


'''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():
       lowerCAmelCase    :     Optional[int]					=						"""pt"""
elif is_tf_available():
       lowerCAmelCase    :     Dict					=						"""tf"""
else:
       lowerCAmelCase    :     Optional[int]					=						"""jax"""
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    PerceiverTokenizer
     __magic_name__    			=    False







     def a	( self   ):

            '''simple docstring'''

            super().setUp()
            _lowerCAmelCase    :							str   			=       PerceiverTokenizer()
            tokenizer.save_pretrained(self.tmpdirname   )







     @cached_property
     def a	( self   ):

            '''simple docstring'''

            return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'   )







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            return self.tokenizer_class.from_pretrained(self.tmpdirname      ,	**snake_case__   )







     def a	( self      ,	snake_case__      ,	snake_case__=False      ,	snake_case__=20      ,	snake_case__=5   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       []
            for i in range(len(snake_case__   )   ):
                   try:
                          _lowerCAmelCase    :							List[str]   			=       tokenizer.decode([i]      ,	clean_up_tokenization_spaces=snake_case__   )
                   except UnicodeDecodeError:
                          pass
                   toks.append((i, tok)   )

            _lowerCAmelCase    :							Optional[Any]   			=       list(filter(lambda snake_case__   : re.match(R'^[ a-zA-Z]+$'      ,	t[1]   )      ,	snake_case__   )   )
            _lowerCAmelCase    :							Optional[int]   			=       list(filter(lambda snake_case__   : [t[0]] == tokenizer.encode(t[1]      ,	add_special_tokens=snake_case__   )      ,	snake_case__   )   )
            if max_length is not None and len(snake_case__   ) > max_length:
                   _lowerCAmelCase    :							Any   			=       toks[:max_length]
            if min_length is not None and len(snake_case__   ) < min_length and len(snake_case__   ) > 0:
                   while len(snake_case__   ) < min_length:
                          _lowerCAmelCase    :							str   			=       toks + toks
        # toks_str = [t[1] for t in toks]
            _lowerCAmelCase    :							Tuple   			=       [t[0] for t in toks]

            # Ensure consistency
            _lowerCAmelCase    :							Optional[Any]   			=       tokenizer.decode(snake_case__      ,	clean_up_tokenization_spaces=snake_case__   )
            if " " not in output_txt and len(snake_case__   ) > 1:
                   _lowerCAmelCase    :							List[str]   			=       (
                       tokenizer.decode([toks_ids[0]]      ,	clean_up_tokenization_spaces=snake_case__   )
                       + ' '
                       + tokenizer.decode(toks_ids[1:]      ,	clean_up_tokenization_spaces=snake_case__   )
                   )
            if with_prefix_space:
                   _lowerCAmelCase    :							List[str]   			=       ' ' + output_txt
            _lowerCAmelCase    :							Optional[Any]   			=       tokenizer.encode(snake_case__      ,	add_special_tokens=snake_case__   )
            return output_txt, output_ids







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       self.perceiver_tokenizer
            _lowerCAmelCase    :							Any   			=       'Unicode €.'
            _lowerCAmelCase    :							int   			=       tokenizer(snake_case__   )
            _lowerCAmelCase    :							Any   			=       [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
            self.assertEqual(encoded['input_ids']      ,	snake_case__   )

            # decoding
            _lowerCAmelCase    :							Optional[int]   			=       tokenizer.decode(snake_case__   )
            self.assertEqual(snake_case__      ,	'[CLS]Unicode €.[SEP]'   )

            _lowerCAmelCase    :							Optional[int]   			=       tokenizer('e è é ê ë'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
            self.assertEqual(encoded['input_ids']      ,	snake_case__   )
            # decoding
            _lowerCAmelCase    :							Optional[Any]   			=       tokenizer.decode(snake_case__   )
            self.assertEqual(snake_case__      ,	'[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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       self.perceiver_tokenizer
            _lowerCAmelCase    :							Union[str, Any]   			=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
            # fmt: off
            _lowerCAmelCase    :							Union[str, Any]   			=       [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
            # fmt: on
            _lowerCAmelCase    :							int   			=       tokenizer(snake_case__      ,	padding=snake_case__      ,	return_tensors=snake_case__   )
            self.assertIsInstance(snake_case__      ,	snake_case__   )

            if FRAMEWORK != "jax":
                   _lowerCAmelCase    :							List[str]   			=       list(batch.input_ids.numpy()[0]   )
            else:
                   _lowerCAmelCase    :							Optional[Any]   			=       list(batch.input_ids.tolist()[0]   )

            self.assertListEqual(snake_case__      ,	snake_case__   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.perceiver_tokenizer
            _lowerCAmelCase    :							Optional[Any]   			=       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
            _lowerCAmelCase    :							int   			=       tokenizer(snake_case__      ,	padding=snake_case__      ,	return_tensors=snake_case__   )
            # check if input_ids are returned and no decoder_input_ids
            self.assertIn('input_ids'      ,	snake_case__   )
            self.assertIn('attention_mask'      ,	snake_case__   )
            self.assertNotIn('decoder_input_ids'      ,	snake_case__   )
            self.assertNotIn('decoder_attention_mask'      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       self.perceiver_tokenizer
            _lowerCAmelCase    :							Dict   			=       [
                'Summary of the text.',
                'Another summary.',
            ]
            _lowerCAmelCase    :							str   			=       tokenizer(
                text_target=snake_case__      ,	max_length=32      ,	padding='max_length'      ,	truncation=snake_case__      ,	return_tensors=snake_case__   )
            self.assertEqual(32      ,	targets['input_ids'].shape[1]   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       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
            _lowerCAmelCase    :							str   			=       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
                          _lowerCAmelCase    :							Tuple   			=       tempfile.mkdtemp()

                          _lowerCAmelCase    :							Optional[Any]   			=       ' He is very happy, UNwant\u00E9d,running'
                          _lowerCAmelCase    :							Tuple   			=       tokenizer.encode(snake_case__      ,	add_special_tokens=snake_case__   )
                          tokenizer.save_pretrained(snake_case__   )

                          _lowerCAmelCase    :							List[Any]   			=       tokenizer.__class__.from_pretrained(snake_case__   )
                          _lowerCAmelCase    :							Dict   			=       after_tokenizer.encode(snake_case__      ,	add_special_tokens=snake_case__   )
                          self.assertListEqual(snake_case__      ,	snake_case__   )

                          shutil.rmtree(snake_case__   )

            _lowerCAmelCase    :							int   			=       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
                          _lowerCAmelCase    :							Optional[Any]   			=       tempfile.mkdtemp()

                          _lowerCAmelCase    :							List[str]   			=       ' He is very happy, UNwant\u00E9d,running'
                          tokenizer.add_tokens(['bim', 'bambam']   )
                          _lowerCAmelCase    :							List[str]   			=       tokenizer.additional_special_tokens
                          additional_special_tokens.append('new_additional_special_token'   )
                          tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}   )
                          _lowerCAmelCase    :							Tuple   			=       tokenizer.encode(snake_case__      ,	add_special_tokens=snake_case__   )
                          tokenizer.save_pretrained(snake_case__   )

                          _lowerCAmelCase    :							int   			=       tokenizer.__class__.from_pretrained(snake_case__   )
                          _lowerCAmelCase    :							int   			=       after_tokenizer.encode(snake_case__      ,	add_special_tokens=snake_case__   )
                          self.assertListEqual(snake_case__      ,	snake_case__   )
                          self.assertIn('new_additional_special_token'      ,	after_tokenizer.additional_special_tokens   )
                          self.assertEqual(after_tokenizer.model_max_length      ,	42   )

                          _lowerCAmelCase    :							Union[str, Any]   			=       tokenizer.__class__.from_pretrained(snake_case__      ,	model_max_length=43   )
                          self.assertEqual(tokenizer.model_max_length      ,	43   )

                          shutil.rmtree(snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       []
            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(snake_case__   )

                          with open(os.path.join(snake_case__      ,	'special_tokens_map.json'   )      ,	encoding='utf-8'   ) as json_file:
                                 _lowerCAmelCase    :							str   			=       json.load(snake_case__   )

                          with open(os.path.join(snake_case__      ,	'tokenizer_config.json'   )      ,	encoding='utf-8'   ) as json_file:
                                 _lowerCAmelCase    :							List[str]   			=       json.load(snake_case__   )

                          _lowerCAmelCase    :							Union[str, Any]   			=       [F'<extra_id_{i}>' for i in range(125   )]

                          _lowerCAmelCase    :							Optional[int]   			=       added_tokens_extra_ids + [
                              'an_additional_special_token'
                          ]
                          _lowerCAmelCase    :							List[str]   			=       added_tokens_extra_ids + [
                              'an_additional_special_token'
                          ]

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

                          # 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
                          _lowerCAmelCase    :							Tuple   			=       tokenizer_class.from_pretrained(
                              snake_case__      ,	)
                          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
                          _lowerCAmelCase    :							Any   			=       added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'      ,	lstrip=snake_case__   )]
                          _lowerCAmelCase    :							List[str]   			=       tokenizer_class.from_pretrained(
                              snake_case__      ,	additional_special_tokens=snake_case__      ,	)

                          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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       self.perceiver_tokenizer
            self.assertEqual(tokenizer.decode([178]   )      ,	'�'   )







     def a	( self   ):

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.get_tokenizers(fast=snake_case__      ,	do_lower_case=snake_case__   )
            for tokenizer in tokenizers:
                   with self.subTest(F'{tokenizer.__class__.__name__}'   ):
                          _lowerCAmelCase    :							Dict   			=       ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
                          _lowerCAmelCase    :							List[str]   			=       tokenizer.convert_tokens_to_string(snake_case__   )

                          self.assertIsInstance(snake_case__      ,	snake_case__   )





'''simple docstring'''







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase    :     Tuple					=						False
lowerCAmelCase    :     str					=						True
lowerCAmelCase    :     List[Any]					=						False


if __name__ == "__main__":
       lowerCAmelCase    :     Any					=						argparse.ArgumentParser()

       parser.add_argument(
           """--repo_path""",
           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.""")

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

       lowerCAmelCase    :     int					=						{
           """image_size""": """sample_size""",
           """num_res_blocks""": """layers_per_block""",
           """block_channels""": """block_out_channels""",
           """down_blocks""": """down_block_types""",
           """up_blocks""": """up_block_types""",
           """downscale_freq_shift""": """freq_shift""",
           """resnet_num_groups""": """norm_num_groups""",
           """resnet_act_fn""": """act_fn""",
           """resnet_eps""": """norm_eps""",
           """num_head_channels""": """attention_head_dim""",
       }

       lowerCAmelCase    :     int					=						{
           """time_steps""": """time_proj""",
           """mid""": """mid_block""",
           """downsample_blocks""": """down_blocks""",
           """upsample_blocks""": """up_blocks""",
       }

       lowerCAmelCase    :     Optional[Any]					=						"""""" if has_file(args.repo_path, """config.json""") else """unet"""

       with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
              lowerCAmelCase    :     int					=						reader.read()
              lowerCAmelCase    :     List[str]					=						json.loads(text)

       if do_only_config:
              for key in config_parameters_to_change.keys():
                     config.pop(key, None)

       if has_file(args.repo_path, """config.json"""):
              lowerCAmelCase    :     str					=						UNetaDModel(**config)
       else:
              lowerCAmelCase    :     Union[str, Any]					=						UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
              lowerCAmelCase    :     Dict					=						class_name(**config)

       if do_only_config:
              model.save_config(os.path.join(args.repo_path, subfolder))

       lowerCAmelCase    :     Union[str, Any]					=						dict(model.config)

       if do_only_renaming:
              for key, value in config_parameters_to_change.items():
                     if key in config:
                            lowerCAmelCase    :     str					=						config[key]
                            del config[key]

              lowerCAmelCase    :     Optional[int]					=						[k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
              lowerCAmelCase    :     Dict					=						[k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

       if do_only_weights:
              lowerCAmelCase    :     Tuple					=						torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

              lowerCAmelCase    :     str					=						{}
              for param_key, param_value in state_dict.items():
                     if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
                            continue
                     lowerCAmelCase    :     str					=						False
                     for key, new_key in key_parameters_to_change.items():
                            if not has_changed and param_key.split(""".""")[0] == key:
                                   lowerCAmelCase    :     Dict					=						param_value
                                   lowerCAmelCase    :     Tuple					=						True
                     if not has_changed:
                            lowerCAmelCase    :     Tuple					=						param_value

              model.load_state_dict(new_state_dict)
              model.save_pretrained(os.path.join(args.repo_path, subfolder))

