



"""simple docstring"""




import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
     from transformers import (
         FlaxBertModel,
         FlaxCLIPVisionModel,
         FlaxVisionTextDualEncoderModel,
         FlaxViTModel,
         VisionTextDualEncoderConfig,
         VisionTextDualEncoderProcessor,
     )
     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )


if is_torch_available():
     import torch

     from transformers import VisionTextDualEncoderModel

if is_vision_available():
     from PIL import Image


def      lowercase__   (	_UpperCAmelCase )   ->	List[str]:






      '''simple docstring'''


      if isinstance(__UpperCamelCase		,					collections.abc.Iterable ):
            return x
      return (x, x)


@require_flax
class      a__						:


   def 				lowercase (							self					:							Union[str, Any],   lowerCAmelCase					:							str,   lowerCAmelCase					:							List[str]	)			->				Dict:
         pass


   def 				lowercase (							self					:							int	)			->				int:
         pass


   def 				lowercase (							self					:							Optional[int]	)			->				Dict:
         pass


   def 				lowercase (							self					:							Any,   lowerCAmelCase					:							np.ndarray,   lowerCAmelCase					:							np.ndarray,   lowerCAmelCase					:							float	)			->				str:
         lowercase     :					Tuple 		=      np.abs((a - b)	).max()
         self.assertLessEqual(A_,   A_,   f'''Difference between torch and flax is {diff} (>= {tol}).'''	)


   def 				lowercase (							self					:							Dict,   lowerCAmelCase					:							List[str],   lowerCAmelCase					:							Tuple,   lowerCAmelCase					:							Optional[int],   lowerCAmelCase					:							List[str],   lowerCAmelCase					:							Optional[int]=None,   **lowerCAmelCase					:							Union[str, Any]	)			->				Tuple:
         lowercase     :					Optional[Any] 		=      VisionTextDualEncoderConfig.from_vision_text_configs(A_,   A_	)

         lowercase     :					str 		=      FlaxVisionTextDualEncoderModel(A_	)

         lowercase     :					Union[str, Any] 		=      model(input_ids=A_,   pixel_values=A_,   attention_mask=A_	)

         self.assertEqual(output['text_embeds'].shape,   (input_ids.shape[0], config.projection_dim)	)
         self.assertEqual(output['image_embeds'].shape,   (pixel_values.shape[0], config.projection_dim)	)


   def 				lowercase (							self					:							List[Any],   lowerCAmelCase					:							Union[str, Any],   lowerCAmelCase					:							Optional[int],   lowerCAmelCase					:							int,   lowerCAmelCase					:							Tuple,   lowerCAmelCase					:							str=None,   **lowerCAmelCase					:							Any	)			->				Tuple:
         lowercase     :					Optional[Any] 		=      self.get_vision_text_model(A_,   A_	)
         lowercase     :					Optional[int] 		=      {'''vision_model''': vision_model, '''text_model''': text_model}
         lowercase     :					Tuple 		=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_	)

         lowercase     :					Tuple 		=      model(input_ids=A_,   pixel_values=A_,   attention_mask=A_	)

         self.assertEqual(output['text_embeds'].shape,   (input_ids.shape[0], model.config.projection_dim)	)
         self.assertEqual(output['image_embeds'].shape,   (pixel_values.shape[0], model.config.projection_dim)	)


   def 				lowercase (							self					:							Optional[int],   lowerCAmelCase					:							Optional[int],   lowerCAmelCase					:							Tuple,   lowerCAmelCase					:							int,   lowerCAmelCase					:							Dict,   lowerCAmelCase					:							Union[str, Any]=None,   **lowerCAmelCase					:							Any	)			->				Union[str, Any]:
         lowercase     :					Optional[Any] 		=      self.get_vision_text_model(A_,   A_	)
         lowercase     :					List[Any] 		=      {'''vision_model''': vision_model, '''text_model''': text_model}
         lowercase     :					List[Any] 		=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_	)

         lowercase     :					Optional[Any] 		=      model(input_ids=A_,   pixel_values=A_,   attention_mask=A_	)
         lowercase     :					int 		=      output[0]

         with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(A_	)
               lowercase     :					str 		=      FlaxVisionTextDualEncoderModel.from_pretrained(A_	)

               lowercase     :					Optional[Any] 		=      model(input_ids=A_,   pixel_values=A_,   attention_mask=A_	)
               lowercase     :					Dict 		=      after_output[0]
               lowercase     :					List[Any] 		=      np.amax(np.abs(out_a - out_a	)	)
               self.assertLessEqual(A_,   1e-3	)


   def 				lowercase (							self					:							List[str],   lowerCAmelCase					:							int,   lowerCAmelCase					:							Optional[int],   lowerCAmelCase					:							List[str],   lowerCAmelCase					:							Optional[Any],   lowerCAmelCase					:							int=None,   **lowerCAmelCase					:							Any	)			->				str:
         lowercase     :					Union[str, Any] 		=      self.get_vision_text_model(A_,   A_	)
         lowercase     :					Dict 		=      {'''vision_model''': vision_model, '''text_model''': text_model}
         lowercase     :					Union[str, Any] 		=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**A_	)

         lowercase     :					int 		=      model(
             input_ids=A_,   pixel_values=A_,   attention_mask=A_,   output_attentions=A_	)

         lowercase     :					Optional[int] 		=      output.vision_model_output.attentions
         self.assertEqual(len(A_	),   vision_config.num_hidden_layers	)

         # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
         lowercase     :					Tuple 		=      to_atuple(vision_model.config.image_size	)
         lowercase     :					Any 		=      to_atuple(vision_model.config.patch_size	)
         lowercase     :					str 		=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         lowercase     :					Any 		=      num_patches + 1
         self.assertEqual(vision_attentions[0].shape[-3:],   (vision_config.num_attention_heads, seq_len, seq_len)	)

         lowercase     :					Optional[Any] 		=      output.text_model_output.attentions
         self.assertEqual(len(A_	),   text_config.num_hidden_layers	)

         self.assertEqual(
             text_attentions[0].shape[-3:],   (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),   )


   def 				lowercase (							self					:							int,   lowerCAmelCase					:							Any,   lowerCAmelCase					:							Tuple,   lowerCAmelCase					:							Tuple	)			->				Tuple:
         pt_model.to(A_	)
         pt_model.eval()

         # prepare inputs
         lowercase     :					int 		=      inputs_dict
         lowercase     :					Union[str, Any] 		=      {k: torch.tensor(v.tolist()	) for k, v in flax_inputs.items()}

         with torch.no_grad():
               lowercase     :					Any 		=      pt_model(**A_	).to_tuple()

         lowercase     :					Dict 		=      fx_model(**A_	).to_tuple()
         self.assertEqual(len(A_	),   len(A_	),   'Output lengths differ between Flax and PyTorch'	)
         for fx_output, pt_output in zip(fx_outputs[:4],   pt_outputs[:4]	):
               self.assert_almost_equals(A_,   pt_output.numpy(),   4e-2	)

         # PT -> Flax
         with tempfile.TemporaryDirectory() as tmpdirname:
               pt_model.save_pretrained(A_	)
               lowercase     :					List[Any] 		=      FlaxVisionTextDualEncoderModel.from_pretrained(A_,   from_pt=A_	)

         lowercase     :					Optional[Any] 		=      fx_model_loaded(**A_	).to_tuple()
         self.assertEqual(len(A_	),   len(A_	),   'Output lengths differ between Flax and PyTorch'	)
         for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4],   pt_outputs[:4]	):
               self.assert_almost_equals(A_,   pt_output.numpy(),   4e-2	)

         # Flax -> PT
         with tempfile.TemporaryDirectory() as tmpdirname:
               fx_model.save_pretrained(A_	)
               lowercase     :					Union[str, Any] 		=      VisionTextDualEncoderModel.from_pretrained(A_,   from_flax=A_	)

         pt_model_loaded.to(A_	)
         pt_model_loaded.eval()

         with torch.no_grad():
               lowercase     :					Dict 		=      pt_model_loaded(**A_	).to_tuple()

         self.assertEqual(len(A_	),   len(A_	),   'Output lengths differ between Flax and PyTorch'	)
         for fx_output, pt_output_loaded in zip(fx_outputs[:4],   pt_outputs_loaded[:4]	):
               self.assert_almost_equals(A_,   pt_output_loaded.numpy(),   4e-2	)


   def 				lowercase (							self					:							Tuple,   lowerCAmelCase					:							Tuple,   lowerCAmelCase					:							Tuple,   lowerCAmelCase					:							str	)			->				List[Any]:
         lowercase     :					Dict 		=      VisionTextDualEncoderConfig.from_vision_text_configs(A_,   A_	)

         lowercase     :					Any 		=      VisionTextDualEncoderModel(A_	)
         lowercase     :					int 		=      FlaxVisionTextDualEncoderModel(A_	)

         lowercase     :					Optional[Any] 		=      convert_pytorch_state_dict_to_flax(pt_model.state_dict(),   A_	)
         lowercase     :					int 		=      fx_state

         self.check_pt_flax_equivalence(A_,   A_,   A_	)


   def 				lowercase (							self					:							int,   lowerCAmelCase					:							List[str],   lowerCAmelCase					:							Any,   lowerCAmelCase					:							Optional[Any]	)			->				Dict:
         lowercase     :					Optional[int] 		=      VisionTextDualEncoderConfig.from_vision_text_configs(A_,   A_	)

         lowercase     :					Union[str, Any] 		=      VisionTextDualEncoderModel(A_	)
         lowercase     :					Dict 		=      FlaxVisionTextDualEncoderModel(A_	)

         lowercase     :					Optional[int] 		=      load_flax_weights_in_pytorch_model(A_,   fx_model.params	)

         self.check_pt_flax_equivalence(A_,   A_,   A_	)


   def 				lowercase (							self					:							int	)			->				Any:
         lowercase     :					int 		=      self.prepare_config_and_inputs()
         self.check_model_from_pretrained_configs(**A_	)


   def 				lowercase (							self					:							Optional[int]	)			->				int:
         lowercase     :					Optional[int] 		=      self.prepare_config_and_inputs()
         self.check_vision_text_dual_encoder_from_pretrained(**A_	)


   def 				lowercase (							self					:							str	)			->				Optional[Any]:
         lowercase     :					str 		=      self.prepare_config_and_inputs()
         self.check_save_load(**A_	)


   def 				lowercase (							self					:							List[Any]	)			->				int:
         lowercase     :					List[str] 		=      self.prepare_config_and_inputs()
         self.check_vision_text_output_attention(**A_	)


   @is_pt_flax_cross_test
   def 				lowercase (							self					:							Optional[Any]	)			->				Any:
         lowercase     :					List[Any] 		=      self.prepare_config_and_inputs()
         lowercase     :					str 		=      config_inputs_dict.pop('vision_config'	)
         lowercase     :					Optional[Any] 		=      config_inputs_dict.pop('text_config'	)

         lowercase     :					Tuple 		=      config_inputs_dict

         self.check_equivalence_pt_to_flax(A_,   A_,   A_	)
         self.check_equivalence_flax_to_pt(A_,   A_,   A_	)






   @slow
   def 				lowercase (							self					:							Optional[int]	)			->				Optional[int]:
         lowercase     :					List[Any] 		=      self.get_pretrained_model_and_inputs()

         lowercase     :					Any 		=      model_a(**A_	)
         lowercase     :					int 		=      outputs[0]

         with tempfile.TemporaryDirectory() as tmp_dirname:
               model_a.save_pretrained(A_	)
               lowercase     :					int 		=      FlaxVisionTextDualEncoderModel.from_pretrained(A_	)

               lowercase     :					int 		=      model_a(**A_	)
               lowercase     :					Optional[int] 		=      after_outputs[0]
               lowercase     :					Optional[Any] 		=      np.amax(np.abs(out_a - out_a	)	)
               self.assertLessEqual(A_,   1e-5	)




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


   def 				lowercase (							self					:							int	)			->				Tuple:
         lowercase     :					int 		=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
             'hf-internal-testing/tiny-random-vit',   'hf-internal-testing/tiny-bert',   vision_from_pt=A_,   text_from_pt=A_,   )
         lowercase     :					int 		=      13
         lowercase     :					int 		=      floats_tensor(
             [
                 batch_size,
                 model.config.vision_config.num_channels,
                 model.config.vision_config.image_size,
                 model.config.vision_config.image_size,
             ]	)
         lowercase     :					int 		=      ids_tensor([batch_size, 4],   model.config.text_config.vocab_size	)
         lowercase     :					str 		=      random_attention_mask([batch_size, 4]	)
         lowercase     :					List[Any] 		=      {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

         return model, inputs


   def 				lowercase (							self					:							Optional[Any],   lowerCAmelCase					:							Dict,   lowerCAmelCase					:							List[Any]	)			->				List[str]:
         lowercase     :					str 		=      FlaxViTModel(A_	)
         lowercase     :					int 		=      FlaxBertModel(A_	)
         return vision_model, text_model






   def 				lowercase (							self					:							str	)			->				Optional[Any]:
         lowercase     :					int 		=      FlaxViTModelTester(self	)
         lowercase     :					Any 		=      FlaxBertModelTester(self	)
         lowercase     :					Any 		=      vit_model_tester.prepare_config_and_inputs()
         lowercase     :					int 		=      bert_model_tester.prepare_config_and_inputs()

         lowercase     :					Tuple 		=      vision_config_and_inputs

         lowercase     :					Optional[Any] 		=      text_config_and_inputs

         # make sure that cross attention layers are added
         return {
             "text_config": text_config,
             "vision_config": vision_config,
             "pixel_values": pixel_values,
             "attention_mask": attention_mask,
             "input_ids": input_ids,
             "token_type_ids": token_type_ids,
         }




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


   def 				lowercase (							self					:							Dict	)			->				Optional[Any]:
         lowercase     :					List[Any] 		=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
             'hf-internal-testing/tiny-random-clip',   'hf-internal-testing/tiny-bert',   vision_from_pt=A_,   text_from_pt=A_,   )
         lowercase     :					List[str] 		=      13
         lowercase     :					Union[str, Any] 		=      floats_tensor(
             [
                 batch_size,
                 model.config.vision_config.num_channels,
                 model.config.vision_config.image_size,
                 model.config.vision_config.image_size,
             ]	)
         lowercase     :					str 		=      ids_tensor([batch_size, 4],   model.config.text_config.vocab_size	)
         lowercase     :					int 		=      random_attention_mask([batch_size, 4]	)
         lowercase     :					Tuple 		=      {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

         return model, inputs


   def 				lowercase (							self					:							int,   lowerCAmelCase					:							List[Any],   lowerCAmelCase					:							str	)			->				Union[str, Any]:
         lowercase     :					List[str] 		=      FlaxCLIPVisionModel(A_	)
         lowercase     :					int 		=      FlaxBertModel(A_	)
         return vision_model, text_model






   def 				lowercase (							self					:							List[Any]	)			->				str:
         lowercase     :					List[str] 		=      FlaxCLIPVisionModelTester(self	)
         lowercase     :					Dict 		=      FlaxBertModelTester(self	)
         lowercase     :					Any 		=      clip_model_tester.prepare_config_and_inputs()
         lowercase     :					Any 		=      bert_model_tester.prepare_config_and_inputs()

         lowercase     :					Union[str, Any] 		=      vision_config_and_inputs

         lowercase     :					int 		=      text_config_and_inputs

         # make sure that cross attention layers are added
         return {
             "text_config": text_config,
             "vision_config": vision_config,
             "pixel_values": pixel_values,
             "attention_mask": attention_mask,
             "input_ids": input_ids,
             "token_type_ids": token_type_ids,
         }




@require_flax
@require_vision
class      a__						(  unittest.TestCase   ):


   @slow
   def 				lowercase (							self					:							Union[str, Any]	)			->				str:
         lowercase     :					int 		=      FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian',   logit_scale_init_value=1.0	)
         lowercase     :					str 		=      VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'	)

         lowercase     :					str 		=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
         lowercase     :					Optional[int] 		=      processor(
             text=['una foto di un gatto', 'una foto di un cane'],   images=A_,   padding=A_,   return_tensors='np'	)

         lowercase     :					Any 		=      model(**A_	)

         # verify the logits
         self.assertEqual(outputs.logits_per_image.shape,   (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])	)
         self.assertEqual(
             outputs.logits_per_text.shape,   (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),   )

         lowercase     :					Any 		=      np.array([[1.228_4727, 0.310_4122]]	)

         self.assertTrue(np.allclose(outputs.logits_per_image,   A_,   atol=1e-3	)	)





import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


A__  :  Union[str, Any]								=					{
    '''n_samples''': 64,
    '''horizon''': 32,
    '''num_inference_steps''': 20,
    '''n_guide_steps''': 2,  # can set to 0 for faster sampling, does not use value network
    '''scale_grad_by_std''': True,
    '''scale''': 0.1,
    '''eta''': 0.0,
    '''t_grad_cutoff''': 2,
    '''device''': '''cpu''',
}


if __name__ == "__main__":
						A__  :  Optional[int]								=					'''hopper-medium-v2'''
						A__  :  int								=					gym.make(env_name)

						A__  :  Optional[int]								=					ValueGuidedRLPipeline.from_pretrained(
						    '''bglick13/hopper-medium-v2-value-function-hor32''',
						    env=env,
						)

						env.seed(0)
						A__  :  int								=					env.reset()
						A__  :  Optional[int]								=					0
						A__  :  Union[str, Any]								=					0
						A__  :  Union[str, Any]								=					1000
						A__  :  Optional[Any]								=					[obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		A__  :  Union[str, Any]								=					pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		A__					,     A__					,     A__					,     A__  :  str								=					env.step(denorm_actions)
																		A__  :  Dict								=					env.get_normalized_score(total_reward)

																		# update return
																		total_reward += reward
																		total_score += score
																		print(
																		    F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
																		    F''' {total_score}'''
																		)

																		# save observations for rendering
																		rollout.append(next_observation.copy())

																		A__  :  List[str]								=					next_observation
						except KeyboardInterrupt:
												pass

						print(F'''Total reward: {total_reward}''')





