

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__a    :Optional[Any]      =			get_tests_dir('fixtures/test_sentencepiece.model')

@require_sentencepiece
@require_tokenizers
class   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			int      		=       XGLMTokenizer
      _lowerCamelCase       :			Union[str, Any]      		=       XGLMTokenizerFast
      _lowerCamelCase       :			Dict      		=       True
      _lowerCamelCase       :			Optional[Any]      		=       True





      def 		__A	(   self							:				Tuple	):
            super().setUp()

            # We have a SentencePiece fixture for testing
            A_						  =				XGLMTokenizer(UpperCAmelCase						,			keep_accents=UpperCAmelCase	)
            tokenizer.save_pretrained(self.tmpdirname	)





      def 		__A	(   self							:				Dict	):
            A_						  =				"<pad>"
            A_						  =				1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase	)						,			UpperCAmelCase	)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase	)						,			UpperCAmelCase	)





      def 		__A	(   self							:				Optional[int]	):
            A_						  =				list(self.get_tokenizer().get_vocab().keys()	)

            self.assertEqual(vocab_keys[0]						,			"<s>"	)
            self.assertEqual(vocab_keys[1]						,			"<pad>"	)
            self.assertEqual(len(UpperCAmelCase	)						,			1008	)





      def 		__A	(   self							:				int	):
            self.assertEqual(self.get_tokenizer().vocab_size						,			1008	)





      def 		__A	(   self							:				Dict	):
            A_						  =				XGLMTokenizer(UpperCAmelCase						,			keep_accents=UpperCAmelCase	)

            A_						  =				tokenizer.tokenize("This is a test"	)
            self.assertListEqual(UpperCAmelCase						,			["▁This", "▁is", "▁a", "▁t", "est"]	)

            self.assertListEqual(
                tokenizer.convert_tokens_to_ids(UpperCAmelCase	)						,			[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,			)

            A_						  =				tokenizer.tokenize("I was born in 92000, and this is falsé."	)
            self.assertListEqual(
                UpperCAmelCase						,			[
                    SPIECE_UNDERLINE + "I",
                    SPIECE_UNDERLINE + "was",
                    SPIECE_UNDERLINE + "b",
                    "or",
                    "n",
                    SPIECE_UNDERLINE + "in",
                    SPIECE_UNDERLINE + "",
                    "9",
                    "2",
                    "0",
                    "0",
                    "0",
                    ",",
                    SPIECE_UNDERLINE + "and",
                    SPIECE_UNDERLINE + "this",
                    SPIECE_UNDERLINE + "is",
                    SPIECE_UNDERLINE + "f",
                    "al",
                    "s",
                    "é",
                    ".",
                ]						,			)
            A_						  =				tokenizer.convert_tokens_to_ids(UpperCAmelCase	)
            self.assertListEqual(
                UpperCAmelCase						,			[
                    value + tokenizer.fairseq_offset
                    for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                ]						,			)

            A_						  =				tokenizer.convert_ids_to_tokens(UpperCAmelCase	)
            self.assertListEqual(
                UpperCAmelCase						,			[
                    SPIECE_UNDERLINE + "I",
                    SPIECE_UNDERLINE + "was",
                    SPIECE_UNDERLINE + "b",
                    "or",
                    "n",
                    SPIECE_UNDERLINE + "in",
                    SPIECE_UNDERLINE + "",
                    "<unk>",
                    "2",
                    "0",
                    "0",
                    "0",
                    ",",
                    SPIECE_UNDERLINE + "and",
                    SPIECE_UNDERLINE + "this",
                    SPIECE_UNDERLINE + "is",
                    SPIECE_UNDERLINE + "f",
                    "al",
                    "s",
                    "<unk>",
                    ".",
                ]						,			)





      @cached_property
      def 		__A	(   self							:				List[Any]	):
            return XGLMTokenizer.from_pretrained("facebook/xglm-564M"	)





      def 		__A	(   self							:				Union[str, Any]	):
            with tempfile.NamedTemporaryFile() as f:
                  shutil.copyfile(UpperCAmelCase						,			f.name	)
                  A_						  =				XGLMTokenizer(f.name						,			keep_accents=UpperCAmelCase	)
                  A_						  =				pickle.dumps(UpperCAmelCase	)
            pickle.loads(UpperCAmelCase	)





      def 		__A	(   self							:				str	):
            if not self.test_rust_tokenizer:
                  return

            A_						  =				self.get_tokenizer()
            A_						  =				self.get_rust_tokenizer()

            A_						  =				"I was born in 92000, and this is falsé."

            A_						  =				tokenizer.tokenize(UpperCAmelCase	)
            A_						  =				rust_tokenizer.tokenize(UpperCAmelCase	)
            self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				tokenizer.encode(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
            A_						  =				rust_tokenizer.encode(UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
            self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				self.get_rust_tokenizer()
            A_						  =				tokenizer.encode(UpperCAmelCase	)
            A_						  =				rust_tokenizer.encode(UpperCAmelCase	)
            self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)





      @slow
      def 		__A	(   self							:				Optional[int]	):
            A_						  =				"Hello World!"
            A_						  =				[2, 31227, 4447, 35]

            self.assertListEqual(UpperCAmelCase						,			self.big_tokenizer.encode(UpperCAmelCase	)	)





      @slow
      def 		__A	(   self							:				Any	):
            A_						  =				(
                "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
                " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
            )
            # fmt: off
            A_						  =				[2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
            # fmt: on

            self.assertListEqual(UpperCAmelCase						,			self.big_tokenizer.encode(UpperCAmelCase	)	)







      @slow
      def 		__A	(   self							:				Any	):
            # fmt: off
            A_						  =				{
                "input_ids": [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 7, 597, 32, 24, 3688, 9865, 5]],
                "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
            }  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=UpperCAmelCase						,			model_name="facebook/xglm-564M"						,			padding=UpperCAmelCase						,			)






'''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": 48000,
        "sample_size": 65536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 48000,
        "sample_size": 65536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 48000,
        "sample_size": 131072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 16000,
        "sample_size": 65536,
    },
}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   return torch.atana(__UpperCAmelCase				,						__UpperCAmelCase   ) / math.pi * 2

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch.sin(t * math.pi / 2   ) ** 2
   __SCREAMING_SNAKE_CASE   				=							(1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(__UpperCAmelCase				,						__UpperCAmelCase   )







class __a			( _snake_case      ):
   pass







class __a			( nn.Module      ):





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

      super().__init__()

      __SCREAMING_SNAKE_CASE   				=							DiffusionAttnUnetaD(lowerCamelCase					,n_attn_layers=4     )
      __SCREAMING_SNAKE_CASE   				=							deepcopy(self.diffusion     )
      __SCREAMING_SNAKE_CASE   				=							torch.quasirandom.SobolEngine(1					,scramble=lowerCamelCase     )





def     __magic_name__							(				__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							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     __magic_name__							(				__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







   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     __magic_name__							(				__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=13   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							input_string

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

   __SCREAMING_SNAKE_CASE   				=							0
   if string.startswith("""net.3."""   ):
      depth += 1
      __SCREAMING_SNAKE_CASE   				=							string[6:]
   elif string.startswith("""net."""   ):
      __SCREAMING_SNAKE_CASE   				=							string[4:]

   while string.startswith("""main.7."""   ):
      depth += 1
      __SCREAMING_SNAKE_CASE   				=							string[7:]

   if string.startswith("""main."""   ):
      __SCREAMING_SNAKE_CASE   				=							string[5:]

   # mid block
   if string[:2].isdigit():
      __SCREAMING_SNAKE_CASE   				=							string[:2]
      __SCREAMING_SNAKE_CASE   				=							string[2:]
   else:
      __SCREAMING_SNAKE_CASE   				=							string[0]
      __SCREAMING_SNAKE_CASE   				=							string[1:]

   if depth == max_depth:
      __SCREAMING_SNAKE_CASE   				=							MID_NUM_TO_LAYER[layer_num]
      __SCREAMING_SNAKE_CASE   				=							"""mid_block"""
   elif depth > 0 and int(__UpperCAmelCase   ) < 7:
      __SCREAMING_SNAKE_CASE   				=							DOWN_NUM_TO_LAYER[layer_num]
      __SCREAMING_SNAKE_CASE   				=							f"""down_blocks.{depth}"""
   elif depth > 0 and int(__UpperCAmelCase   ) > 7:
      __SCREAMING_SNAKE_CASE   				=							UP_NUM_TO_LAYER[layer_num]
      __SCREAMING_SNAKE_CASE   				=							f"""up_blocks.{max_depth - depth - 1}"""
   elif depth == 0:
      __SCREAMING_SNAKE_CASE   				=							DEPTH_0_TO_LAYER[layer_num]
      __SCREAMING_SNAKE_CASE   				=							f"""up_blocks.{max_depth - 1}""" if int(__UpperCAmelCase   ) > 3 else """down_blocks.0"""

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

   __SCREAMING_SNAKE_CASE   				=							string_left[1:]

   if "resnets" in new_layer:
      __SCREAMING_SNAKE_CASE   				=							convert_resconv_naming(__UpperCAmelCase   )
   elif "attentions" in new_layer:
      __SCREAMING_SNAKE_CASE   				=							convert_attn_naming(__UpperCAmelCase   )
      __SCREAMING_SNAKE_CASE   				=							new_string_left

   if not isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							prefix + """.""" + new_layer + """.""" + string_left
   else:
      __SCREAMING_SNAKE_CASE   				=							[prefix + """.""" + new_layer + """.""" + s for s in string_left]
   return new_string

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							{}
   for k, v in state_dict.items():
      if k.endswith("""kernel"""   ):
         # up- and downsample layers, don't have trainable weights
         continue

      __SCREAMING_SNAKE_CASE   				=							rename(__UpperCAmelCase   )

      # check if we need to transform from Conv => Linear for attention
      if isinstance(__UpperCAmelCase				,						__UpperCAmelCase   ):
         __SCREAMING_SNAKE_CASE   				=							transform_conv_attns(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
      else:
         __SCREAMING_SNAKE_CASE   				=							v

   return new_state_dict

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[Any]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )

   __SCREAMING_SNAKE_CASE   				=							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()}"""
      __SCREAMING_SNAKE_CASE   				=							download(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							MODELS_MAP[model_name]["""sample_rate"""]
   __SCREAMING_SNAKE_CASE   				=							MODELS_MAP[model_name]["""sample_size"""]

   __SCREAMING_SNAKE_CASE   				=							Object()
   __SCREAMING_SNAKE_CASE   				=							sample_size
   __SCREAMING_SNAKE_CASE   				=							sample_rate
   __SCREAMING_SNAKE_CASE   				=							0

   __SCREAMING_SNAKE_CASE   				=							UNetaDModel(sample_size=__UpperCAmelCase				,						sample_rate=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							diffusers_model.state_dict()

   __SCREAMING_SNAKE_CASE   				=							DiffusionUncond(__UpperCAmelCase   )
   orig_model.load_state_dict(torch.load(args.model_path				,						map_location=__UpperCAmelCase   )["""state_dict"""]   )
   __SCREAMING_SNAKE_CASE   				=							orig_model.diffusion_ema.eval()
   __SCREAMING_SNAKE_CASE   				=							orig_model.state_dict()
   __SCREAMING_SNAKE_CASE   				=							rename_orig_weights(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							set(renamed_state_dict.keys()   ) - set(diffusers_state_dict.keys()   )
   __SCREAMING_SNAKE_CASE   				=							set(diffusers_state_dict.keys()   ) - set(renamed_state_dict.keys()   )

   assert len(__UpperCAmelCase   ) == 0, f"""Problem with {renamed_minus_diffusers}"""
   assert all(k.endswith("""kernel"""   ) for k in list(__UpperCAmelCase   )   ), 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":
         __SCREAMING_SNAKE_CASE   				=							value.squeeze()

      __SCREAMING_SNAKE_CASE   				=							value

   diffusers_model.load_state_dict(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							100
   __SCREAMING_SNAKE_CASE   				=							33

   __SCREAMING_SNAKE_CASE   				=							IPNDMScheduler(num_train_timesteps=__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							torch.randn([1, 2, config.sample_size]				,						generator=__UpperCAmelCase   ).to(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							torch.linspace(1				,						0				,						steps + 1				,						device=__UpperCAmelCase   )[:-1]
   __SCREAMING_SNAKE_CASE   				=							get_crash_schedule(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							DanceDiffusionPipeline(unet=__UpperCAmelCase				,						scheduler=__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(33   )
   __SCREAMING_SNAKE_CASE   				=							pipe(num_inference_steps=__UpperCAmelCase				,						generator=__UpperCAmelCase   ).audios

   __SCREAMING_SNAKE_CASE   				=							sampling.iplms_sample(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						{}   )
   __SCREAMING_SNAKE_CASE   				=							generated.clamp(-1				,						1   )

   __SCREAMING_SNAKE_CASE   				=							(generated - audio).abs().sum()
   __SCREAMING_SNAKE_CASE   				=							(generated - audio).abs().max()

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

   print("""Diff sum"""				,						__UpperCAmelCase   )
   print("""Diff max"""				,						__UpperCAmelCase   )

   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)





