




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

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


UpperCamelCase__         =      logging.get_logger(__name__)

UpperCamelCase__         =      {
    """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json""",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class       a__   ( snake_case__				):
						_a							:   Dict =   """blenderbot-small"""
						_a							:   List[Any] =   ["""past_key_values"""]
						_a							:   List[str] =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






						def __init__(       self		,      _A=5_0_2_6_5		,      _A=5_1_2		,      _A=8		,      _A=2_0_4_8		,      _A=1_6		,      _A=8		,      _A=2_0_4_8		,      _A=1_6		,      _A=0.0		,      _A=0.0		,      _A=True		,      _A=True		,      _A="gelu"		,      _A=5_1_2		,      _A=0.1		,      _A=0.0		,      _A=0.0		,      _A=0.02		,      _A=1		,      _A=False		,      _A=0		,      _A=1		,      _A=2		,      _A=2		,      **_A		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	vocab_size
											__lowerCAmelCase           =	max_position_embeddings
											__lowerCAmelCase           =	d_model
											__lowerCAmelCase           =	encoder_ffn_dim
											__lowerCAmelCase           =	encoder_layers
											__lowerCAmelCase           =	encoder_attention_heads
											__lowerCAmelCase           =	decoder_ffn_dim
											__lowerCAmelCase           =	decoder_layers
											__lowerCAmelCase           =	decoder_attention_heads
											__lowerCAmelCase           =	dropout
											__lowerCAmelCase           =	attention_dropout
											__lowerCAmelCase           =	activation_dropout
											__lowerCAmelCase           =	activation_function
											__lowerCAmelCase           =	init_std
											__lowerCAmelCase           =	encoder_layerdrop
											__lowerCAmelCase           =	decoder_layerdrop
											__lowerCAmelCase           =	use_cache
											__lowerCAmelCase           =	encoder_layers
											__lowerCAmelCase           =	scale_embedding  # scale factor will be sqrt(d_model) if True

											super().__init__(
											    pad_token_id=_A		,      bos_token_id=_A		,      eos_token_id=_A		,      is_encoder_decoder=_A		,      decoder_start_token_id=_A		,      forced_eos_token_id=_A		,      **_A		,      )






class       a__   ( snake_case__				):






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											if self.task in ["default", "seq2seq-lm"]:
																__lowerCAmelCase           =	OrderedDict(
																    [
																        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																    ]      )

																if self.use_past:
																					__lowerCAmelCase           =	{0: "batch"}
																					__lowerCAmelCase           =	{0: "batch", 1: "past_decoder_sequence + sequence"}
																else:
																					__lowerCAmelCase           =	{0: "batch", 1: "decoder_sequence"}
																					__lowerCAmelCase           =	{0: "batch", 1: "decoder_sequence"}

																if self.use_past:
																					self.fill_with_past_key_values_(_A		,      direction="inputs"      )
											elif self.task == "causal-lm":
																# TODO: figure this case out.
																__lowerCAmelCase           =	OrderedDict(
																    [
																        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																    ]      )
																if self.use_past:
																					__lowerCAmelCase       ,							__lowerCAmelCase           =	self.num_layers
																					for i in range(_A      ):
																										__lowerCAmelCase           =	{0: "batch", 2: "past_sequence + sequence"}
																										__lowerCAmelCase           =	{0: "batch", 2: "past_sequence + sequence"}
											else:
																__lowerCAmelCase           =	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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											if self.task in ["default", "seq2seq-lm"]:
																__lowerCAmelCase           =	super().outputs
											else:
																__lowerCAmelCase           =	super(_A		,      self      ).outputs
																if self.use_past:
																					__lowerCAmelCase       ,							__lowerCAmelCase           =	self.num_layers
																					for i in range(_A      ):
																										__lowerCAmelCase           =	{0: "batch", 2: "past_sequence + sequence"}
																										__lowerCAmelCase           =	{0: "batch", 2: "past_sequence + sequence"}
											return common_outputs






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = -1		,      _A = -1		,      _A = False		,      _A = None		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    _A		,      _A		,      _A		,      _A		,      _A      )

											# Generate decoder inputs
											__lowerCAmelCase           =	seq_length if not self.use_past else 1
											__lowerCAmelCase           =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    _A		,      _A		,      _A		,      _A		,      _A      )
											__lowerCAmelCase           =	{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
											__lowerCAmelCase           =	dict(**_A		,      **_A      )

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

																__lowerCAmelCase           =	torch.cat(
																    [common_inputs["decoder_attention_mask"], torch.ones(_A		,      _A      )]		,      dim=1      )

																__lowerCAmelCase           =	[]
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																__lowerCAmelCase       ,							__lowerCAmelCase           =	self.num_layers
																__lowerCAmelCase           =	min(_A		,      _A      )
																__lowerCAmelCase           =	max(_A		,      _A      ) - min_num_layers
																__lowerCAmelCase           =	"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = -1		,      _A = -1		,      _A = False		,      _A = None		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    _A		,      _A		,      _A		,      _A		,      _A      )

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

																__lowerCAmelCase           =	common_inputs["attention_mask"].dtype
																__lowerCAmelCase           =	torch.cat(
																    [common_inputs["attention_mask"], torch.ones(_A		,      _A		,      dtype=_A      )]		,      dim=1      )
																__lowerCAmelCase           =	[
																    (torch.zeros(_A      ), torch.zeros(_A      )) for _ in range(_A      )
																]
											return common_inputs






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = -1		,      _A = -1		,      _A = False		,      _A = None		,      ):




											"""simple docstring"""



											__lowerCAmelCase           =	compute_effective_axis_dimension(
											    _A		,      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
											__lowerCAmelCase           =	tokenizer.num_special_tokens_to_add(_A      )
											__lowerCAmelCase           =	compute_effective_axis_dimension(
											    _A		,      fixed_dimension=OnnxConfig.default_fixed_sequence		,      num_token_to_add=_A      )

											# Generate dummy inputs according to compute batch and sequence
											__lowerCAmelCase           =	[" ".join([tokenizer.unk_token]      ) * seq_length] * batch_size
											__lowerCAmelCase           =	dict(tokenizer(_A		,      return_tensors=_A      )      )
											return common_inputs






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A = -1		,      _A = -1		,      _A = False		,      _A = None		,      ):




											"""simple docstring"""



											if self.task in ["default", "seq2seq-lm"]:
																__lowerCAmelCase           =	self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    _A		,      batch_size=_A		,      seq_length=_A		,      is_pair=_A		,      framework=_A      )

											elif self.task == "causal-lm":
																__lowerCAmelCase           =	self._generate_dummy_inputs_for_causal_lm(
																    _A		,      batch_size=_A		,      seq_length=_A		,      is_pair=_A		,      framework=_A      )
											else:
																__lowerCAmelCase           =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																    _A		,      batch_size=_A		,      seq_length=_A		,      is_pair=_A		,      framework=_A      )

											return common_inputs






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											if self.task in ["default", "seq2seq-lm"]:
																__lowerCAmelCase           =	super()._flatten_past_key_values_(_A		,      _A		,      _A		,      _A      )
											else:
																__lowerCAmelCase           =	super(_A		,      self      )._flatten_past_key_values_(
																    _A		,      _A		,      _A		,      _A      )










import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any					):
					# encoder.embeddings are double copied in original FLAVA
					return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =	{}

					for key, value in state_dict.items():
										if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
															continue

										__lowerCAmelCase           =	key.replace("heads.cmd.mim_head.cls.predictions"				,				"mmm_image_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.mlm_head.cls.predictions"				,				"mmm_text_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.cls"				,				"itm_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.pooler"				,				"itm_head.pooler"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.clip_head.logit_scale"				,				"flava.logit_scale"					)
										__lowerCAmelCase           =	key.replace("heads.fairseq_mlm.cls.predictions"				,				"mlm_head"					)
										__lowerCAmelCase           =	key.replace("heads.imagenet.mim_head.cls.predictions"				,				"mim_head"					)
										__lowerCAmelCase           =	key.replace("mm_text_projection"				,				"flava.text_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("mm_image_projection"				,				"flava.image_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("image_encoder.module"				,				"flava.image_model"					)
										__lowerCAmelCase           =	key.replace("text_encoder.module"				,				"flava.text_model"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module.encoder.cls_token"				,				"flava.multimodal_model.cls_token"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module"				,				"flava.multimodal_model"					)
										__lowerCAmelCase           =	key.replace("text_projection"				,				"flava.text_projection"					)
										__lowerCAmelCase           =	key.replace("image_projection"				,				"flava.image_projection"					)

										__lowerCAmelCase           =	value.float()

					for key, value in codebook_state_dict.items():
										__lowerCAmelCase           =	value

					return upgrade







@torch.no_grad()
def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    int=None					):
					if config_path is not None:
										__lowerCAmelCase           =	FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	FlavaConfig()

					__lowerCAmelCase           =	FlavaForPreTraining(SCREAMING_SNAKE_CASE_					).eval()

					__lowerCAmelCase           =	convert_dalle_checkpoint(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				save_checkpoint=SCREAMING_SNAKE_CASE_					)

					if os.path.exists(SCREAMING_SNAKE_CASE_					):
										__lowerCAmelCase           =	torch.load(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)
					else:
										__lowerCAmelCase           =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)

					__lowerCAmelCase           =	upgrade_state_dict(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					hf_model.load_state_dict(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	hf_model.state_dict()
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					) + count_parameters(SCREAMING_SNAKE_CASE_					)

					assert torch.allclose(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				atol=1E-3					)

					hf_model.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					UpperCamelCase__         =      argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
					parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					UpperCamelCase__         =      parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)




