






"""simple docstring"""
import gc
import unittest

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


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


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







						def 			A      (    self							)		->							Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2'   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		int       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2'
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler_params

										_SCREAMING_SNAKE_CASE   :		int       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		int       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		str       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		str       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2









"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			lowerCAmelCase__    						=			argparse.ArgumentParser(
			    description=(
			        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
			        ' Distillation'
			    )
			)
			parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
			parser.add_argument('--model_name', default='roberta-large', type=str)
			parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
			parser.add_argument('--vocab_transform', action='store_true')
			lowerCAmelCase__    						=			parser.parse_args()

			if args.model_type == "roberta":
						lowerCAmelCase__    						=			RobertaForMaskedLM.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'roberta'
			elif args.model_type == "gpt2":
						lowerCAmelCase__    						=			GPTaLMHeadModel.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'transformer'

			lowerCAmelCase__    						=			model.state_dict()
			lowerCAmelCase__    						=			{}

			# Embeddings #
			if args.model_type == "gpt2":
						for param_name in ["wte.weight", "wpe.weight"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.{param_name}']
			else:
						for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.{w}.weight'
									lowerCAmelCase__    						=			state_dict[param_name]
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.LayerNorm.{w}'
									lowerCAmelCase__    						=			state_dict[param_name]

    # Transformer Blocks #
			lowerCAmelCase__    						=			0
			for teacher_idx in [0, 2, 4, 7, 9, 11]:
						if args.model_type == "gpt2":
									for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
															]
									lowerCAmelCase__    						=			state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
						else:
									for layer in [
									    "attention.self.query",
									    "attention.self.key",
									    "attention.self.value",
									    "attention.output.dense",
									    "attention.output.LayerNorm",
									    "intermediate.dense",
									    "output.dense",
									    "output.LayerNorm",
									]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
															]
						std_idx += 1

			# Language Modeling Head ###s
			if args.model_type == "roberta":
						for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
									lowerCAmelCase__    						=			state_dict[F'{layer}']
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												lowerCAmelCase__    						=			state_dict[F'lm_head.dense.{w}']
												lowerCAmelCase__    						=			state_dict[F'lm_head.layer_norm.{w}']
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.ln_f.{w}']
						lowerCAmelCase__    						=			state_dict['lm_head.weight']

			print(F'N layers selected for distillation: {std_idx}')
			print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

			print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
			torch.save(compressed_sd, args.dump_checkpoint)

