
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
		lowercase__											='pt'
elif is_tf_available():
		lowercase__											='tf'
else:
		lowercase__											='jax'







class   a_ (      UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			Optional[int]       =    PerceiverTokenizer
			lowerCamelCase__	:			Dict       =    False




			def 					lowerCAmelCase__					(       self	):
				super().setUp()
				a_      				=						PerceiverTokenizer()
				tokenizer.save_pretrained(self.tmpdirname	)




			@cached_property
			def 					lowerCAmelCase__					(       self	):
				return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""	)




			def 					lowerCAmelCase__					(       self					,   **UpperCAmelCase	):
				return self.tokenizer_class.from_pretrained(self.tmpdirname					,   **UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase=False					,   UpperCAmelCase=20					,   UpperCAmelCase=5	):
				# XXX The default common tokenizer tests assume that every ID is decodable on its own.
				# This assumption is invalid for Perceiver because single bytes might not be
				# valid utf-8 (byte 128 for instance).
				# Here we're overriding the smallest possible method to provide
				# a clean sequence without making the same assumption.

				a_      				=						[]
				for i in range(len(UpperCAmelCase	)	):
					try:
						a_      				=						tokenizer.decode([i]					,   clean_up_tokenization_spaces=UpperCAmelCase	)
					except UnicodeDecodeError:
						pass
					toks.append((i, tok)	)

				a_      				=						list(filter(lambda UpperCAmelCase	: re.match(R"""^[ a-zA-Z]+$"""					,   t[1]	)					,   UpperCAmelCase	)	)
				a_      				=						list(filter(lambda UpperCAmelCase	: [t[0]] == tokenizer.encode(t[1]					,   add_special_tokens=UpperCAmelCase	)					,   UpperCAmelCase	)	)
				if max_length is not None and len(UpperCAmelCase	) > max_length:
					a_      				=						toks[:max_length]
				if min_length is not None and len(UpperCAmelCase	) < min_length and len(UpperCAmelCase	) > 0:
					while len(UpperCAmelCase	) < min_length:
						a_      				=						toks + toks
        # toks_str = [t[1] for t in toks]
				a_      				=						[t[0] for t in toks]

				# Ensure consistency
				a_      				=						tokenizer.decode(UpperCAmelCase					,   clean_up_tokenization_spaces=UpperCAmelCase	)
				if " " not in output_txt and len(UpperCAmelCase	) > 1:
					a_      				=						(
					    tokenizer.decode([toks_ids[0]]					,   clean_up_tokenization_spaces=UpperCAmelCase	)
					    + """ """
					    + tokenizer.decode(toks_ids[1:]					,   clean_up_tokenization_spaces=UpperCAmelCase	)
					)
				if with_prefix_space:
					a_      				=						""" """ + output_txt
				a_      				=						tokenizer.encode(UpperCAmelCase					,   add_special_tokens=UpperCAmelCase	)
				return output_txt, output_ids




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.perceiver_tokenizer
				a_      				=						"""Unicode €."""
				a_      				=						tokenizer(UpperCAmelCase	)
				a_      				=						[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
				self.assertEqual(encoded["""input_ids"""]					,   UpperCAmelCase	)

				# decoding
				a_      				=						tokenizer.decode(UpperCAmelCase	)
				self.assertEqual(UpperCAmelCase					,   """[CLS]Unicode €.[SEP]"""	)

				a_      				=						tokenizer("""e è é ê ë"""	)
				a_      				=						[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
				self.assertEqual(encoded["""input_ids"""]					,   UpperCAmelCase	)
				# decoding
				a_      				=						tokenizer.decode(UpperCAmelCase	)
				self.assertEqual(UpperCAmelCase					,   """[CLS]e è é ê ë[SEP]"""	)

				# encode/decode, but with `encode` instead of `__call__`
				self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""	)	)					,   """[CLS]e è é ê ë[SEP]"""	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.perceiver_tokenizer
				a_      				=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
				# fmt: off
				a_      				=						[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
				# fmt: on
				a_      				=						tokenizer(UpperCAmelCase					,   padding=UpperCAmelCase					,   return_tensors=UpperCAmelCase	)
				self.assertIsInstance(UpperCAmelCase					,   UpperCAmelCase	)

				if FRAMEWORK != "jax":
					a_      				=						list(batch.input_ids.numpy()[0]	)
				else:
					a_      				=						list(batch.input_ids.tolist()[0]	)

				self.assertListEqual(UpperCAmelCase					,   UpperCAmelCase	)

				self.assertEqual((2, 38)					,   batch.input_ids.shape	)
				self.assertEqual((2, 38)					,   batch.attention_mask.shape	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.perceiver_tokenizer
				a_      				=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
				a_      				=						tokenizer(UpperCAmelCase					,   padding=UpperCAmelCase					,   return_tensors=UpperCAmelCase	)
				# check if input_ids are returned and no decoder_input_ids
				self.assertIn("""input_ids"""					,   UpperCAmelCase	)
				self.assertIn("""attention_mask"""					,   UpperCAmelCase	)
				self.assertNotIn("""decoder_input_ids"""					,   UpperCAmelCase	)
				self.assertNotIn("""decoder_attention_mask"""					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.perceiver_tokenizer
				a_      				=						[
				    """Summary of the text.""",
				    """Another summary.""",
				]
				a_      				=						tokenizer(
				    text_target=UpperCAmelCase					,   max_length=32					,   padding="""max_length"""					,   truncation=UpperCAmelCase					,   return_tensors=UpperCAmelCase	)
				self.assertEqual(32					,   targets["""input_ids"""].shape[1]	)




			def 					lowerCAmelCase__					(       self	):
				# safety check on max_len default value so we are sure the test works
				a_      				=						self.get_tokenizers()
				for tokenizer in tokenizers:
					with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
						self.assertNotEqual(tokenizer.model_max_length					,   42	)

        # Now let's start the test
				a_      				=						self.get_tokenizers()
				for tokenizer in tokenizers:
					with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
						# Isolate this from the other tests because we save additional tokens/etc
						a_      				=						tempfile.mkdtemp()

						a_      				=						""" He is very happy, UNwant\u00E9d,running"""
						a_      				=						tokenizer.encode(UpperCAmelCase					,   add_special_tokens=UpperCAmelCase	)
						tokenizer.save_pretrained(UpperCAmelCase	)

						a_      				=						tokenizer.__class__.from_pretrained(UpperCAmelCase	)
						a_      				=						after_tokenizer.encode(UpperCAmelCase					,   add_special_tokens=UpperCAmelCase	)
						self.assertListEqual(UpperCAmelCase					,   UpperCAmelCase	)

						shutil.rmtree(UpperCAmelCase	)

				a_      				=						self.get_tokenizers(model_max_length=42	)
				for tokenizer in tokenizers:
					with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
						# Isolate this from the other tests because we save additional tokens/etc
						a_      				=						tempfile.mkdtemp()

						a_      				=						""" He is very happy, UNwant\u00E9d,running"""
						tokenizer.add_tokens(["""bim""", """bambam"""]	)
						a_      				=						tokenizer.additional_special_tokens
						additional_special_tokens.append("""new_additional_special_token"""	)
						tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}	)
						a_      				=						tokenizer.encode(UpperCAmelCase					,   add_special_tokens=UpperCAmelCase	)
						tokenizer.save_pretrained(UpperCAmelCase	)

						a_      				=						tokenizer.__class__.from_pretrained(UpperCAmelCase	)
						a_      				=						after_tokenizer.encode(UpperCAmelCase					,   add_special_tokens=UpperCAmelCase	)
						self.assertListEqual(UpperCAmelCase					,   UpperCAmelCase	)
						self.assertIn("""new_additional_special_token"""					,   after_tokenizer.additional_special_tokens	)
						self.assertEqual(after_tokenizer.model_max_length					,   42	)

						a_      				=						tokenizer.__class__.from_pretrained(UpperCAmelCase					,   model_max_length=43	)
						self.assertEqual(tokenizer.model_max_length					,   43	)

						shutil.rmtree(UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						[]
				if self.test_slow_tokenizer:
					tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

				if self.test_rust_tokenizer:
					tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

				for tokenizer_class, tokenizer_utils in tokenizer_list:
					with tempfile.TemporaryDirectory() as tmp_dir:
						tokenizer_utils.save_pretrained(UpperCAmelCase	)

						with open(os.path.join(UpperCAmelCase					,   """special_tokens_map.json"""	)					,   encoding="""utf-8"""	) as json_file:
							a_      				=						json.load(UpperCAmelCase	)

						with open(os.path.join(UpperCAmelCase					,   """tokenizer_config.json"""	)					,   encoding="""utf-8"""	) as json_file:
							a_      				=						json.load(UpperCAmelCase	)

						a_      				=						[f'''<extra_id_{i}>''' for i in range(1_25	)]

						a_      				=						added_tokens_extra_ids + [
						    """an_additional_special_token"""
						]
						a_      				=						added_tokens_extra_ids + [
						    """an_additional_special_token"""
						]

						with open(os.path.join(UpperCAmelCase					,   """special_tokens_map.json"""	)					,   """w"""					,   encoding="""utf-8"""	) as outfile:
							json.dump(UpperCAmelCase					,   UpperCAmelCase	)
						with open(os.path.join(UpperCAmelCase					,   """tokenizer_config.json"""	)					,   """w"""					,   encoding="""utf-8"""	) as outfile:
							json.dump(UpperCAmelCase					,   UpperCAmelCase	)

						# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
						# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
						# "special_tokens_map.json" files
						a_      				=						tokenizer_class.from_pretrained(
						    UpperCAmelCase					,   )
						self.assertIn(
						    """an_additional_special_token"""					,   tokenizer_without_change_in_init.additional_special_tokens	)
						self.assertEqual(
						    ["""an_additional_special_token"""]					,   tokenizer_without_change_in_init.convert_ids_to_tokens(
						        tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]	)	)					,   )

						# Now we test that we can change the value of additional_special_tokens in the from_pretrained
						a_      				=						added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""					,   lstrip=UpperCAmelCase	)]
						a_      				=						tokenizer_class.from_pretrained(
						    UpperCAmelCase					,   additional_special_tokens=UpperCAmelCase					,   )

						self.assertIn("""a_new_additional_special_token"""					,   tokenizer.additional_special_tokens	)
						self.assertEqual(
						    ["""a_new_additional_special_token"""]					,   tokenizer.convert_ids_to_tokens(
						        tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]	)	)					,   )




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.perceiver_tokenizer
				self.assertEqual(tokenizer.decode([1_78]	)					,   """�"""	)




			def 					lowerCAmelCase__					(       self	):
				pass




			def 					lowerCAmelCase__					(       self	):
				pass




			def 					lowerCAmelCase__					(       self	):
				pass




			def 					lowerCAmelCase__					(       self	):
				pass






			def 					lowerCAmelCase__					(       self	):
				# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
				# strings and special added tokens as tokens
				a_      				=						self.get_tokenizers(fast=UpperCAmelCase					,   do_lower_case=UpperCAmelCase	)
				for tokenizer in tokenizers:
					with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
						a_      				=						["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
						a_      				=						tokenizer.convert_tokens_to_string(UpperCAmelCase	)

						self.assertIsInstance(UpperCAmelCase					,   UpperCAmelCase	)


'''simple docstring'''
import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowercase__											=version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__  ,      A__=False  ,      ):
	output_path.parent.mkdir(parents=A__  ,      exist_ok=A__						)
	# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
	# so we check the torch version for backwards compatibility
	if is_torch_less_than_1_11:
		export(
		    A__  ,      A__  ,      f=output_path.as_posix()  ,      input_names=A__  ,      output_names=A__  ,      dynamic_axes=A__  ,      do_constant_folding=A__  ,      use_external_data_format=A__  ,      enable_onnx_checker=A__  ,      opset_version=A__  ,      )
	else:
		export(
		    A__  ,      A__  ,      f=output_path.as_posix()  ,      input_names=A__  ,      output_names=A__  ,      dynamic_axes=A__  ,      do_constant_folding=A__  ,      opset_version=A__  ,      )






@torch.no_grad()
def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      A__ = False						):
	a_      				=						torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		a_      				=						"""cuda"""
	elif fpaa and not torch.cuda.is_available():
		raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""						)
	else:
		a_      				=						"""cpu"""
	a_      				=						Path(A__						)

	# VAE DECODER
	a_      				=						AutoencoderKL.from_pretrained(model_path + """/vae"""						)
	a_      				=						vae_decoder.config.latent_channels
	# forward only through the decoder part
	a_      				=						vae_decoder.decode
	onnx_export(
	    A__  ,      model_args=(
	        torch.randn(1  ,      A__  ,      25  ,      25						).to(device=A__  ,      dtype=A__						),
	        False,
	    )  ,      output_path=output_path / """vae_decoder""" / """model.onnx"""  ,      ordered_input_names=["""latent_sample""", """return_dict"""]  ,      output_names=["""sample"""]  ,      dynamic_axes={
	        """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
	    }  ,      opset=A__  ,      )
	del vae_decoder


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

		parser.add_argument(
		    '--model_path',
		    type=str,
		    required=True,
		    help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
		)

		parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
		parser.add_argument(
		    '--opset',
		    default=14,
		    type=int,
		    help='The version of the ONNX operator set to use.',
		)
		parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')

		lowercase__											=parser.parse_args()
		print(args.output_path)
		convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
		print('SD: Done: ONNX')
