
import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax.numpy as jnp

    from transformers.models.albert.modeling_flax_albert import (
        FlaxAlbertForMaskedLM,
        FlaxAlbertForMultipleChoice,
        FlaxAlbertForPreTraining,
        FlaxAlbertForQuestionAnswering,
        FlaxAlbertForSequenceClassification,
        FlaxAlbertForTokenClassification,
        FlaxAlbertModel,
    )




class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1_3						,						__SCREAMING_SNAKE_CASE=7						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=9_9						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=5						,						__SCREAMING_SNAKE_CASE=4						,						__SCREAMING_SNAKE_CASE=3_7						,						__SCREAMING_SNAKE_CASE="gelu"						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=0.1						,						__SCREAMING_SNAKE_CASE=5_1_2						,						__SCREAMING_SNAKE_CASE=1_6						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=4						,						)  ->   str:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]				=				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size  )

           UpperCamelCase__    :   Tuple				=				None
           if self.use_attention_mask:
                 UpperCamelCase__    :   Dict				=				random_attention_mask([self.batch_size, self.seq_length]  )

           UpperCamelCase__    :   Tuple				=				None
           if self.use_token_type_ids:
                 UpperCamelCase__    :   Union[str, Any]				=				ids_tensor([self.batch_size, self.seq_length]						,						self.type_vocab_size  )

           UpperCamelCase__    :   List[str]				=				AlbertConfig(
               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=__SCREAMING_SNAKE_CASE						,						initializer_range=self.initializer_range						,						)

           return config, input_ids, token_type_ids, attention_mask






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   str				=				self.prepare_config_and_inputs()
           UpperCamelCase__    :   str				=				config_and_inputs
           UpperCamelCase__    :   str				=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict




@require_flax
class 				_lowerCamelCase (      UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (
         (
             FlaxAlbertModel,
             FlaxAlbertForPreTraining,
             FlaxAlbertForMaskedLM,
             FlaxAlbertForMultipleChoice,
             FlaxAlbertForQuestionAnswering,
             FlaxAlbertForSequenceClassification,
             FlaxAlbertForTokenClassification,
             FlaxAlbertForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]				=				FlaxAlbertModelTester(self  )






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           for model_class_name in self.all_model_classes:
                 UpperCamelCase__    :   str				=				model_class_name.from_pretrained('''albert-base-v2'''  )
                 UpperCamelCase__    :   int				=				model(np.ones((1, 1)  )  )
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )




@require_flax
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]				=				FlaxAlbertModel.from_pretrained('''albert-base-v2'''  )
           UpperCamelCase__    :   Any				=				np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]  )
           UpperCamelCase__    :   str				=				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
           UpperCamelCase__    :   List[str]				=				model(__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   Tuple				=				(1, 1_1, 7_6_8)
           self.assertEqual(output.shape						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple				=				np.array(
               [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]  )

           self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						,						__SCREAMING_SNAKE_CASE						,						atol=1e-4  )  )




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase      =False
lowerCamelCase      =True
lowerCamelCase      =False


if __name__ == "__main__":
    lowerCamelCase      =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      =parser.parse_args()

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

    lowerCamelCase      ="" 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      =reader.read()
        lowerCamelCase      =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      =UNetaDModel(**config)
    else:
        lowerCamelCase      =UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
        lowerCamelCase      =class_name(**config)

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

    lowerCamelCase      =dict(model.config)

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

        lowerCamelCase      =[k.replace("UNetRes", "") for k in config["down_block_types"]]
        lowerCamelCase      =[k.replace("UNetRes", "") for k in config["up_block_types"]]

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

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

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


