
										if p in tmp_symbols:
															tmp_symbols.remove(_lowerCAmelCase)
										return dpll_algorithm(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)

					UpperCamelCase_	,     UpperCamelCase_			= find_unit_clauses(_lowerCAmelCase ,						_lowerCAmelCase)
					UpperCamelCase_			= None
					if len(_lowerCAmelCase) > 0:
										UpperCamelCase_	,     UpperCamelCase_			= unit_symbols[0], assignment[unit_symbols[0]]
					if p:
										UpperCamelCase_			= model
										UpperCamelCase_			= value
										UpperCamelCase_			= list(_lowerCAmelCase)
										if p in tmp_symbols:
															tmp_symbols.remove(_lowerCAmelCase)
										return dpll_algorithm(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)
					UpperCamelCase_			= symbols[0]
					UpperCamelCase_			= symbols[1:]
					UpperCamelCase_	,     UpperCamelCase_			= model, model
					UpperCamelCase_	,     UpperCamelCase_			= True, False

					return dpll_algorithm(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase) or dpll_algorithm(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	UpperCAmelCase :				List[Any]							=generate_formula()
	print(F"The formula {formula} is", end=""" """)

	UpperCAmelCase			,			UpperCAmelCase :				Any							=generate_parameters(formula)
	UpperCAmelCase			,			UpperCAmelCase :				str							=dpll_algorithm(clauses, symbols, {})

	if solution:
		print(F"satisfiable with the assignment {model}.")
	else:
		print("""not satisfiable.""")
