'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


A_        =   logging.get_logger(__name__)

A_        =   {
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}




class 		SCREAMING_SNAKE_CASE_				(a_			):



			"""simple docstring"""






			__magic_name__   :							Union[str, Any]             =					'mbart'
			__magic_name__   :							Any             =					['past_key_values']
			__magic_name__   :							List[str]             =					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



			def __init__(       self							, snake_case_=50_265							, snake_case_=1_024							, snake_case_=12							, snake_case_=4_096							, snake_case_=16							, snake_case_=12							, snake_case_=4_096							, snake_case_=16							, snake_case_=0.0							, snake_case_=0.0							, snake_case_=True							, snake_case_=True							, snake_case_="gelu"							, snake_case_=1_024							, snake_case_=0.1							, snake_case_=0.0							, snake_case_=0.0							, snake_case_=0.02							, snake_case_=0.0							, snake_case_=False							, snake_case_=1							, snake_case_=0							, snake_case_=2							, snake_case_=2							, **snake_case_							, )     ->   Tuple:
									snake_case   :					Tuple							=						vocab_size
									snake_case   :					Union[str, Any]							=						max_position_embeddings
									snake_case   :					Optional[Any]							=						d_model
									snake_case   :					Dict							=						encoder_ffn_dim
									snake_case   :					List[str]							=						encoder_layers
									snake_case   :					List[str]							=						encoder_attention_heads
									snake_case   :					Optional[int]							=						decoder_ffn_dim
									snake_case   :					Any							=						decoder_layers
									snake_case   :					int							=						decoder_attention_heads
									snake_case   :					Optional[Any]							=						dropout
									snake_case   :					Union[str, Any]							=						attention_dropout
									snake_case   :					Tuple							=						activation_dropout
									snake_case   :					str							=						activation_function
									snake_case   :					Tuple							=						init_std
									snake_case   :					Optional[Any]							=						encoder_layerdrop
									snake_case   :					str							=						decoder_layerdrop
									snake_case   :					Union[str, Any]							=						classifier_dropout
									snake_case   :					List[str]							=						use_cache
									snake_case   :					str							=						encoder_layers
									snake_case   :					Tuple							=						scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=_snake_case							, bos_token_id=_snake_case							, eos_token_id=_snake_case							, is_encoder_decoder=_snake_case							, forced_eos_token_id=_snake_case							, **_snake_case							, )







class 		SCREAMING_SNAKE_CASE_				(a_			):



			"""simple docstring"""






			@property
			def      lowerCamelCase   (       self				)     ->   Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
															snake_case   :					Any							=						OrderedDict(
															    [
															        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
															        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
															    ]				)

															if self.use_past:
																					snake_case   :					Union[str, Any]							=						{0: '''batch'''}
																					snake_case   :					Any							=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
															else:
																					snake_case   :					int							=						{0: '''batch''', 1: '''decoder_sequence'''}
																					snake_case   :					List[Any]							=						{0: '''batch''', 1: '''decoder_sequence'''}

															if self.use_past:
																					self.fill_with_past_key_values_(_snake_case							, direction="inputs"				)
									elif self.task == "causal-lm":
															# TODO: figure this case out.
															snake_case   :					List[str]							=						OrderedDict(
															    [
															        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
															        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
															    ]				)
															if self.use_past:
																					snake_case   :					Tuple							=						self.num_layers
																					for i in range(_snake_case				):
																											snake_case   :					Any							=						{0: '''batch''', 2: '''past_sequence + sequence'''}
																											snake_case   :					Tuple							=						{0: '''batch''', 2: '''past_sequence + sequence'''}
									else:
															snake_case   :					Dict							=						OrderedDict(
															    [
															        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
															        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
															        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
															        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
															    ]				)

									return common_inputs



			@property
			def      lowerCamelCase   (       self				)     ->   Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
															snake_case   :					int							=						super().outputs
									else:
															snake_case   :					Optional[Any]							=						super(_snake_case							, self				).outputs
															if self.use_past:
																					snake_case   :					List[Any]							=						self.num_layers
																					for i in range(_snake_case				):
																											snake_case   :					Any							=						{0: '''batch''', 2: '''past_sequence + sequence'''}
																											snake_case   :					Dict							=						{0: '''batch''', 2: '''past_sequence + sequence'''}
									return common_outputs



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_ = -1							, snake_case_ = -1							, snake_case_ = False							, snake_case_ = None							, )     ->   Mapping[str, Any]:
									snake_case   :					int							=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case				)

									# Generate decoder inputs
									snake_case   :					Dict							=						seq_length if not self.use_past else 1
									snake_case   :					List[str]							=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case				)
									snake_case   :					Optional[Any]							=						{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									snake_case   :					int							=						dict(**_snake_case							, **_snake_case				)

									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
															else:
																					import torch
															snake_case   :					List[str]							=						common_inputs['''input_ids'''].shape
															snake_case   :					Optional[Any]							=						common_inputs['''decoder_input_ids'''].shape[1]
															snake_case   :					Tuple							=						self.num_attention_heads
															snake_case   :					Union[str, Any]							=						(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															snake_case   :					Union[str, Any]							=						decoder_seq_length + 3
															snake_case   :					Optional[int]							=						(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															snake_case   :					int							=						torch.cat(
															    [common_inputs["decoder_attention_mask"], torch.ones(_snake_case							, _snake_case				)]							, dim=1				)

															snake_case   :					Optional[int]							=						[]
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															snake_case   :					Tuple							=						self.num_layers
															snake_case   :					str							=						min(_snake_case							, _snake_case				)
															snake_case   :					Tuple							=						max(_snake_case							, _snake_case				) - min_num_layers
															snake_case   :					int							=						'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

															for _ in range(_snake_case				):
																					common_inputs["past_key_values"].append(
																					    (
																					        torch.zeros(_snake_case				),
																					        torch.zeros(_snake_case				),
																					        torch.zeros(_snake_case				),
																					        torch.zeros(_snake_case				),
																					    )				)
															# TODO: test this.
															snake_case   :					Any							=						encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
															for _ in range(_snake_case							, _snake_case				):
																					common_inputs["past_key_values"].append((torch.zeros(_snake_case				), torch.zeros(_snake_case				))				)
									return common_inputs



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_ = -1							, snake_case_ = -1							, snake_case_ = False							, snake_case_ = None							, )     ->   Mapping[str, Any]:
									snake_case   :					List[str]							=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _snake_case							, _snake_case							, _snake_case							, _snake_case							, _snake_case				)

									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."				)
															else:
																					import torch
															snake_case   :					List[str]							=						common_inputs['''input_ids'''].shape
															# Not using the same length for past_key_values
															snake_case   :					Optional[int]							=						seqlen + 2
															snake_case   :					List[Any]							=						self.num_layers
															snake_case   :					Tuple							=						self.num_attention_heads
															snake_case   :					Optional[int]							=						(
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)

															snake_case   :					Union[str, Any]							=						common_inputs['''attention_mask'''].dtype
															snake_case   :					Optional[int]							=						torch.cat(
															    [common_inputs["attention_mask"], torch.ones(_snake_case							, _snake_case							, dtype=_snake_case				)]							, dim=1				)
															snake_case   :					Tuple							=						[
															    (torch.zeros(_snake_case				), torch.zeros(_snake_case				)) for _ in range(_snake_case				)
															]
									return common_inputs



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_ = -1							, snake_case_ = -1							, snake_case_ = False							, snake_case_ = None							, )     ->   Mapping[str, Any]:
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									snake_case   :					Any							=						compute_effective_axis_dimension(
									    _snake_case							, fixed_dimension=OnnxConfig.default_fixed_batch							, num_token_to_add=0				)

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									snake_case   :					List[str]							=						tokenizer.num_special_tokens_to_add(_snake_case				)
									snake_case   :					List[Any]							=						compute_effective_axis_dimension(
									    _snake_case							, fixed_dimension=OnnxConfig.default_fixed_sequence							, num_token_to_add=_snake_case				)

									# Generate dummy inputs according to compute batch and sequence
									snake_case   :					Any							=						[''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
									snake_case   :					List[str]							=						dict(tokenizer(_snake_case							, return_tensors=_snake_case				)				)
									return common_inputs



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_ = -1							, snake_case_ = -1							, snake_case_ = False							, snake_case_ = None							, )     ->   Mapping[str, Any]:
									if self.task in ["default", "seq2seq-lm"]:
															snake_case   :					Dict							=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
															    _snake_case							, batch_size=_snake_case							, seq_length=_snake_case							, is_pair=_snake_case							, framework=_snake_case				)

									elif self.task == "causal-lm":
															snake_case   :					Dict							=						self._generate_dummy_inputs_for_causal_lm(
															    _snake_case							, batch_size=_snake_case							, seq_length=_snake_case							, is_pair=_snake_case							, framework=_snake_case				)
									else:
															snake_case   :					Union[str, Any]							=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
															    _snake_case							, batch_size=_snake_case							, seq_length=_snake_case							, is_pair=_snake_case							, framework=_snake_case				)

									return common_inputs






			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_				)     ->   Optional[int]:
									if self.task in ["default", "seq2seq-lm"]:
															snake_case   :					Dict							=						super()._flatten_past_key_values_(_snake_case							, _snake_case							, _snake_case							, _snake_case				)
									else:
															snake_case   :					Optional[int]							=						super(_snake_case							, self				)._flatten_past_key_values_(
															    _snake_case							, _snake_case							, _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)





