




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 			__magic_name__				( A		:					int   ):






			'''simple docstring'''







			return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()   )




def 			__magic_name__				( A		:					Optional[Any],  A		:					List[Any]   ):






			'''simple docstring'''







			a    =			{}

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

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

						a    =			value.float()

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

			return upgrade




@torch.no_grad()
def 			__magic_name__				( A		:					Union[str, Any],  A		:					List[Any],  A		:					List[Any],  A		:					Any=None   ):






			'''simple docstring'''







			if config_path is not None:
						a    =			FlavaConfig.from_pretrained(A   )
			else:
						a    =			FlavaConfig()

			a    =			FlavaForPreTraining(A   ).eval()

			a    =			convert_dalle_checkpoint(A,  A,  save_checkpoint=A   )

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

			a    =			upgrade_state_dict(A,  A   )
			hf_model.load_state_dict(A   )
			a    =			hf_model.state_dict()
			a    =			count_parameters(A   )
			a    =			count_parameters(A   ) + count_parameters(A   )

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

			hf_model.save_pretrained(A   )


if __name__ == "__main__":
		__lowerCAmelCase		:				Optional[Any]       			=		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')
		__lowerCAmelCase		:				Union[str, Any]       			=		parser.parse_args()

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







import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
		import jax.numpy as jnp

if is_tf_available():
		import tensorflow as tf

if is_torch_available():
		import torch






class 		a__						(					unittest.TestCase					):

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    {
							    "task_specific_params": {
							        "summarization": {"length_penalty": 1.0, "max_length": 128, "min_length": 12, "num_beams": 4},
							        "summarization_cnn": {"length_penalty": 2.0, "max_length": 142, "min_length": 56, "num_beams": 4},
							        "summarization_xsum": {"length_penalty": 1.0, "max_length": 62, "min_length": 11, "num_beams": 6},
							    }
							}
							SCREAMING_SNAKE_CASE_  :		Any				      =    {
							    "task_specific_params.summarization.length_penalty": 1.0,
							    "task_specific_params.summarization.max_length": 128,
							    "task_specific_params.summarization.min_length": 12,
							    "task_specific_params.summarization.num_beams": 4,
							    "task_specific_params.summarization_cnn.length_penalty": 2.0,
							    "task_specific_params.summarization_cnn.max_length": 142,
							    "task_specific_params.summarization_cnn.min_length": 56,
							    "task_specific_params.summarization_cnn.num_beams": 4,
							    "task_specific_params.summarization_xsum.length_penalty": 1.0,
							    "task_specific_params.summarization_xsum.max_length": 62,
							    "task_specific_params.summarization_xsum.min_length": 11,
							    "task_specific_params.summarization_xsum.num_beams": 6,
							}

							self.assertEqual(flatten_dict(_A    ),_A    )

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    np.random.randn(3,4    )
							self.assertTrue(np.allclose(transpose(_A    ),x.transpose()    )    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.random.randn(3,4,5    )
							self.assertTrue(np.allclose(transpose(_A,axes=(1, 2, 0)    ),x.transpose((1, 2, 0)    )    )    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(transpose(_A    ),transpose(_A    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(transpose(_A,axes=(1, 2, 0)    ),transpose(_A,axes=(1, 2, 0)    ).numpy()    )    )

					@require_tf
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(transpose(_A    ),transpose(_A    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(transpose(_A,axes=(1, 2, 0)    ),transpose(_A,axes=(1, 2, 0)    ).numpy()    )    )

					@require_flax
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(transpose(_A    ),np.asarray(transpose(_A    )    )    )    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(transpose(_A,axes=(1, 2, 0)    ),np.asarray(transpose(_A,axes=(1, 2, 0)    )    )    )    )

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.random.randn(3,4    )
							self.assertTrue(np.allclose(reshape(_A,(4, 3)    ),np.reshape(_A,(4, 3)    )    )    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    np.random.randn(3,4,5    )
							self.assertTrue(np.allclose(reshape(_A,(12, 5)    ),np.reshape(_A,(12, 5)    )    )    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(reshape(_A,(4, 3)    ),reshape(_A,(4, 3)    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		int				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(reshape(_A,(12, 5)    ),reshape(_A,(12, 5)    ).numpy()    )    )

					@require_tf
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(reshape(_A,(4, 3)    ),reshape(_A,(4, 3)    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		int				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(reshape(_A,(12, 5)    ),reshape(_A,(12, 5)    ).numpy()    )    )

					@require_flax
					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		int				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(reshape(_A,(4, 3)    ),np.asarray(reshape(_A,(4, 3)    )    )    )    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    np.random.randn(3,4,5    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(reshape(_A,(12, 5)    ),np.asarray(reshape(_A,(12, 5)    )    )    )    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    np.random.randn(1,3,4    )
							self.assertTrue(np.allclose(squeeze(_A    ),np.squeeze(_A    )    )    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    np.random.randn(1,4,1,5    )
							self.assertTrue(np.allclose(squeeze(_A,axis=2    ),np.squeeze(_A,axis=2    )    )    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    np.random.randn(1,3,4    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(squeeze(_A    ),squeeze(_A    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    np.random.randn(1,4,1,5    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(squeeze(_A,axis=2    ),squeeze(_A,axis=2    ).numpy()    )    )

					@require_tf
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    np.random.randn(1,3,4    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(squeeze(_A    ),squeeze(_A    ).numpy()    )    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    np.random.randn(1,4,1,5    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(squeeze(_A,axis=2    ),squeeze(_A,axis=2    ).numpy()    )    )

					@require_flax
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    np.random.randn(1,3,4    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(squeeze(_A    ),np.asarray(squeeze(_A    )    )    )    )

							SCREAMING_SNAKE_CASE_  :		str				      =    np.random.randn(1,4,1,5    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(squeeze(_A,axis=2    ),np.asarray(squeeze(_A,axis=2    )    )    )    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.random.randn(3,4    )
							self.assertTrue(np.allclose(expand_dims(_A,axis=1    ),np.expand_dims(_A,axis=1    )    )    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.tensor(_A    )
							self.assertTrue(np.allclose(expand_dims(_A,axis=1    ),expand_dims(_A,axis=1    ).numpy()    )    )

					@require_tf
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tf.constant(_A    )
							self.assertTrue(np.allclose(expand_dims(_A,axis=1    ),expand_dims(_A,axis=1    ).numpy()    )    )






					@require_flax
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    np.random.randn(3,4    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    jnp.array(_A    )
							self.assertTrue(np.allclose(expand_dims(_A,axis=1    ),np.asarray(expand_dims(_A,axis=1    )    )    )    )
