
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=1	):
					'''simple docstring'''
					if n_shave_prefix_segments >= 0:
										return ".".join(path.split('''.'''	)[n_shave_prefix_segments:]	)
					else:
										return ".".join(path.split('''.'''	)[:n_shave_prefix_segments]	)



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=0	):
					'''simple docstring'''
					lowercase					=		[]
					for old_item in old_list:
										lowercase					=		old_item.replace('''in_layers.0'''				,    '''norm1'''	)
										lowercase					=		new_item.replace('''in_layers.2'''				,    '''conv1'''	)

										lowercase					=		new_item.replace('''out_layers.0'''				,    '''norm2'''	)
										lowercase					=		new_item.replace('''out_layers.3'''				,    '''conv2'''	)

										lowercase					=		new_item.replace('''emb_layers.1'''				,    '''time_emb_proj'''	)
										lowercase					=		new_item.replace('''skip_connection'''				,    '''conv_shortcut'''	)

										lowercase					=		shave_segments(lowerCAmelCase__				,    n_shave_prefix_segments=lowerCAmelCase__	)

										mapping.append({'''old''': old_item, '''new''': new_item}	)

					return mapping



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=0	):
					'''simple docstring'''
					lowercase					=		[]
					for old_item in old_list:
										lowercase					=		old_item

										lowercase					=		new_item.replace('''norm.weight'''				,    '''group_norm.weight'''	)
										lowercase					=		new_item.replace('''norm.bias'''				,    '''group_norm.bias'''	)

										lowercase					=		new_item.replace('''proj_out.weight'''				,    '''proj_attn.weight'''	)
										lowercase					=		new_item.replace('''proj_out.bias'''				,    '''proj_attn.bias'''	)

										lowercase					=		shave_segments(lowerCAmelCase__				,    n_shave_prefix_segments=lowerCAmelCase__	)

										mapping.append({'''old''': old_item, '''new''': new_item}	)

					return mapping



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=None				,    lowerCAmelCase__=None				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					assert isinstance(lowerCAmelCase__				,    lowerCAmelCase__	), "Paths should be a list of dicts containing 'old' and 'new' keys."

					# Splits the attention layers into three variables.
					if attention_paths_to_split is not None:
										for path, path_map in attention_paths_to_split.items():
															lowercase					=		old_checkpoint[path]
															lowercase					=		old_tensor.shape[0] // 3

															lowercase					=		(-1, channels) if len(old_tensor.shape	) == 3 else (-1)

															lowercase					=		old_tensor.shape[0] // config['''num_head_channels'''] // 3

															lowercase					=		old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
															lowercase			,		lowercase			,		lowercase					=		old_tensor.split(channels // num_heads				,    dim=1	)

															lowercase					=		query.reshape(lowerCAmelCase__	)
															lowercase					=		key.reshape(lowerCAmelCase__	)
															lowercase					=		value.reshape(lowerCAmelCase__	)

					for path in paths:
										lowercase					=		path['''new''']

										# These have already been assigned
										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
															continue

										# Global renaming happens here
										lowercase					=		new_path.replace('''middle_block.0'''				,    '''mid_block.resnets.0'''	)
										lowercase					=		new_path.replace('''middle_block.1'''				,    '''mid_block.attentions.0'''	)
										lowercase					=		new_path.replace('''middle_block.2'''				,    '''mid_block.resnets.1'''	)

										if additional_replacements is not None:
															for replacement in additional_replacements:
																				lowercase					=		new_path.replace(replacement['''old''']				,    replacement['''new''']	)

        # proj_attn.weight has to be converted from conv 1D to linear
										if "proj_attn.weight" in new_path:
															lowercase					=		old_checkpoint[path['''old''']][:, :, 0]
										else:
															lowercase					=		old_checkpoint[path['''old''']]



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		{}

					lowercase					=		checkpoint['''time_embed.0.weight''']
					lowercase					=		checkpoint['''time_embed.0.bias''']
					lowercase					=		checkpoint['''time_embed.2.weight''']
					lowercase					=		checkpoint['''time_embed.2.bias''']

					lowercase					=		checkpoint['''input_blocks.0.0.weight''']
					lowercase					=		checkpoint['''input_blocks.0.0.bias''']

					lowercase					=		checkpoint['''out.0.weight''']
					lowercase					=		checkpoint['''out.0.bias''']
					lowercase					=		checkpoint['''out.2.weight''']
					lowercase					=		checkpoint['''out.2.bias''']

					# Retrieves the keys for the input blocks only
					lowercase					=		len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''input_blocks''' in layer}	)
					lowercase					=		{
					    layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
					    for layer_id in range(lowerCAmelCase__	)
					}

					# Retrieves the keys for the middle blocks only
					lowercase					=		len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''middle_block''' in layer}	)
					lowercase					=		{
					    layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
					    for layer_id in range(lowerCAmelCase__	)
					}

					# Retrieves the keys for the output blocks only
					lowercase					=		len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''output_blocks''' in layer}	)
					lowercase					=		{
					    layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
					    for layer_id in range(lowerCAmelCase__	)
					}

					for i in range(1				,    lowerCAmelCase__	):
										lowercase					=		(i - 1) // (config['''num_res_blocks'''] + 1)
										lowercase					=		(i - 1) % (config['''num_res_blocks'''] + 1)

										lowercase					=		[key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
										lowercase					=		[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

										if f'input_blocks.{i}.0.op.weight' in checkpoint:
															lowercase					=		checkpoint[
															    f'input_blocks.{i}.0.op.weight'
															]
															lowercase					=		checkpoint[
															    f'input_blocks.{i}.0.op.bias'
															]
															continue

										lowercase					=		renew_resnet_paths(lowerCAmelCase__	)
										lowercase					=		{'''old''': f'input_blocks.{i}.0', '''new''': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
										lowercase					=		{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
										assign_to_checkpoint(
										    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    additional_replacements=[meta_path, resnet_op]				,    config=lowerCAmelCase__	)

										if len(lowerCAmelCase__	):
															lowercase					=		renew_attention_paths(lowerCAmelCase__	)
															lowercase					=		{
															    '''old''': f'input_blocks.{i}.1',
															    '''new''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
															}
															lowercase					=		{
															    f'input_blocks.{i}.1.qkv.bias': {
															        '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
															        '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
															        '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
															    },
															    f'input_blocks.{i}.1.qkv.weight': {
															        '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
															        '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
															        '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
															    },
															}
															assign_to_checkpoint(
															    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    additional_replacements=[meta_path]				,    attention_paths_to_split=lowerCAmelCase__				,    config=lowerCAmelCase__				,    )

					lowercase					=		middle_blocks[0]
					lowercase					=		middle_blocks[1]
					lowercase					=		middle_blocks[2]

					lowercase					=		renew_resnet_paths(lowerCAmelCase__	)
					assign_to_checkpoint(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    config=lowerCAmelCase__	)

					lowercase					=		renew_resnet_paths(lowerCAmelCase__	)
					assign_to_checkpoint(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    config=lowerCAmelCase__	)

					lowercase					=		renew_attention_paths(lowerCAmelCase__	)
					lowercase					=		{
					    '''middle_block.1.qkv.bias''': {
					        '''key''': '''mid_block.attentions.0.key.bias''',
					        '''query''': '''mid_block.attentions.0.query.bias''',
					        '''value''': '''mid_block.attentions.0.value.bias''',
					    },
					    '''middle_block.1.qkv.weight''': {
					        '''key''': '''mid_block.attentions.0.key.weight''',
					        '''query''': '''mid_block.attentions.0.query.weight''',
					        '''value''': '''mid_block.attentions.0.value.weight''',
					    },
					}
					assign_to_checkpoint(
					    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    attention_paths_to_split=lowerCAmelCase__				,    config=lowerCAmelCase__	)

					for i in range(lowerCAmelCase__	):
										lowercase					=		i // (config['''num_res_blocks'''] + 1)
										lowercase					=		i % (config['''num_res_blocks'''] + 1)
										lowercase					=		[shave_segments(lowerCAmelCase__				,    2	) for name in output_blocks[i]]
										lowercase					=		{}

										for layer in output_block_layers:
															lowercase			,		lowercase					=		layer.split('''.'''	)[0], shave_segments(lowerCAmelCase__				,    1	)
															if layer_id in output_block_list:
																				output_block_list[layer_id].append(lowerCAmelCase__	)
															else:
																				lowercase					=		[layer_name]

										if len(lowerCAmelCase__	) > 1:
															lowercase					=		[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
															lowercase					=		[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

															lowercase					=		renew_resnet_paths(lowerCAmelCase__	)
															lowercase					=		renew_resnet_paths(lowerCAmelCase__	)

															lowercase					=		{'''old''': f'output_blocks.{i}.0', '''new''': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
															assign_to_checkpoint(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    additional_replacements=[meta_path]				,    config=lowerCAmelCase__	)

															if ["conv.weight", "conv.bias"] in output_block_list.values():
																				lowercase					=		list(output_block_list.values()	).index(['''conv.weight''', '''conv.bias''']	)
																				lowercase					=		checkpoint[
																				    f'output_blocks.{i}.{index}.conv.weight'
																				]
																				lowercase					=		checkpoint[
																				    f'output_blocks.{i}.{index}.conv.bias'
																				]

																				# Clear attentions as they have been attributed above.
																				if len(lowerCAmelCase__	) == 2:
																									lowercase					=		[]

															if len(lowerCAmelCase__	):
																				lowercase					=		renew_attention_paths(lowerCAmelCase__	)
																				lowercase					=		{
																				    '''old''': f'output_blocks.{i}.1',
																				    '''new''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
																				}
																				lowercase					=		{
																				    f'output_blocks.{i}.1.qkv.bias': {
																				        '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
																				        '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
																				        '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
																				    },
																				    f'output_blocks.{i}.1.qkv.weight': {
																				        '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
																				        '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
																				        '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
																				    },
																				}
																				assign_to_checkpoint(
																				    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    additional_replacements=[meta_path]				,    attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions	) else None				,    config=lowerCAmelCase__				,    )
										else:
															lowercase					=		renew_resnet_paths(lowerCAmelCase__				,    n_shave_prefix_segments=1	)
															for path in resnet_0_paths:
																				lowercase					=		'''.'''.join(['''output_blocks''', str(lowerCAmelCase__	), path['''old''']]	)
																				lowercase					=		'''.'''.join(['''up_blocks''', str(lowerCAmelCase__	), '''resnets''', str(lowerCAmelCase__	), path['''new''']]	)

																				lowercase					=		checkpoint[old_path]

					return new_checkpoint


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

							parser.add_argument(
							    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
							)

							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help="The config json file corresponding to the architecture.",
							)

							parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

							lowercase__	:Optional[int]										=					parser.parse_args()

							lowercase__	:List[Any]										=					torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														lowercase__	:Union[str, Any]										=					json.loads(f.read())

							lowercase__	:List[Any]										=					convert_ldm_checkpoint(checkpoint, config)

							if "ldm" in config:
														del config["ldm"]

							lowercase__	:List[Any]										=					UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														lowercase__	:int										=					DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
														lowercase__	:List[Any]										=					VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

														lowercase__	:Optional[int]										=					LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)






import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
							import os

							import jax.numpy as jnp
							from jax import jit

							from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
							from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

							lowercase__	:Optional[Any]										=					"0.12"  # assumed parallelism: 8


if is_torch_available():
							import torch



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					if rng is None:
										lowercase					=		random.Random()

					lowercase					=		1
					for dim in shape:
										total_dims *= dim

					lowercase					=		[]
					for _ in range(lowerCAmelCase__	):
										values.append(rng.randint(0				,    vocab_size - 1	)	)

					lowercase					=		np.array(lowerCAmelCase__				,    dtype=jnp.intaa	).reshape(lowerCAmelCase__	)

					return output



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__=None	):
					'''simple docstring'''
					lowercase					=		ids_tensor(lowerCAmelCase__				,    vocab_size=2				,    rng=lowerCAmelCase__	)
					# make sure that at least one token is attended to for each batch
					lowercase					=		1
					return attn_mask







@require_flax
class        lowercase	:
							lowercase_ : Any       =None
							lowercase_ : List[str]       =()




							def 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

												# cut to half length & take max batch_size 3
												lowercase					=		2
												lowercase					=		inputs['''input_ids'''].shape[-1] // 2
												lowercase					=		inputs['''input_ids'''][:max_batch_size, :sequence_length]

												lowercase					=		jnp.ones_like(A__)
												lowercase					=		attention_mask[:max_batch_size, :sequence_length]

												# generate max 5 tokens
												lowercase					=		input_ids.shape[-1] + 5
												if config.eos_token_id is not None and config.pad_token_id is None:
																	# hack to allow generate for models such as GPT2 as is done in `generate()`
																	lowercase					=		config.eos_token_id
												return config, input_ids, attention_mask, max_length




							@is_pt_flax_cross_test
							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		0

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
																	lowercase					=		getattr(A__     ,A__)
																	lowercase					=		pt_model_class(A__).eval()
																	lowercase					=		load_flax_weights_in_pytorch_model(A__     ,flax_model.params)

																	lowercase					=		flax_model.generate(A__).sequences
																	lowercase					=		pt_model.generate(torch.tensor(A__     ,dtype=torch.long))

																	if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																						lowercase					=		flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																	self.assertListEqual(pt_generation_outputs.numpy().tolist()     ,flax_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		True
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		2

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		False
												lowercase					=		max_length
												lowercase					=		2
												lowercase					=		2

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[0]     ,input_ids.shape[0] * config.num_return_sequences)




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		True
												lowercase					=		max_length
												lowercase					=		0.8
												lowercase					=		1_0
												lowercase					=		0.3
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		max_length
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()
												lowercase					=		max_length
												lowercase					=		2
												lowercase					=		1
												lowercase					=		8
												lowercase					=		9

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		False
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())




							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		True
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())





							def 				A__   (       self):
												lowercase			,		lowercase			,		lowercase			,		lowercase					=		self._get_input_ids_and_config()

												# pad attention mask on the left
												lowercase					=		attention_mask.at[(0, 0)].set(0)

												lowercase					=		2
												lowercase					=		max_length

												for model_class in self.all_generative_model_classes:
																	lowercase					=		model_class(A__)

																	lowercase					=		model.generate(A__     ,attention_mask=A__).sequences
																	self.assertEqual(generation_outputs.shape[-1]     ,A__)

																	lowercase					=		jit(model.generate)
																	lowercase					=		jit_generate(A__     ,attention_mask=A__).sequences

																	self.assertListEqual(generation_outputs.tolist()     ,jit_generation_outputs.tolist())







@require_flax
class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												lowercase					=		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''')
												lowercase					=		FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''')

												lowercase					=		'''Hello world'''
												lowercase					=		tokenizer(A__     ,return_tensors='''np''').input_ids

												# typos are quickly detected (the correct argument is `do_sample`)
												with self.assertRaisesRegex(A__     ,'''do_samples'''):
																	model.generate(A__     ,do_samples=A__)

												# arbitrary arguments that will not be used anywhere are also not accepted
												with self.assertRaisesRegex(A__     ,'''foo'''):
																	lowercase					=		{'''foo''': '''bar'''}
																	model.generate(A__     ,**A__)




