
import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase__       =False
lowercase__       =True
lowercase__       =False


if __name__ == "__main__":
      lowercase__       =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.')

      lowercase__       =parser.parse_args()

      lowercase__       ={
          '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',
      }

      lowercase__       ={
          'time_steps': 'time_proj',
          'mid': 'mid_block',
          'downsample_blocks': 'down_blocks',
          'upsample_blocks': 'up_blocks',
      }

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

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

      lowercase__       =dict(model.config)

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

            lowercase__       =[k.replace('UNetRes', '') for k in config['down_block_types']]
            lowercase__       =[k.replace('UNetRes', '') for k in config['up_block_types']]

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

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

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


import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
          MobileBertModel,
      )



class 		UpperCamelCase__  :
   def __init__(self :					List[Any]					,				snake_case_ :					int					,				snake_case_ :					List[str]=1_3					,				snake_case_ :					Tuple=7					,				snake_case_ :					List[Any]=True					,				snake_case_ :					List[Any]=True					,				snake_case_ :					Dict=True					,				snake_case_ :					Optional[int]=True					,				snake_case_ :					str=9_9					,				snake_case_ :					Dict=6_4					,				snake_case_ :					Any=3_2					,				snake_case_ :					str=5					,				snake_case_ :					int=4					,				snake_case_ :					List[Any]=3_7					,				snake_case_ :					Any="gelu"					,				snake_case_ :					Dict=0.1					,				snake_case_ :					List[str]=0.1					,				snake_case_ :					str=5_1_2					,				snake_case_ :					Any=1_6					,				snake_case_ :					str=2					,				snake_case_ :					int=0.02					,				snake_case_ :					Union[str, Any]=3					,				snake_case_ :					Optional[Any]=4					,				snake_case_ :					List[Any]=None					,				):
    __a :  Any						=  parent
    __a :  Optional[int]						=  batch_size
    __a :  Any						=  seq_length
    __a :  int						=  is_training
    __a :  Optional[int]						=  use_input_mask
    __a :  List[Any]						=  use_token_type_ids
    __a :  Dict						=  use_labels
    __a :  Tuple						=  vocab_size
    __a :  str						=  hidden_size
    __a :  List[Any]						=  embedding_size
    __a :  List[Any]						=  num_hidden_layers
    __a :  str						=  num_attention_heads
    __a :  str						=  intermediate_size
    __a :  Union[str, Any]						=  hidden_act
    __a :  Optional[Any]						=  hidden_dropout_prob
    __a :  Tuple						=  attention_probs_dropout_prob
    __a :  Union[str, Any]						=  max_position_embeddings
    __a :  Any						=  type_vocab_size
    __a :  int						=  type_sequence_label_size
    __a :  int						=  initializer_range
    __a :  int						=  num_labels
    __a :  Union[str, Any]						=  num_choices
    __a :  Dict						=  scope
   def       lowerCAmelCase			(self :					str   ):
    __a :  int						=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size   )

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

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

    __a :  Dict						=  None
    __a :  List[str]						=  None
    __a :  Optional[Any]						=  None
    if self.use_labels:
     __a :  str						=  ids_tensor([self.batch_size]					,				self.type_sequence_label_size   )
     __a :  Dict						=  ids_tensor([self.batch_size, self.seq_length]					,				self.num_labels   )
     __a :  Union[str, Any]						=  ids_tensor([self.batch_size]					,				self.num_choices   )

    __a :  Optional[int]						=  self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def       lowerCAmelCase			(self :					int   ):
    return MobileBertConfig(
        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					,				embedding_size=self.embedding_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					,				)
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					Tuple					,				snake_case_ :					List[str]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					Any					,				snake_case_ :					List[Any]					,				snake_case_ :					Any   ):
    __a :  Any						=  MobileBertModel(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  List[str]						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_   )
    __a :  Optional[Any]						=  model(snake_case_					,				token_type_ids=snake_case_   )
    __a :  Optional[Any]						=  model(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       lowerCAmelCase			(self :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Tuple					,				snake_case_ :					Dict					,				snake_case_ :					Tuple					,				snake_case_ :					str					,				snake_case_ :					List[Any]   ):
    __a :  str						=  MobileBertForMaskedLM(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Tuple						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_   )
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)   )
   def       lowerCAmelCase			(self :					Tuple					,				snake_case_ :					Any					,				snake_case_ :					Dict					,				snake_case_ :					List[Any]					,				snake_case_ :					Any					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Optional[int]					,				snake_case_ :					Dict   ):
    __a :  Optional[Any]						=  MobileBertForNextSentencePrediction(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  int						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_					,				)
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, 2)   )
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Optional[int]					,				snake_case_ :					Any					,				snake_case_ :					Any					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Dict					,				snake_case_ :					Optional[Any]   ):
    __a :  str						=  MobileBertForPreTraining(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Union[str, Any]						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_					,				next_sentence_label=snake_case_					,				)
    self.parent.assertEqual(result.prediction_logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size)   )
    self.parent.assertEqual(result.seq_relationship_logits.shape					,				(self.batch_size, 2)   )
   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Tuple					,				snake_case_ :					Any					,				snake_case_ :					Dict					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					str					,				snake_case_ :					str   ):
    __a :  str						=  MobileBertForQuestionAnswering(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Optional[Any]						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				start_positions=snake_case_					,				end_positions=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       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					Any					,				snake_case_ :					List[str]					,				snake_case_ :					List[Any]					,				snake_case_ :					str					,				snake_case_ :					Any					,				snake_case_ :					Tuple					,				snake_case_ :					Optional[int]   ):
    __a :  Any						=  self.num_labels
    __a :  Union[str, Any]						=  MobileBertForSequenceClassification(snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Tuple						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_   )
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels)   )
   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					Optional[int]					,				snake_case_ :					str					,				snake_case_ :					Dict					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Dict					,				snake_case_ :					Optional[int]   ):
    __a :  Union[str, Any]						=  self.num_labels
    __a :  str						=  MobileBertForTokenClassification(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Any						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_   )
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.num_labels)   )
   def       lowerCAmelCase			(self :					Union[str, Any]					,				snake_case_ :					List[str]					,				snake_case_ :					Any					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Union[str, Any]   ):
    __a :  Union[str, Any]						=  self.num_choices
    __a :  List[str]						=  MobileBertForMultipleChoice(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  List[Any]						=  input_ids.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  int						=  token_type_ids.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  str						=  input_mask.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  Any						=  model(
        snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_					,				labels=snake_case_					,				)
    self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_choices)   )







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





@require_torch
class 		UpperCamelCase__  (       __lowercase    ,__lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							str	       =      (
       (
           MobileBertModel,
           MobileBertForMaskedLM,
           MobileBertForMultipleChoice,
           MobileBertForNextSentencePrediction,
           MobileBertForPreTraining,
           MobileBertForQuestionAnswering,
           MobileBertForSequenceClassification,
           MobileBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _SCREAMING_SNAKE_CASE      :							Any	       =      (
       {
           "feature-extraction": MobileBertModel,
           "fill-mask": MobileBertForMaskedLM,
           "question-answering": MobileBertForQuestionAnswering,
           "text-classification": MobileBertForSequenceClassification,
           "token-classification": MobileBertForTokenClassification,
           "zero-shot": MobileBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      True
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					Tuple					,				snake_case_ :					List[Any]					,				snake_case_ :					Union[str, Any]=False   ):
    __a :  List[str]						=  super()._prepare_for_class(snake_case_					,				snake_case_					,				return_labels=snake_case_   )

    if return_labels:
     if model_class in get_values(snake_case_   ):
      __a :  Optional[int]						=  torch.zeros(
          (self.model_tester.batch_size, self.model_tester.seq_length)					,				dtype=torch.long					,				device=snake_case_   )
      __a :  int						=  torch.zeros(
          self.model_tester.batch_size					,				dtype=torch.long					,				device=snake_case_   )
    return inputs_dict
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  List[Any]						=  MobileBertModelTester(self   )
    __a :  int						=  ConfigTester(self					,				config_class=snake_case_					,				hidden_size=3_7   )
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    self.config_tester.run_common_tests()
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Dict						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_model(*snake_case_   )
   def       lowerCAmelCase			(self :					str   ):
    __a :  Any						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_   )
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  str						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_   )
   def       lowerCAmelCase			(self :					Dict   ):
    __a :  Optional[Any]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  Tuple						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  int						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  Optional[int]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_   )







   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Optional[int]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_   )





def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 return torch.tensor(
     lowerCAmelCase__  ,   dtype=torch.long  ,   device=lowerCAmelCase__  ,   )


lowercase__       =1e-3



@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (       unittest.TestCase    ):
   @slow
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Dict						=  MobileBertModel.from_pretrained('''google/mobilebert-uncased'''   ).to(snake_case_   )
    __a :  Tuple						=  _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]   )
    with torch.no_grad():
     __a :  str						=  model(snake_case_   )[0]
    __a :  List[Any]						=  torch.Size((1, 9, 5_1_2)   )
    self.assertEqual(output.shape					,				snake_case_   )
    __a :  Union[str, Any]						=  torch.tensor(
        [
            [
                [-2.473_6526E07, 8.269_1656E04, 1.652_1838E05],
                [-5.754_1704E-01, 3.905_6022E00, 4.401_1507E00],
                [2.604_7359E00, 1.567_7652E00, -1.732_4188E-01],
            ]
        ]					,				device=snake_case_					,				)

    # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
    # ~1 difference, it's therefore not a good idea to measure using addition.
    # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
    # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
    __a :  List[str]						=  torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE   )
    __a :  Any						=  torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE   )

    self.assertTrue(lower_bound and upper_bound   )
