


'''simple docstring'''







import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.bert.modeling_flax_bert import (
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForNextSentencePrediction,
           FlaxBertForPreTraining,
           FlaxBertForQuestionAnswering,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertModel,
       )
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__=13      ,	snake_case__=7      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=99      ,	snake_case__=32      ,	snake_case__=5      ,	snake_case__=4      ,	snake_case__=37      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=16      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=4      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       parent
            _lowerCAmelCase    :							Union[str, Any]   			=       batch_size
            _lowerCAmelCase    :							Union[str, Any]   			=       seq_length
            _lowerCAmelCase    :							Any   			=       is_training
            _lowerCAmelCase    :							List[Any]   			=       use_attention_mask
            _lowerCAmelCase    :							Any   			=       use_token_type_ids
            _lowerCAmelCase    :							Optional[Any]   			=       use_labels
            _lowerCAmelCase    :							Tuple   			=       vocab_size
            _lowerCAmelCase    :							Union[str, Any]   			=       hidden_size
            _lowerCAmelCase    :							int   			=       num_hidden_layers
            _lowerCAmelCase    :							List[Any]   			=       num_attention_heads
            _lowerCAmelCase    :							Union[str, Any]   			=       intermediate_size
            _lowerCAmelCase    :							List[Any]   			=       hidden_act
            _lowerCAmelCase    :							List[Any]   			=       hidden_dropout_prob
            _lowerCAmelCase    :							int   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							int   			=       max_position_embeddings
            _lowerCAmelCase    :							Optional[int]   			=       type_vocab_size
            _lowerCAmelCase    :							Dict   			=       type_sequence_label_size
            _lowerCAmelCase    :							List[str]   			=       initializer_range
            _lowerCAmelCase    :							Optional[Any]   			=       num_choices







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       ids_tensor([self.batch_size, self.seq_length]      ,	self.vocab_size   )

            _lowerCAmelCase    :							Dict   			=       None
            if self.use_attention_mask:
                   _lowerCAmelCase    :							List[Any]   			=       random_attention_mask([self.batch_size, self.seq_length]   )

            _lowerCAmelCase    :							Any   			=       None
            if self.use_token_type_ids:
                   _lowerCAmelCase    :							Optional[Any]   			=       ids_tensor([self.batch_size, self.seq_length]      ,	self.type_vocab_size   )

            _lowerCAmelCase    :							Tuple   			=       BertConfig(
                vocab_size=self.vocab_size      ,	hidden_size=self.hidden_size      ,	num_hidden_layers=self.num_hidden_layers      ,	num_attention_heads=self.num_attention_heads      ,	intermediate_size=self.intermediate_size      ,	hidden_act=self.hidden_act      ,	hidden_dropout_prob=self.hidden_dropout_prob      ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,	max_position_embeddings=self.max_position_embeddings      ,	type_vocab_size=self.type_vocab_size      ,	is_decoder=snake_case__      ,	initializer_range=self.initializer_range      ,	)

            return config, input_ids, token_type_ids, attention_mask







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.prepare_config_and_inputs()
            _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Dict   			=       config_and_inputs
            _lowerCAmelCase    :							Optional[Any]   			=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
            return config, inputs_dict







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.prepare_config_and_inputs()
            _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Any   			=       config_and_inputs

            _lowerCAmelCase    :							Optional[Any]   			=       True
            _lowerCAmelCase    :							Dict   			=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
            _lowerCAmelCase    :							int   			=       ids_tensor([self.batch_size, self.seq_length]      ,	vocab_size=2   )

            return (
                config,
                input_ids,
                attention_mask,
                encoder_hidden_states,
                encoder_attention_mask,
            )
@require_flax
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    True

     __magic_name__    			=    (
         (
             FlaxBertModel,
             FlaxBertForPreTraining,
             FlaxBertForMaskedLM,
             FlaxBertForMultipleChoice,
             FlaxBertForQuestionAnswering,
             FlaxBertForNextSentencePrediction,
             FlaxBertForSequenceClassification,
             FlaxBertForTokenClassification,
             FlaxBertForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       FlaxBertModelTester(self   )







     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       FlaxBertModel.from_pretrained('bert-base-cased'   )
            _lowerCAmelCase    :							List[Any]   			=       model(np.ones((1, 1)   )   )
            self.assertIsNotNone(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))

