


'''simple docstring'''



from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
      from transformers import (
          TFAutoModel,
          TFAutoModelForCausalLM,
          TFAutoModelForMaskedLM,
          TFAutoModelForPreTraining,
          TFAutoModelForQuestionAnswering,
          TFAutoModelForSeqaSeqLM,
          TFAutoModelForSequenceClassification,
          TFAutoModelForTableQuestionAnswering,
          TFAutoModelForTokenClassification,
          TFAutoModelWithLMHead,
          TFBertForMaskedLM,
          TFBertForPreTraining,
          TFBertForQuestionAnswering,
          TFBertForSequenceClassification,
          TFBertModel,
          TFFunnelBaseModel,
          TFFunnelModel,
          TFGPTaLMHeadModel,
          TFRobertaForMaskedLM,
          TFTaForConditionalGeneration,
          TFTapasForQuestionAnswering,
      )
      from transformers.models.auto.modeling_tf_auto import (
          TF_MODEL_FOR_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_MASKED_LM_MAPPING,
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
          TF_MODEL_MAPPING,
      )
      from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
      from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST



class 			a       (	_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     "new-model"


if is_tf_available():



      class 			a       (	_a ):


           """simple docstring"""






           SCREAMING_SNAKE_CASE :				Optional[int]							     =     NewModelConfig



@require_tf
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     @slow
     def 				lowerCamelCase__				(       self  :	Dict    )		->							Dict:
          __UpperCAmelCase			:				Any													=    '''bert-base-cased'''
          __UpperCAmelCase			:				Union[str, Any]													=    AutoConfig.from_pretrained(snake_case    )
          self.assertIsNotNone(snake_case    )
          self.assertIsInstance(snake_case							,				snake_case    )

          __UpperCAmelCase			:				Optional[int]													=    TFAutoModel.from_pretrained(snake_case    )
          self.assertIsNotNone(snake_case    )
          self.assertIsInstance(snake_case							,				snake_case    )

     @slow
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							int:
          __UpperCAmelCase			:				List[str]													=    '''bert-base-cased'''
          __UpperCAmelCase			:				Dict													=    AutoConfig.from_pretrained(snake_case    )
          self.assertIsNotNone(snake_case    )
          self.assertIsInstance(snake_case							,				snake_case    )

          __UpperCAmelCase			:				Union[str, Any]													=    TFAutoModelForPreTraining.from_pretrained(snake_case    )
          self.assertIsNotNone(snake_case    )
          self.assertIsInstance(snake_case							,				snake_case    )

     @slow
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							List[Any]:
          for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase			:				int													=    AutoConfig.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

               __UpperCAmelCase			:				Any													=    TFAutoModelForCausalLM.from_pretrained(snake_case    )
               __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[Any]													=    TFAutoModelForCausalLM.from_pretrained(snake_case							,				output_loading_info=snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

     @slow
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Any:
          for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase			:				int													=    AutoConfig.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

               __UpperCAmelCase			:				Optional[Any]													=    TFAutoModelWithLMHead.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

     @slow
     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Union[str, Any]:
          for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase			:				Tuple													=    AutoConfig.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

               __UpperCAmelCase			:				Union[str, Any]													=    TFAutoModelForMaskedLM.from_pretrained(snake_case    )
               __UpperCAmelCase							,     __UpperCAmelCase			:				int													=    TFAutoModelForMaskedLM.from_pretrained(snake_case							,				output_loading_info=snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

     @slow
     def 				lowerCamelCase__				(       self  :	int    )		->							Dict:
          for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase			:				str													=    AutoConfig.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

               __UpperCAmelCase			:				int													=    TFAutoModelForSeqaSeqLM.from_pretrained(snake_case    )
               __UpperCAmelCase							,     __UpperCAmelCase			:				Tuple													=    TFAutoModelForSeqaSeqLM.from_pretrained(snake_case							,				output_loading_info=snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

     @slow
     def 				lowerCamelCase__				(       self  :	Dict    )		->							Optional[int]:
          # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          for model_name in ["bert-base-uncased"]:
               __UpperCAmelCase			:				Tuple													=    AutoConfig.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

               __UpperCAmelCase			:				List[str]													=    TFAutoModelForSequenceClassification.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

     @slow
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Any:
          # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          for model_name in ["bert-base-uncased"]:
               __UpperCAmelCase			:				Dict													=    AutoConfig.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

               __UpperCAmelCase			:				Dict													=    TFAutoModelForQuestionAnswering.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

     @slow
     @require_tensorflow_probability
     def 				lowerCamelCase__				(       self  :	str    )		->							List[Any]:
          for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
               __UpperCAmelCase			:				Union[str, Any]													=    AutoConfig.from_pretrained(snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

               __UpperCAmelCase			:				List[str]													=    TFAutoModelForTableQuestionAnswering.from_pretrained(snake_case    )
               __UpperCAmelCase							,     __UpperCAmelCase			:				Optional[Any]													=    TFAutoModelForTableQuestionAnswering.from_pretrained(
                   snake_case							,				output_loading_info=snake_case    )
               self.assertIsNotNone(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Dict:
          __UpperCAmelCase			:				int													=    TFAutoModelWithLMHead.from_pretrained(snake_case    )
          self.assertIsInstance(snake_case							,				snake_case    )
          self.assertEqual(model.num_parameters()							,				1_4410    )
          self.assertEqual(model.num_parameters(only_trainable=snake_case    )							,				1_4410    )

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Optional[int]:
          __UpperCAmelCase			:				Optional[Any]													=    TFAutoModelWithLMHead.from_pretrained(snake_case    )
          self.assertIsInstance(snake_case							,				snake_case    )
          self.assertEqual(model.num_parameters()							,				1_4410    )
          self.assertEqual(model.num_parameters(only_trainable=snake_case    )							,				1_4410    )

     def 				lowerCamelCase__				(       self  :	str    )		->							List[Any]:
          # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
          __UpperCAmelCase			:				Any													=    TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''    )
          self.assertIsInstance(snake_case							,				snake_case    )

          __UpperCAmelCase			:				Tuple													=    copy.deepcopy(model.config    )
          __UpperCAmelCase			:				List[Any]													=    ['''FunnelBaseModel''']
          __UpperCAmelCase			:				Optional[Any]													=    TFAutoModel.from_config(snake_case    )
          self.assertIsInstance(snake_case							,				snake_case    )

          with tempfile.TemporaryDirectory() as tmp_dir:
               model.save_pretrained(snake_case    )
               __UpperCAmelCase			:				Optional[Any]													=    TFAutoModel.from_pretrained(snake_case    )
               self.assertIsInstance(snake_case							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Any:
          try:
               AutoConfig.register('''new-model'''							,				snake_case    )

               __UpperCAmelCase			:				Tuple													=    [
                   TFAutoModel,
                   TFAutoModelForCausalLM,
                   TFAutoModelForMaskedLM,
                   TFAutoModelForPreTraining,
                   TFAutoModelForQuestionAnswering,
                   TFAutoModelForSequenceClassification,
                   TFAutoModelForTokenClassification,
               ]

               for auto_class in auto_classes:
                    with self.subTest(auto_class.__name__    ):
                         # Wrong config class will raise an error
                         with self.assertRaises(snake_case    ):
                              auto_class.register(snake_case							,				snake_case    )
                         auto_class.register(snake_case							,				snake_case    )
                         # Trying to register something existing in the Transformers library will raise an error
                         with self.assertRaises(snake_case    ):
                              auto_class.register(snake_case							,				snake_case    )

                         # Now that the config is registered, it can be used as any other config with the auto-API
                         __UpperCAmelCase			:				Optional[int]													=    BertModelTester(self    ).get_config()
                         __UpperCAmelCase			:				int													=    NewModelConfig(**tiny_config.to_dict()    )
                         __UpperCAmelCase			:				Any													=    auto_class.from_config(snake_case    )
                         self.assertIsInstance(snake_case							,				snake_case    )

                         with tempfile.TemporaryDirectory() as tmp_dir:
                              model.save_pretrained(snake_case    )
                              __UpperCAmelCase			:				Union[str, Any]													=    auto_class.from_pretrained(snake_case    )
                              self.assertIsInstance(snake_case							,				snake_case    )

          finally:
               if "new-model" in CONFIG_MAPPING._extra_content:
                    del CONFIG_MAPPING._extra_content["new-model"]
               for mapping in (
                   TF_MODEL_MAPPING,
                   TF_MODEL_FOR_PRETRAINING_MAPPING,
                   TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                   TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                   TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                   TF_MODEL_FOR_CAUSAL_LM_MAPPING,
                   TF_MODEL_FOR_MASKED_LM_MAPPING,
               ):
                    if NewModelConfig in mapping._extra_content:
                         del mapping._extra_content[NewModelConfig]

     def 				lowerCamelCase__				(       self  :	Dict    )		->							List[str]:
          with self.assertRaisesRegex(
              snake_case							,				'''bert-base is not a local folder and is not a valid model identifier'''    ):
               __UpperCAmelCase			:				Optional[int]													=    TFAutoModel.from_pretrained('''bert-base'''    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							str:
          with self.assertRaisesRegex(
              snake_case							,				r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''    ):
               __UpperCAmelCase			:				Dict													=    TFAutoModel.from_pretrained(snake_case							,				revision='''aaaaaa'''    )

     def 				lowerCamelCase__				(       self  :	int    )		->							List[str]:
          with self.assertRaisesRegex(
              snake_case							,				'''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''							,				):
               __UpperCAmelCase			:				Dict													=    TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							List[Any]:
          with self.assertRaisesRegex(snake_case							,				'''Use `from_pt=True` to load this model'''    ):
               __UpperCAmelCase			:				Dict													=    TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''    )






     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Dict:
          # Make sure we have cached the model.
          __UpperCAmelCase			:				Any													=    TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )
          with RequestCounter() as counter:
               __UpperCAmelCase			:				Any													=    TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )
               self.assertEqual(counter.get_request_count							,				0    )
               self.assertEqual(counter.head_request_count							,				1    )
               self.assertEqual(counter.other_request_count							,				0    )

          # With a sharded checkpoint
          __UpperCAmelCase			:				str													=    TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''    )
          with RequestCounter() as counter:
               __UpperCAmelCase			:				int													=    TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''    )
               self.assertEqual(counter.get_request_count							,				0    )
               self.assertEqual(counter.head_request_count							,				1    )
               self.assertEqual(counter.other_request_count							,				0    )



'''simple docstring'''



import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)

os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)




def 			_a			(		_lowercase      :			Any						):





     '''simple docstring'''






     if hor == 128:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Union[str, Any]													=    (32, 128, 256)
          __UpperCAmelCase			:				str													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

     elif hor == 32:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Any													=    (32, 64, 128, 256)
          __UpperCAmelCase			:				Optional[int]													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
     __UpperCAmelCase			:				Any													=    torch.load(F'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'						)
     __UpperCAmelCase			:				str													=    model.state_dict()
     __UpperCAmelCase			:				int													=    {
         '''down_block_types''': down_block_types,
         '''block_out_channels''': block_out_channels,
         '''up_block_types''': up_block_types,
         '''layers_per_block''': 1,
         '''use_timestep_embedding''': True,
         '''out_block_type''': '''OutConv1DBlock''',
         '''norm_num_groups''': 8,
         '''downsample_each_block''': False,
         '''in_channels''': 14,
         '''out_channels''': 14,
         '''extra_in_channels''': 0,
         '''time_embedding_type''': '''positional''',
         '''flip_sin_to_cos''': False,
         '''freq_shift''': 1,
         '''sample_size''': 65536,
         '''mid_block_type''': '''MidResTemporalBlock1D''',
         '''act_fn''': '''mish''',
     }
     __UpperCAmelCase			:				Optional[Any]													=    UNetaDModel(**_lowercase						)
     print(F'length of state dict: {len(state_dict.keys()						)}'						)
     print(F'length of value function dict: {len(hf_value_function.state_dict().keys()						)}'						)
     __UpperCAmelCase			:				Union[str, Any]													=    dict(zip(model.state_dict().keys()    ,      hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          __UpperCAmelCase			:				List[Any]													=    state_dict.pop(_lowercase						)
     hf_value_function.load_state_dict(_lowercase						)

     torch.save(hf_value_function.state_dict()    ,      F'hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'						)
     with open(F'hub/hopper-medium-v2/unet/hor{hor}/config.json'    ,      '''w'''						) as f:
          json.dump(_lowercase    ,      _lowercase						)




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    {
         '''in_channels''': 14,
         '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
         '''up_block_types''': (),
         '''out_block_type''': '''ValueFunction''',
         '''mid_block_type''': '''ValueFunctionMidBlock1D''',
         '''block_out_channels''': (32, 64, 128, 256),
         '''layers_per_block''': 1,
         '''downsample_each_block''': True,
         '''sample_size''': 65536,
         '''out_channels''': 14,
         '''extra_in_channels''': 0,
         '''time_embedding_type''': '''positional''',
         '''use_timestep_embedding''': True,
         '''flip_sin_to_cos''': False,
         '''freq_shift''': 1,
         '''norm_num_groups''': 8,
         '''act_fn''': '''mish''',
     }

     __UpperCAmelCase			:				str													=    torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''						)
     __UpperCAmelCase			:				Optional[Any]													=    model
     __UpperCAmelCase			:				Optional[int]													=    UNetaDModel(**_lowercase						)
     print(F'length of state dict: {len(state_dict.keys()						)}'						)
     print(F'length of value function dict: {len(hf_value_function.state_dict().keys()						)}'						)

     __UpperCAmelCase			:				Union[str, Any]													=    dict(zip(state_dict.keys()    ,      hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          __UpperCAmelCase			:				Dict													=    state_dict.pop(_lowercase						)

     hf_value_function.load_state_dict(_lowercase						)

     torch.save(hf_value_function.state_dict()    ,      '''hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'''						)
     with open('''hub/hopper-medium-v2/value_function/config.json'''    ,      '''w'''						) as f:
          json.dump(_lowercase    ,      _lowercase						)


if __name__ == "__main__":
      unet(3_2)
      # unet(128)
      value_function()