





"""simple docstring"""






import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


A			      =	{
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 65_536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 65_536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 48_000,
        '''sample_size''': 131_072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 16_000,
        '''sample_size''': 65_536,
    },
}

def      __A   (      a_    :Optional[int] ,							a_    :Union[str, Any])		->		Union[str, Any]:
				return torch.atana(_lowercase ,							_lowercase) / math.pi * 2

def      __A   (      a_    :Tuple)		->		Union[str, Any]:
				__a			:    Dict      							=	torch.sin(t * math.pi / 2) ** 2
				__a			:    List[str]      							=	(1 - sigma**2) ** 0.5
				return alpha_sigma_to_t(_lowercase ,							_lowercase)




class 					__lowercase      (      a__				):







					'''simple docstring'''




					pass






class 					__lowercase      (      nn.Module				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase  ):
									super().__init__()

									__a			:    Tuple      							=	DiffusionAttnUnetaD(lowerCAmelCase__						,      n_attn_layers=4  )
									__a			:    Union[str, Any]      							=	deepcopy(self.diffusion  )
									__a			:    List[Any]      							=	torch.quasirandom.SobolEngine(1						,      scramble=lowerCAmelCase__  )






def      __A   (      a_    :Tuple)		->		Dict:
				__a			:    List[Any]      							=	MODELS_MAP[model_name]["url"]
				os.system(F"""wget {url} ./""")

				return F"""./{model_name}.ckpt"""


A			      =	{
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
A			      =	{
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
A			      =	{
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
    '''8''': '''resnets.3''',
    '''9''': '''attentions.3''',
    '''10''': '''resnets.4''',
    '''11''': '''attentions.4''',
    '''12''': '''resnets.5''',
    '''13''': '''attentions.5''',
}
A			      =	{
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

A			      =	{
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

A			      =	{
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}

def      __A   (      a_    :Dict)		->		Optional[Any]:






				if name.startswith('''skip'''):
								return name.replace('''skip''' ,							RES_CONV_MAP['''skip'''])

				# name has to be of format main.{digit}
				if not name.startswith('''main.'''):
								raise ValueError(F"""ResConvBlock error with {name}""")

				return name.replace(name[:6] ,							RES_CONV_MAP[name[:6]])

def      __A   (      a_    :Optional[int])		->		List[str]:






				for key, value in ATTN_MAP.items():
								if name.startswith(_lowercase) and not isinstance(_lowercase ,							_lowercase):
												return name.replace(_lowercase ,							_lowercase)
								elif name.startswith(_lowercase):
												return [name.replace(_lowercase ,							_lowercase) for v in value]
				raise ValueError(F"""Attn error with {name}""")

def      __A   (      a_    :Dict ,							a_    :Optional[Any]=13)		->		List[Any]:
				__a			:    int      							=	input_string

				if string.split('''.''')[0] == "timestep_embed":
								return string.replace('''timestep_embed''' ,							'''time_proj''')

				__a			:    Tuple      							=	0
				if string.startswith('''net.3.'''):
								depth += 1
								__a			:    str      							=	string[6:]
				elif string.startswith('''net.'''):
								__a			:    Dict      							=	string[4:]

				while string.startswith('''main.7.'''):
								depth += 1
								__a			:    str      							=	string[7:]

				if string.startswith('''main.'''):
								__a			:    Optional[int]      							=	string[5:]

				# mid block
				if string[:2].isdigit():
								__a			:    List[str]      							=	string[:2]
								__a			:    List[str]      							=	string[2:]
				else:
								__a			:    Optional[Any]      							=	string[0]
								__a			:    Any      							=	string[1:]

				if depth == max_depth:
								__a			:    Any      							=	MID_NUM_TO_LAYER[layer_num]
								__a			:    List[Any]      							=	"mid_block"
				elif depth > 0 and int(_lowercase) < 7:
								__a			:    int      							=	DOWN_NUM_TO_LAYER[layer_num]
								__a			:    Optional[int]      							=	F"""down_blocks.{depth}"""
				elif depth > 0 and int(_lowercase) > 7:
								__a			:    Optional[Any]      							=	UP_NUM_TO_LAYER[layer_num]
								__a			:    List[Any]      							=	F"""up_blocks.{max_depth - depth - 1}"""
				elif depth == 0:
								__a			:    List[str]      							=	DEPTH_0_TO_LAYER[layer_num]
								__a			:    Tuple      							=	F"""up_blocks.{max_depth - 1}""" if int(_lowercase) > 3 else "down_blocks.0"

				if not string_left.startswith('''.'''):
								raise ValueError(F"""Naming error with {input_string} and string_left: {string_left}.""")

				__a			:    Optional[int]      							=	string_left[1:]

				if "resnets" in new_layer:
								__a			:    Dict      							=	convert_resconv_naming(_lowercase)
				elif "attentions" in new_layer:
								__a			:    Union[str, Any]      							=	convert_attn_naming(_lowercase)
								__a			:    str      							=	new_string_left

				if not isinstance(_lowercase ,							_lowercase):
								__a			:    Dict      							=	prefix + "." + new_layer + "." + string_left
				else:
								__a			:    int      							=	[prefix + "." + new_layer + "." + s for s in string_left]
				return new_string

def      __A   (      a_    :int)		->		Tuple:
				__a			:    Tuple      							=	{}
				for k, v in state_dict.items():
								if k.endswith('''kernel'''):
												# up- and downsample layers, don't have trainable weights
												continue

								__a			:    Dict      							=	rename(_lowercase)

								# check if we need to transform from Conv => Linear for attention
								if isinstance(_lowercase ,							_lowercase):
												__a			:    Tuple      							=	transform_conv_attns(_lowercase ,							_lowercase ,							_lowercase)
								else:
												__a			:    int      							=	v

				return new_state_dict

def      __A   (      a_    :int ,							a_    :Tuple ,							a_    :Optional[Any])		->		Dict:






				if len(_lowercase) == 1:
								if len(v.shape) == 3:
												# weight
												__a			:    List[str]      							=	v[:, :, 0]
								else:
												# bias
												__a			:    Dict      							=	v
				else:
								# qkv matrices
								__a			:    int      							=	v.shape[0]
								__a			:    str      							=	trippled_shape // 3
								for i in range(3):
												if len(v.shape) == 3:
																__a			:    List[str]      							=	v[i * single_shape : (i + 1) * single_shape, :, 0]
												else:
																__a			:    List[str]      							=	v[i * single_shape : (i + 1) * single_shape]
				return new_state_dict

def      __A   (      a_    :int)		->		str:
				__a			:    Any      							=	torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''')

				__a			:    Dict      							=	args.model_path.split('''/''')[-1].split('''.''')[0]
				if not os.path.isfile(args.model_path):
								assert (
								    model_name == args.model_path
								), F"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
								__a			:    Optional[Any]      							=	download(_lowercase)

				__a			:    str      							=	MODELS_MAP[model_name]["sample_rate"]
				__a			:    Any      							=	MODELS_MAP[model_name]["sample_size"]

				__a			:    str      							=	Object()
				__a			:    List[str]      							=	sample_size
				__a			:    str      							=	sample_rate
				__a			:    Dict      							=	0

				__a			:    List[str]      							=	UNetaDModel(sample_size=_lowercase ,							sample_rate=_lowercase)
				__a			:    List[Any]      							=	diffusers_model.state_dict()

				__a			:    Dict      							=	DiffusionUncond(_lowercase)
				orig_model.load_state_dict(torch.load(args.model_path ,							map_location=_lowercase)['''state_dict'''])
				__a			:    str      							=	orig_model.diffusion_ema.eval()
				__a			:    str      							=	orig_model.state_dict()
				__a			:    Tuple      							=	rename_orig_weights(_lowercase)

				__a			:    Optional[int]      							=	set(renamed_state_dict.keys()) - set(diffusers_state_dict.keys())
				__a			:    Optional[Any]      							=	set(diffusers_state_dict.keys()) - set(renamed_state_dict.keys())

				assert len(_lowercase) == 0, F"""Problem with {renamed_minus_diffusers}"""
				assert all(k.endswith('''kernel''') for k in list(_lowercase)), F"""Problem with {diffusers_minus_renamed}"""

				for key, value in renamed_state_dict.items():
								assert (
								    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
								), F"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
								if key == "time_proj.weight":
												__a			:    Optional[Any]      							=	value.squeeze()

								__a			:    str      							=	value

				diffusers_model.load_state_dict(_lowercase)

				__a			:    List[Any]      							=	1_00
				__a			:    Optional[int]      							=	33

				__a			:    int      							=	IPNDMScheduler(num_train_timesteps=_lowercase)

				__a			:    Optional[Any]      							=	torch.manual_seed(_lowercase)
				__a			:    List[str]      							=	torch.randn([1, 2, config.sample_size] ,							generator=_lowercase).to(_lowercase)

				__a			:    int      							=	torch.linspace(1 ,							0 ,							steps + 1 ,							device=_lowercase)[:-1]
				__a			:    List[str]      							=	get_crash_schedule(_lowercase)

				__a			:    Tuple      							=	DanceDiffusionPipeline(unet=_lowercase ,							scheduler=_lowercase)

				__a			:    List[Any]      							=	torch.manual_seed(33)
				__a			:    List[str]      							=	pipe(num_inference_steps=_lowercase ,							generator=_lowercase).audios

				__a			:    Optional[Any]      							=	sampling.iplms_sample(_lowercase ,							_lowercase ,							_lowercase ,							{})
				__a			:    Optional[int]      							=	generated.clamp(-1 ,							1)

				__a			:    str      							=	(generated - audio).abs().sum()
				__a			:    Optional[Any]      							=	(generated - audio).abs().max()

				if args.save:
								pipe.save_pretrained(args.checkpoint_path)

				print('''Diff sum''' ,							_lowercase)
				print('''Diff max''' ,							_lowercase)

				assert diff_max < 1e-3, F"""Diff max: {diff_max} is too much :-/"""

				print(F"""Conversion for {model_name} successful!""")


if __name__ == "__main__":
		A			      =	argparse.ArgumentParser()

		parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
		parser.add_argument(
		    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
		)
		parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
		A			      =	parser.parse_args()

		main(args)






"""simple docstring"""




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

		a		:					Optional[Any]	      =     '''0.12'''  # assumed parallelism: 8


if is_torch_available():
		import torch






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       Any       ,			_lowercase		:       Union[str, Any]=None   )					->Union[str, Any]:






	'''simple docstring'''






	if rng is None:
		a :						Dict      =							random.Random()

	a :						Any      =							1
	for dim in shape:
		total_dims *= dim

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

	a :						Dict      =							np.array(_lowercase       ,			dtype=jnp.intaa   ).reshape(_lowercase   )

	return output






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       int=None   )					->Optional[Any]:






	'''simple docstring'''






	a :						str      =							ids_tensor(_lowercase       ,			vocab_size=2       ,			rng=_lowercase   )
	# make sure that at least one token is attended to for each batch
	a :						Union[str, Any]      =							1
	return attn_mask


@require_flax
class 			__UpperCamelCase							:
							lowerCamelCase  :   Dict    =None
							lowerCamelCase  :   List[Any]    =()






							def        __a  (  self )      ->					Union[str, Any]:
								a,			a :						List[Any]      =							self.model_tester.prepare_config_and_inputs_for_common()

								# cut to half length & take max batch_size 3
								a :						Any      =							2
								a :						List[Any]      =							inputs["input_ids"].shape[-1] // 2
								a :						Union[str, Any]      =							inputs["input_ids"][:max_batch_size, :sequence_length]

								a :						Optional[Any]      =							jnp.ones_like(lowerCAmelCase__ )
								a :						Any      =							attention_mask[:max_batch_size, :sequence_length]

								# generate max 5 tokens
								a :						int      =							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()`
									a :						List[str]      =							config.eos_token_id
								return config, input_ids, attention_mask, max_length






							@is_pt_flax_cross_test
							def        __a  (  self )      ->					List[Any]:
								a,			a,			a,			a :						int      =							self._get_input_ids_and_config()
								a :						Dict      =							False
								a :						Tuple      =							max_length
								a :						List[Any]      =							0

								for model_class in self.all_generative_model_classes:
									a :						List[str]      =							model_class(lowerCAmelCase__ )

									a :						int      =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
									a :						List[Any]      =							getattr(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a :						Any      =							pt_model_class(lowerCAmelCase__ ).eval()
									a :						int      =							load_flax_weights_in_pytorch_model(lowerCAmelCase__ ,   flax_model.params )

									a :						Optional[Any]      =							flax_model.generate(lowerCAmelCase__ ).sequences
									a :						Tuple      =							pt_model.generate(torch.tensor(lowerCAmelCase__ ,   dtype=torch.long ) )

									if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
										a :						List[str]      =							flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

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






							def        __a  (  self )      ->					Optional[Any]:
								a,			a,			a,			a :						Optional[Any]      =							self._get_input_ids_and_config()
								a :						List[str]      =							False
								a :						Tuple      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						Dict      =							model_class(lowerCAmelCase__ )

									a :						Union[str, Any]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Dict      =							jit(model.generate )
									a :						int      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Any:
								a,			a,			a,			a :						List[Any]      =							self._get_input_ids_and_config()
								a :						Dict      =							True
								a :						Any      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						int      =							model_class(lowerCAmelCase__ )

									a :						Optional[int]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[Any]      =							jit(model.generate )
									a :						List[Any]      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Dict:
								a,			a,			a,			a :						str      =							self._get_input_ids_and_config()
								a :						str      =							False
								a :						Union[str, Any]      =							max_length
								a :						Optional[int]      =							2

								for model_class in self.all_generative_model_classes:
									a :						List[Any]      =							model_class(lowerCAmelCase__ )

									a :						Optional[Any]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Union[str, Any]      =							jit(model.generate )
									a :						Optional[int]      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Optional[Any]:
								a,			a,			a,			a :						Optional[Any]      =							self._get_input_ids_and_config()
								a :						Optional[Any]      =							False
								a :						Tuple      =							max_length
								a :						List[Any]      =							2
								a :						Any      =							2

								for model_class in self.all_generative_model_classes:
									a :						Any      =							model_class(lowerCAmelCase__ )

									a :						List[str]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[0] ,   input_ids.shape[0] * config.num_return_sequences )






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						List[str]      =							self._get_input_ids_and_config()
								a :						Optional[int]      =							True
								a :						Dict      =							max_length
								a :						Dict      =							0.8
								a :						Any      =							10
								a :						Union[str, Any]      =							0.3
								a :						List[Any]      =							1
								a :						Optional[int]      =							8
								a :						List[Any]      =							9

								for model_class in self.all_generative_model_classes:
									a :						Tuple      =							model_class(lowerCAmelCase__ )

									a :						Any      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Tuple      =							jit(model.generate )
									a :						Dict      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						Dict      =							self._get_input_ids_and_config()
								a :						Dict      =							max_length
								a :						List[str]      =							1
								a :						List[str]      =							8
								a :						List[str]      =							9

								for model_class in self.all_generative_model_classes:
									a :						Dict      =							model_class(lowerCAmelCase__ )

									a :						str      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[str]      =							jit(model.generate )
									a :						Union[str, Any]      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					int:
								a,			a,			a,			a :						Tuple      =							self._get_input_ids_and_config()
								a :						List[Any]      =							max_length
								a :						Dict      =							2
								a :						List[Any]      =							1
								a :						List[str]      =							8
								a :						Any      =							9

								for model_class in self.all_generative_model_classes:
									a :						Optional[int]      =							model_class(lowerCAmelCase__ )

									a :						List[str]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						int      =							jit(model.generate )
									a :						Optional[int]      =							jit_generate(lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						List[str]      =							self._get_input_ids_and_config()

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

								a :						Any      =							False
								a :						Union[str, Any]      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						Union[str, Any]      =							model_class(lowerCAmelCase__ )

									a :						Union[str, Any]      =							model.generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[Any]      =							jit(model.generate )
									a :						int      =							jit_generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					List[str]:
								a,			a,			a,			a :						Union[str, Any]      =							self._get_input_ids_and_config()

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

								a :						Dict      =							True
								a :						List[Any]      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						List[str]      =							model_class(lowerCAmelCase__ )

									a :						int      =							model.generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Any      =							jit(model.generate )
									a :						List[str]      =							jit_generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences

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






							def        __a  (  self )      ->					str:
								a,			a,			a,			a :						Dict      =							self._get_input_ids_and_config()

								# pad attention mask on the left
								a :						Union[str, Any]      =							attention_mask.at[(0, 0)].set(0 )

								a :						int      =							2
								a :						Any      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						Optional[Any]      =							model_class(lowerCAmelCase__ )

									a :						List[Any]      =							model.generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[Any]      =							jit(model.generate )
									a :						Dict      =							jit_generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences

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


@require_flax
class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					List[Any]:
								a :						Any      =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert" )
								a :						Tuple      =							FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )

								a :						int      =							"Hello world"
								a :						int      =							tokenizer(lowerCAmelCase__ ,   return_tensors="np" ).input_ids

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

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


