


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
	import torch




class       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_=14       ,  lowercase_=7       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=False       ,  lowercase_=True       ,  lowercase_=99       ,  lowercase_=32       ,  lowercase_=4       ,  lowercase_=4       ,  lowercase_=4       ,  lowercase_=37       ,  lowercase_="gelu"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=512       ,  lowercase_=0.02       ,  )  -> int:
											a__      =parent
											a__      =batch_size
											a__      =seq_length
											a__      =is_training
											a__      =use_input_mask
											a__      =use_token_type_ids
											a__      =use_labels
											a__      =vocab_size
											a__      =hidden_size
											a__      =rotary_dim
											a__      =num_hidden_layers
											a__      =num_attention_heads
											a__      =intermediate_size
											a__      =hidden_act
											a__      =hidden_dropout_prob
											a__      =attention_probs_dropout_prob
											a__      =max_position_embeddings
											a__      =initializer_range
											a__      =None
											a__      =vocab_size - 1
											a__      =vocab_size - 1
											a__      =vocab_size - 1





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size)

											a__      =None
											if self.use_input_mask:
																		a__      =random_attention_mask([self.batch_size, self.seq_length])

											a__      =GPTJConfig(
											    vocab_size=self.vocab_size       ,  n_embd=self.hidden_size       ,  n_layer=self.num_hidden_layers       ,  n_head=self.num_attention_heads       ,  n_positions=self.max_position_embeddings       ,  use_cache=lowercase_       ,  bos_token_id=self.bos_token_id       ,  eos_token_id=self.eos_token_id       ,  pad_token_id=self.pad_token_id       ,  rotary_dim=self.rotary_dim       ,  )

											return (config, input_ids, input_mask)





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =self.prepare_config_and_inputs()
											a__    ,     a__    ,     a__      =config_and_inputs
											a__      ={'input_ids': input_ids, 'attention_mask': attention_mask}
											return config, inputs_dict





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> Any:
											a__      =20
											a__      =model_class_name(lowercase_)

											a__      =model.init_cache(input_ids.shape[0]       ,  lowercase_)
											a__      =jnp.ones((input_ids.shape[0], max_decoder_length)       ,  dtype='i4')

											a__      =jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,  (input_ids.shape[0], input_ids.shape[-1] - 1))
											a__      =model(
											    input_ids[:, :-1]       ,  attention_mask=lowercase_       ,  past_key_values=lowercase_       ,  position_ids=lowercase_       ,  )

											a__      =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,  dtype='i4')
											a__      =model(
											    input_ids[:, -1:]       ,  attention_mask=lowercase_       ,  past_key_values=outputs_cache.past_key_values       ,  position_ids=lowercase_       ,  )

											a__      =model(lowercase_)

											a__      =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
											self.parent.assertTrue(diff < 1e-3       ,  msg=F"""Max diff is {diff}""")





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =20
											a__      =model_class_name(lowercase_)

											a__      =jnp.concatenate(
											    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]       ,  axis=-1       ,  )

											a__      =model.init_cache(input_ids.shape[0]       ,  lowercase_)
											a__      =jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,  (input_ids.shape[0], input_ids.shape[-1] - 1))

											a__      =model(
											    input_ids[:, :-1]       ,  attention_mask=lowercase_       ,  past_key_values=lowercase_       ,  position_ids=lowercase_       ,  )
											a__      =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,  dtype='i4')
											a__      =model(
											    input_ids[:, -1:]       ,  past_key_values=outputs_cache.past_key_values       ,  attention_mask=lowercase_       ,  position_ids=lowercase_       ,  )

											a__      =model(lowercase_       ,  attention_mask=lowercase_)

											a__      =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
											self.parent.assertTrue(diff < 1e-3       ,  msg=F"""Max diff is {diff}""")







@require_flax
class       lowercase_					(lowercase__      ,					lowercase__      ,					unittest.TestCase   ):
				snake_case	    =(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
				snake_case	    =(FlaxGPTJForCausalLM,) if is_flax_available() else ()





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =FlaxGPTJModelTester(self)





				def 						__UpperCamelCase    (				self)  -> Tuple:
											for model_class_name in self.all_model_classes:
																		a__    ,     a__    ,     a__      =self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Any:
											for model_class_name in self.all_model_classes:
																		a__    ,     a__    ,     a__      =self.model_tester.prepare_config_and_inputs()
																		self.model_tester.check_use_cache_forward_with_attn_mask(
																		    lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)





				@tooslow
				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =GPTaTokenizer.from_pretrained('gpt2'       ,  pad_token='<|endoftext|>'       ,  padding_side='left')
											a__      =tokenizer(['Hello this is a long string', 'Hey']       ,  return_tensors='np'       ,  padding=lowercase_       ,  truncation=lowercase_)

											a__      =FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B')
											a__      =False
											a__      =model.config.eos_token_id

											a__      =jax.jit(model.generate)

											a__      =jit_generate(
											    inputs['input_ids']       ,  attention_mask=inputs['attention_mask']       ,  pad_token_id=tokenizer.pad_token_id).sequences

											a__      =tokenizer.batch_decode(lowercase_       ,  skip_special_tokens=lowercase_)

											a__      =[
											    'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
											    'Hey, I\'m a little late to the party. I\'m going to',
											]

											self.assertListEqual(lowercase_       ,  lowercase_)





				@is_pt_flax_cross_test
				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__):
																									# prepare inputs
																									a__      =self._prepare_for_class(lowercase_       ,  lowercase_)
																									a__      ={k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									a__      =model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									a__      =getattr(lowercase_       ,  lowercase_)

																									a__    ,     a__      =pt_inputs['input_ids'].shape
																									a__      =np.random.randint(0       ,  seq_length - 1       ,  size=(batch_size,))
																									for batch_idx, start_index in enumerate(lowercase_):
																																a__      =0
																																a__      =1
																																a__      =0
																																a__      =1
																									a__      =pt_model_class(lowercase_).eval()
																									a__      =model_class(lowercase_       ,  dtype=jnp.floataa)

																									a__      =convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,  lowercase_)
																									a__      =fx_state

																									with torch.no_grad():
																																a__      =pt_model(**lowercase_).to_tuple()

																									a__      =fx_model(**lowercase_).to_tuple()
																									self.assertEqual(len(lowercase_)       ,  len(lowercase_)       ,  'Output lengths differ between Flax and PyTorch')
																									for fx_output, pt_output in zip(lowercase_       ,  lowercase_):
																																self.assert_almost_equals(fx_output[:, -1]       ,  pt_output[:, -1].numpy()       ,  4e-2)

																									with tempfile.TemporaryDirectory() as tmpdirname:
																																pt_model.save_pretrained(lowercase_)
																																a__      =model_class.from_pretrained(lowercase_       ,  from_pt=lowercase_)

																									a__      =fx_model_loaded(**lowercase_).to_tuple()
																									self.assertEqual(
																									    len(lowercase_)       ,  len(lowercase_)       ,  'Output lengths differ between Flax and PyTorch')
																									for fx_output_loaded, pt_output in zip(lowercase_       ,  lowercase_):
																																self.assert_almost_equals(fx_output_loaded[:, -1]       ,  pt_output[:, -1].numpy()       ,  4e-2)





				@is_pt_flax_cross_test
				def 						__UpperCamelCase    (				self)  -> Dict:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__):
																									# prepare inputs
																									a__      =self._prepare_for_class(lowercase_       ,  lowercase_)
																									a__      ={k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

																									# load corresponding PyTorch class
																									a__      =model_class.__name__[4:]  # Skip the "Flax" at the beginning
																									a__      =getattr(lowercase_       ,  lowercase_)

																									a__      =pt_model_class(lowercase_).eval()
																									a__      =model_class(lowercase_       ,  dtype=jnp.floataa)

																									a__      =load_flax_weights_in_pytorch_model(lowercase_       ,  fx_model.params)
																									a__    ,     a__      =pt_inputs['input_ids'].shape
																									a__      =np.random.randint(0       ,  seq_length - 1       ,  size=(batch_size,))
																									for batch_idx, start_index in enumerate(lowercase_):
																																a__      =0
																																a__      =1
																																a__      =0
																																a__      =1

																									# make sure weights are tied in PyTorch
																									pt_model.tie_weights()

																									with torch.no_grad():
																																a__      =pt_model(**lowercase_).to_tuple()

																									a__      =fx_model(**lowercase_).to_tuple()
																									self.assertEqual(len(lowercase_)       ,  len(lowercase_)       ,  'Output lengths differ between Flax and PyTorch')
																									for fx_output, pt_output in zip(lowercase_       ,  lowercase_):
																																self.assert_almost_equals(fx_output[:, -1]       ,  pt_output[:, -1].numpy()       ,  4e-2)

																									with tempfile.TemporaryDirectory() as tmpdirname:
																																fx_model.save_pretrained(lowercase_)
																																a__      =pt_model_class.from_pretrained(lowercase_       ,  from_flax=lowercase_)

																									with torch.no_grad():
																																a__      =pt_model_loaded(**lowercase_).to_tuple()

																									self.assertEqual(
																									    len(lowercase_)       ,  len(lowercase_)       ,  'Output lengths differ between Flax and PyTorch')
																									for fx_output, pt_output in zip(lowercase_       ,  lowercase_):
																																self.assert_almost_equals(fx_output[:, -1]       ,  pt_output[:, -1].numpy()       ,  4e-2)





				@tooslow
				def 						__UpperCamelCase    (				self)  -> Tuple:
											for model_class_name in self.all_model_classes:
																		a__      =model_class_name.from_pretrained('EleutherAI/gpt-j-6B')
																		a__      =model(np.ones((1, 1)))
																		self.assertIsNotNone(lowercase_)





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	_lowerCAmelCase:						int   				=       argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
	)
	parser.add_argument(
	    '--original_config_file',
	    type=str,
	    required=True,
	    help='The YAML config file corresponding to the original architecture.',
	)
	parser.add_argument(
	    '--num_in_channels',
	    default=None,
	    type=int,
	    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
	)
	parser.add_argument(
	    '--image_size',
	    default=512,
	    type=int,
	    help=(
	        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
	        ' Base. Use 768 for Stable Diffusion v2.'
	    ),
	)
	parser.add_argument(
	    '--extract_ema',
	    action='store_true',
	    help=(
	        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
	        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
	        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
	    ),
	)
	parser.add_argument(
	    '--upcast_attention',
	    action='store_true',
	    help=(
	        'Whether the attention computation should always be upcasted. This is necessary when running stable'
	        ' diffusion 2.1.'
	    ),
	)
	parser.add_argument(
	    '--from_safetensors',
	    action='store_true',
	    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
	)
	parser.add_argument(
	    '--to_safetensors',
	    action='store_true',
	    help='Whether to store pipeline in safetensors format or not.',
	)
	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')






	def   _lowercase(					__a				:						Optional[Any]     ):
								if string == "True":
															return True
								elif string == "False":
															return False
								else:
															raise ValueError(f"""could not parse string as bool {string}"""     )

	parser.add_argument(
	    '--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
	)

	parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)

	_lowerCAmelCase:						str   				=       parser.parse_args()

	_lowerCAmelCase:						Tuple   				=       download_controlnet_from_original_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    extract_ema=args.extract_ema,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    use_linear_projection=args.use_linear_projection,
	    cross_attention_dim=args.cross_attention_dim,
	)







	controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

