






import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
	import jax
	import jax.numpy as jnp
	from flax.jax_utils import replicate
	from flax.training.common_utils import shard

	from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline



@require_flax
class 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					def 	_lowercase					( self					:  Optional[int]	):
						with tempfile.TemporaryDirectory() as tmpdirname:
							# pipeline has Flax weights
							snake_case__			:				Optional[int]       =  FlaxDiffusionPipeline.from_pretrained(
							    "hf-internal-testing/tiny-stable-diffusion-pipe"							,		safety_checker=__A							,		cache_dir=__A	)

							snake_case__			:				Union[str, Any]       =  [t[-1] for t in os.walk(os.path.join(__A							,		os.listdir(__A	)[0]							,		"snapshots"	)	)]
							snake_case__			:				Optional[Any]       =  [item for sublist in all_root_files for item in sublist]

							# None of the downloaded files should be a PyTorch file even if we have some here:
							# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
							assert not any(f.endswith(".bin"	) for f in files	)



@slow
@require_flax
class 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					def 	_lowercase					( self					:  str	):
						snake_case__,						snake_case__			:				List[Any]       =  FlaxStableDiffusionPipeline.from_pretrained(
						    "hf-internal-testing/tiny-stable-diffusion-pipe"							,		safety_checker=__A	)

						snake_case__			:				Dict       =  (
						    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
						    " field, close up, split lighting, cinematic"
						)

						snake_case__			:				Dict       =  jax.random.PRNGKey(0	)
						snake_case__			:				int       =  4

						snake_case__			:				Union[str, Any]       =  jax.device_count()
						snake_case__			:				Any       =  num_samples * [prompt]
						snake_case__			:				Optional[int]       =  pipeline.prepare_inputs(__A	)

						# shard inputs and rng
						snake_case__			:				str       =  replicate(__A	)
						snake_case__			:				Optional[int]       =  jax.random.split(__A							,		__A	)
						snake_case__			:				int       =  shard(__A	)

						snake_case__			:				Optional[int]       =  pipeline(__A							,		__A							,		__A							,		__A							,		jit=__A	).images

						assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
						if jax.device_count() == 8:
							assert np.abs(np.abs(images[0, 0, :2, :2, -2:]							,		dtype=np.floataa	).sum() - 4.1_5_1_4_7_4_5	) < 1e-3
							assert np.abs(np.abs(__A							,		dtype=np.floataa	).sum() - 4_9_9_4_7.8_7_5	) < 5e-1

						snake_case__			:				int       =  pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]	)	)	)
						assert len(__A	) == num_samples



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__,						snake_case__			:				Union[str, Any]       =  FlaxStableDiffusionPipeline.from_pretrained(
						    "CompVis/stable-diffusion-v1-4"							,		revision="flax"							,		safety_checker=__A	)

						snake_case__			:				int       =  (
						    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
						    " field, close up, split lighting, cinematic"
						)

						snake_case__			:				Optional[int]       =  jax.random.PRNGKey(0	)
						snake_case__			:				int       =  5_0

						snake_case__			:				List[str]       =  jax.device_count()
						snake_case__			:				Dict       =  num_samples * [prompt]
						snake_case__			:				Union[str, Any]       =  pipeline.prepare_inputs(__A	)

						# shard inputs and rng
						snake_case__			:				List[Any]       =  replicate(__A	)
						snake_case__			:				List[str]       =  jax.random.split(__A							,		__A	)
						snake_case__			:				Dict       =  shard(__A	)

						snake_case__			:				List[Any]       =  pipeline(__A							,		__A							,		__A							,		__A							,		jit=__A	).images

						assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]							,		dtype=np.floataa	).sum() - 0.0_5_6_5_2_4_0_1)	) < 1e-3
							assert np.abs((np.abs(__A							,		dtype=np.floataa	).sum() - 2_3_8_3_8_0_8.2)	) < 5e-1



					def 	_lowercase					( self					:  Dict	):
						snake_case__,						snake_case__			:				str       =  FlaxStableDiffusionPipeline.from_pretrained(
						    "CompVis/stable-diffusion-v1-4"							,		revision="bf16"							,		dtype=jnp.bfloataa							,		safety_checker=__A	)

						snake_case__			:				List[Any]       =  (
						    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
						    " field, close up, split lighting, cinematic"
						)

						snake_case__			:				List[Any]       =  jax.random.PRNGKey(0	)
						snake_case__			:				List[str]       =  5_0

						snake_case__			:				List[Any]       =  jax.device_count()
						snake_case__			:				Union[str, Any]       =  num_samples * [prompt]
						snake_case__			:				Optional[int]       =  pipeline.prepare_inputs(__A	)

						# shard inputs and rng
						snake_case__			:				Any       =  replicate(__A	)
						snake_case__			:				Optional[int]       =  jax.random.split(__A							,		__A	)
						snake_case__			:				List[Any]       =  shard(__A	)

						snake_case__			:				int       =  pipeline(__A							,		__A							,		__A							,		__A							,		jit=__A	).images

						assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]							,		dtype=np.floataa	).sum() - 0.0_4_0_0_3_9_0_6)	) < 1e-3
							assert np.abs((np.abs(__A							,		dtype=np.floataa	).sum() - 2_3_7_3_5_1_6.7_5)	) < 5e-1



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__,						snake_case__			:				Optional[int]       =  FlaxStableDiffusionPipeline.from_pretrained(
						    "CompVis/stable-diffusion-v1-4"							,		revision="bf16"							,		dtype=jnp.bfloataa	)

						snake_case__			:				List[str]       =  (
						    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
						    " field, close up, split lighting, cinematic"
						)

						snake_case__			:				Tuple       =  jax.random.PRNGKey(0	)
						snake_case__			:				Tuple       =  5_0

						snake_case__			:				Optional[Any]       =  jax.device_count()
						snake_case__			:				Any       =  num_samples * [prompt]
						snake_case__			:				int       =  pipeline.prepare_inputs(__A	)

						# shard inputs and rng
						snake_case__			:				Dict       =  replicate(__A	)
						snake_case__			:				Optional[Any]       =  jax.random.split(__A							,		__A	)
						snake_case__			:				Dict       =  shard(__A	)

						snake_case__			:				str       =  pipeline(__A							,		__A							,		__A							,		__A							,		jit=__A	).images

						assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]							,		dtype=np.floataa	).sum() - 0.0_4_0_0_3_9_0_6)	) < 1e-3
							assert np.abs((np.abs(__A							,		dtype=np.floataa	).sum() - 2_3_7_3_5_1_6.7_5)	) < 5e-1



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				Union[str, Any]       =  FlaxDDIMScheduler(
						    beta_start=0.0_0_0_8_5							,		beta_end=0.0_1_2							,		beta_schedule="scaled_linear"							,		set_alpha_to_one=__A							,		steps_offset=1							,		)

						snake_case__,						snake_case__			:				List[str]       =  FlaxStableDiffusionPipeline.from_pretrained(
						    "CompVis/stable-diffusion-v1-4"							,		revision="bf16"							,		dtype=jnp.bfloataa							,		scheduler=__A							,		safety_checker=__A							,		)
						snake_case__			:				str       =  scheduler.create_state()

						snake_case__			:				Any       =  scheduler_state

						snake_case__			:				List[Any]       =  (
						    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
						    " field, close up, split lighting, cinematic"
						)

						snake_case__			:				Union[str, Any]       =  jax.random.PRNGKey(0	)
						snake_case__			:				Tuple       =  5_0

						snake_case__			:				Optional[Any]       =  jax.device_count()
						snake_case__			:				Union[str, Any]       =  num_samples * [prompt]
						snake_case__			:				List[str]       =  pipeline.prepare_inputs(__A	)

						# shard inputs and rng
						snake_case__			:				List[Any]       =  replicate(__A	)
						snake_case__			:				Optional[int]       =  jax.random.split(__A							,		__A	)
						snake_case__			:				str       =  shard(__A	)

						snake_case__			:				Any       =  pipeline(__A							,		__A							,		__A							,		__A							,		jit=__A	).images

						assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
						if jax.device_count() == 8:
							assert np.abs((np.abs(images[0, 0, :2, :2, -2:]							,		dtype=np.floataa	).sum() - 0.0_4_5_0_4_3_9_4_5)	) < 1e-3
							assert np.abs((np.abs(__A							,		dtype=np.floataa	).sum() - 2_3_4_7_6_9_3.5)	) < 5e-1






					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				Tuple       =  (
						    "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"
						    " field, close up, split lighting, cinematic"
						)

						snake_case__			:				str       =  jax.device_count()
						snake_case__			:				Optional[int]       =  num_samples * [prompt]
						snake_case__			:				Optional[Any]       =  jax.random.split(jax.random.PRNGKey(0	)							,		__A	)

						snake_case__,						snake_case__			:				List[str]       =  FlaxStableDiffusionPipeline.from_pretrained(
						    "CompVis/stable-diffusion-v1-4"							,		revision="bf16"							,		dtype=jnp.bfloataa							,		safety_checker=__A							,		)

						snake_case__			:				Optional[int]       =  replicate(__A	)
						snake_case__			:				Tuple       =  pipeline.prepare_inputs(__A	)
						snake_case__			:				Dict       =  shard(__A	)
						snake_case__			:				str       =  pipeline(__A							,		__A							,		__A							,		jit=__A	).images
						assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
						snake_case__			:				Union[str, Any]       =  images[2, 0, 2_5_6, 1_0:1_7, 1]

						# With memory efficient attention
						snake_case__,						snake_case__			:				int       =  FlaxStableDiffusionPipeline.from_pretrained(
						    "CompVis/stable-diffusion-v1-4"							,		revision="bf16"							,		dtype=jnp.bfloataa							,		safety_checker=__A							,		use_memory_efficient_attention=__A							,		)

						snake_case__			:				Any       =  replicate(__A	)
						snake_case__			:				Dict       =  pipeline.prepare_inputs(__A	)
						snake_case__			:				Tuple       =  shard(__A	)
						snake_case__			:				Union[str, Any]       =  pipeline(__A							,		__A							,		__A							,		jit=__A	).images
						assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
						snake_case__			:				List[Any]       =  images[2, 0, 2_5_6, 1_0:1_7, 1]

						# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
						# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
						assert abs(slice_eff - slice	).max() < 1e-2











import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str       ):
	snake_case__			:				Optional[Any]       =  [
	    "encoder.version",
	    "decoder.version",
	    "model.encoder.version",
	    "model.decoder.version",
	    "_float_tensor",
	    "decoder.output_projection.weight",
	]
	for k in ignore_keys:
		state_dict.pop(snake_case_					,  snake_case_       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int       ):
	snake_case__,						snake_case__			:				Optional[Any]       =  emb.weight.shape
	snake_case__			:				Tuple       =  nn.Linear(snake_case_					,  snake_case_					,  bias=snake_case_       )
	snake_case__			:				Optional[int]       =  emb.weight.data
	return lin_layer







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							List[Any]					,  snake_case_ :							Optional[Any]="facebook/mbart-large-en-ro"					,  snake_case_ :							Optional[int]=False					,  snake_case_ :							List[Any]=False       ):
	snake_case__			:				Tuple       =  torch.load(snake_case_					,  map_location="cpu"       )["model"]
	remove_ignore_keys_(snake_case_       )
	snake_case__			:				Any       =  state_dict["encoder.embed_tokens.weight"].shape[0]

	snake_case__			:				List[Any]       =  MBartConfig.from_pretrained(snake_case_					,  vocab_size=snake_case_       )
	if mbart_aa and finetuned:
		snake_case__			:				int       =  "relu"

	snake_case__			:				List[str]       =  state_dict["decoder.embed_tokens.weight"]
	snake_case__			:				Tuple       =  MBartForConditionalGeneration(snake_case_       )
	model.model.load_state_dict(snake_case_       )

	if finetuned:
		snake_case__			:				Any       =  make_linear_from_emb(model.model.shared       )

	return model


if __name__ == "__main__":
	__lowerCamelCase :      int										=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
	)
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	parser.add_argument(
	    """--hf_config""",
	    default="""facebook/mbart-large-cc25""",
	    type=str,
	    help="""Which huggingface architecture to use: mbart-large""",
	)
	parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
	parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
	__lowerCamelCase :      Optional[Any]										=				parser.parse_args()
	__lowerCamelCase :      str										=				convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)



