
import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        LiltForQuestionAnswering,
        LiltForSequenceClassification,
        LiltForTokenClassification,
        LiltModel,
    )
    from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST




class 				_lowerCamelCase :





     """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=2_4						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=6						,						__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=3						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=1_0_0_0						,						)  ->   List[str]:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







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

           UpperCamelCase__    :   Dict				=				ids_tensor([self.batch_size, self.seq_length, 4]						,						self.range_bbox  )
           # Ensure that bbox is legal
           for i in range(bbox.shape[0]  ):
                 for j in range(bbox.shape[1]  ):
                       if bbox[i, j, 3] < bbox[i, j, 1]:
                             UpperCamelCase__    :   Dict				=				bbox[i, j, 3]
                             UpperCamelCase__    :   Any				=				bbox[i, j, 1]
                             UpperCamelCase__    :   Any				=				t
                       if bbox[i, j, 2] < bbox[i, j, 0]:
                             UpperCamelCase__    :   Optional[int]				=				bbox[i, j, 2]
                             UpperCamelCase__    :   Dict				=				bbox[i, j, 0]
                             UpperCamelCase__    :   str				=				t

           UpperCamelCase__    :   Dict				=				None
           if self.use_input_mask:
                 UpperCamelCase__    :   List[str]				=				ids_tensor([self.batch_size, self.seq_length]						,						vocab_size=2  )

           UpperCamelCase__    :   Optional[int]				=				None
           if self.use_token_type_ids:
                 UpperCamelCase__    :   List[str]				=				ids_tensor([self.batch_size, self.seq_length]						,						self.type_vocab_size  )

           UpperCamelCase__    :   Optional[int]				=				None
           UpperCamelCase__    :   Any				=				None
           if self.use_labels:
                 UpperCamelCase__    :   List[str]				=				ids_tensor([self.batch_size]						,						self.type_sequence_label_size  )
                 UpperCamelCase__    :   Any				=				ids_tensor([self.batch_size, self.seq_length]						,						self.num_labels  )

           UpperCamelCase__    :   List[str]				=				self.get_config()

           return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels






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




           """simple docstring"""







           return LiltConfig(
               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						,						initializer_range=self.initializer_range						,						)






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




           """simple docstring"""







           UpperCamelCase__    :   Dict				=				LiltModel(config=__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )
           model.eval()
           UpperCamelCase__    :   Dict				=				model(__SCREAMING_SNAKE_CASE						,						bbox=__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						token_type_ids=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Any				=				model(__SCREAMING_SNAKE_CASE						,						bbox=__SCREAMING_SNAKE_CASE						,						token_type_ids=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Any				=				model(__SCREAMING_SNAKE_CASE						,						bbox=__SCREAMING_SNAKE_CASE  )

           self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)  )
           self.parent.assertEqual(result.pooler_output.shape						,						(self.batch_size, self.hidden_size)  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						)  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]				=				self.num_labels
           UpperCamelCase__    :   int				=				LiltForTokenClassification(config=__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )
           model.eval()
           UpperCamelCase__    :   Any				=				model(
               __SCREAMING_SNAKE_CASE						,						bbox=__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						token_type_ids=__SCREAMING_SNAKE_CASE						,						labels=__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.seq_length, self.num_labels)  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						)  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Tuple				=				LiltForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )
           model.eval()
           UpperCamelCase__    :   Tuple				=				model(
               __SCREAMING_SNAKE_CASE						,						bbox=__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE						,						token_type_ids=__SCREAMING_SNAKE_CASE						,						start_positions=__SCREAMING_SNAKE_CASE						,						end_positions=__SCREAMING_SNAKE_CASE						,						)
           self.parent.assertEqual(result.start_logits.shape						,						(self.batch_size, self.seq_length)  )
           self.parent.assertEqual(result.end_logits.shape						,						(self.batch_size, self.seq_length)  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   int				=				self.prepare_config_and_inputs()
           (
               UpperCamelCase__
           )    :   Union[str, Any]				=				config_and_inputs
           UpperCamelCase__    :   Union[str, Any]				=				{
               '''input_ids''': input_ids,
               '''bbox''': bbox,
               '''token_type_ids''': token_type_ids,
               '''attention_mask''': input_mask,
           }
           return config, inputs_dict




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





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (
         (
             LiltModel,
             LiltForSequenceClassification,
             LiltForTokenClassification,
             LiltForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_		   =  (
         {
             '''feature-extraction''': LiltModel,
             '''question-answering''': LiltForQuestionAnswering,
             '''text-classification''': LiltForSequenceClassification,
             '''token-classification''': LiltForTokenClassification,
             '''zero-shot''': LiltForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False






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




           """simple docstring"""







           return True






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   int				=				LiltModelTester(self  )
           UpperCamelCase__    :   List[str]				=				ConfigTester(self						,						config_class=__SCREAMING_SNAKE_CASE						,						hidden_size=3_7  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           self.config_tester.run_common_tests()






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




           """simple docstring"""







           UpperCamelCase__    :   int				=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Any				=				self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 UpperCamelCase__    :   Any				=				type
                 self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]				=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Any				=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE  )






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__    :   Dict				=				LiltModel.from_pretrained(__SCREAMING_SNAKE_CASE  )
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )




@require_torch
@slow
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]				=				LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''  ).to(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[int]				=				torch.tensor([[1, 2]]						,						device=__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   int				=				torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]						,						device=__SCREAMING_SNAKE_CASE  )

           # forward pass
           with torch.no_grad():
                 UpperCamelCase__    :   str				=				model(input_ids=__SCREAMING_SNAKE_CASE						,						bbox=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[Any]				=				torch.Size([1, 2, 7_6_8]  )
           UpperCamelCase__    :   Dict				=				torch.tensor(
               [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]						,						device=__SCREAMING_SNAKE_CASE						,						)

           self.assertTrue(outputs.last_hidden_state.shape						,						__SCREAMING_SNAKE_CASE  )
           self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]						,						__SCREAMING_SNAKE_CASE						,						atol=1e-3  )  )




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))


