from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__snake_case						=			logging.get_logger(__name__)





def      lowerCAmelCase_       ( __lowerCAmelCase  )->     List[int]:






      '''simple docstring'''





      if isinstance(__lowerCAmelCase						,    np.ndarray  ):
            return list(tensor.shape  )

      UpperCAmelCase			:       Optional[int]			     =tf.shape(__lowerCAmelCase  )

      if tensor.shape == tf.TensorShape(__lowerCAmelCase  ):
            return dynamic

      UpperCAmelCase			:       Union[str, Any]			     =tensor.shape.as_list()

      return [dynamic[i] if s is None else s for i, s in enumerate(__lowerCAmelCase  )]





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase = None						,    __lowerCAmelCase = None  )->     tf.Tensor:






      '''simple docstring'''





      return tf.nn.softmax(logits=logits + 1e-9						,    axis=__lowerCAmelCase						,    name=__lowerCAmelCase  )





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase=1e-5						,    __lowerCAmelCase=-1  )->     Any:






      '''simple docstring'''





      if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(__lowerCAmelCase						,    __lowerCAmelCase  ):
            raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''  )

      # Get mean and variance on the axis to be normalized
      UpperCAmelCase     ,  UpperCAmelCase			:       Dict			     =tf.nn.moments(__lowerCAmelCase						,    axes=[axis]						,    keepdims=__lowerCAmelCase  )

      if axis != -1:
            # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
            # on every dimension except axis
            UpperCAmelCase			:       str			     =[1] * inputs.shape.rank
            UpperCAmelCase			:       Any			     =shape_list(__lowerCAmelCase  )[axis]
            UpperCAmelCase			:       str			     =tf.reshape(__lowerCAmelCase						,    __lowerCAmelCase  )
            UpperCAmelCase			:       Optional[Any]			     =tf.reshape(__lowerCAmelCase						,    __lowerCAmelCase  )

      # Compute layer normalization using the batch_normalization
      # function.
      UpperCAmelCase			:       Optional[Any]			     =tf.nn.batch_normalization(
          __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase						,    offset=__lowerCAmelCase						,    scale=__lowerCAmelCase						,    variance_epsilon=__lowerCAmelCase						,    )
      return outputs





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase=0						,    __lowerCAmelCase=-1  )->     List[Any]:






      '''simple docstring'''





      if end_dim < 0:
            end_dim += input.shape.rank
      if start_dim < 0:
            start_dim += input.shape.rank

      if start_dim == end_dim:
            return input

      UpperCAmelCase			:       Dict			     =tf.shape(__lowerCAmelCase  )
      UpperCAmelCase			:       Dict			     =tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]  )
      UpperCAmelCase			:       List[str]			     =tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,    axis=0  )
      return tf.reshape(__lowerCAmelCase						,    __lowerCAmelCase  )





def      lowerCAmelCase_       ( __lowerCAmelCase  )->     tf.Tensor:






      '''simple docstring'''





      if not isinstance(__lowerCAmelCase						,    tf.Tensor  ):
            UpperCAmelCase			:       Union[str, Any]			     =tf.convert_to_tensor(__lowerCAmelCase  )  # Catches stray NumPy inputs
      if encoder_attention_mask.shape.rank == 3:
            UpperCAmelCase			:       Tuple			     =encoder_attention_mask[:, None, :, :]
      if encoder_attention_mask.shape.rank == 2:
            UpperCAmelCase			:       int			     =encoder_attention_mask[:, None, None, :]
      # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
      # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
      # /transformer/transformer_layers.py#L270
      # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
      # encoder_extended_attention_mask.transpose(-1, -2))
      UpperCAmelCase			:       Union[str, Any]			     =(
          tf.cast(1						,    encoder_attention_mask.dtype  ) - encoder_extended_attention_mask
      ) * encoder_extended_attention_mask.dtype.min

      return encoder_extended_attention_mask





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase = "input_ids"  )->     None:






      '''simple docstring'''





      tf.debugging.assert_less(
          __lowerCAmelCase						,    tf.cast(__lowerCAmelCase						,    dtype=tensor.dtype  )						,    message=(
              f'''The maximum value of {tensor_name} ({tf.math.reduce_max(__lowerCAmelCase  )}) must be smaller than the embedding '''
              f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
          )						,    )





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase  )->     str:






      '''simple docstring'''





      UpperCAmelCase			:       List[str]			     =6_45_12
      # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
      # because in that case even chunking the array would not make the saving
      # possible.
      UpperCAmelCase			:       str			     =[x for x in data if len(__lowerCAmelCase  ) > HDF5_OBJECT_HEADER_LIMIT]

      # Expecting this to never be true.
      if bad_attributes:
            raise RuntimeError(
                '''The following attributes cannot be saved to HDF5 file because '''
                f'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
                f'''bytes: {bad_attributes}'''  )

      UpperCAmelCase			:       Any			     =np.asarray(__lowerCAmelCase  )

      UpperCAmelCase			:       List[str]			     =1
      UpperCAmelCase			:       Dict			     =np.array_split(__lowerCAmelCase						,    __lowerCAmelCase  )

      # This will never loop forever thanks to the test above.
      while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data  ):
            num_chunks += 1
            UpperCAmelCase			:       List[str]			     =np.array_split(__lowerCAmelCase						,    __lowerCAmelCase  )

      if num_chunks > 1:
            for chunk_id, chunk_data in enumerate(__lowerCAmelCase  ):
                  UpperCAmelCase			:       Tuple			     =chunk_data
      else:
            UpperCAmelCase			:       Optional[int]			     =data





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     Optional[int]:






      '''simple docstring'''





      if name in group.attrs:
            UpperCAmelCase			:       Tuple			     =[n.decode('''utf8'''  ) if hasattr(__lowerCAmelCase						,    '''decode'''  ) else n for n in group.attrs[name]]
      else:
            UpperCAmelCase			:       Any			     =[]
            UpperCAmelCase			:       Optional[Any]			     =0
            while "%s%d" % (name, chunk_id) in group.attrs:
                  data.extend(
                      [n.decode('''utf8'''  ) if hasattr(__lowerCAmelCase						,    '''decode'''  ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]  )
                  chunk_id += 1
      return data





def      lowerCAmelCase_       ( __lowerCAmelCase  )->     str:






      '''simple docstring'''





      def _expand_single_ad_tensor(__lowerCAmelCase  ):
            if isinstance(__lowerCAmelCase						,    tf.Tensor  ) and t.shape.rank == 1:
                  return tf.expand_dims(__lowerCAmelCase						,    axis=-1  )
            return t

      return tf.nest.map_structure(_expand_single_ad_tensor						,    __lowerCAmelCase  )








import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  ShapEPipeline
      __snake_case       =  ['''prompt''']
      __snake_case       =  ['''prompt''']
      __snake_case       =  [
          '''num_images_per_prompt''',
          '''num_inference_steps''',
          '''generator''',
          '''latents''',
          '''guidance_scale''',
          '''frame_size''',
          '''output_type''',
          '''return_dict''',
      ]
      __snake_case       =  False







      @property
      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Union[str, Any]:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->List[str]:




       """simple docstring"""


       return 32







      @property
      def       __lowerCAmelCase					(      self      :						Any  )    ->Tuple:




       """simple docstring"""


       return self.time_input_dim * 4







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Optional[Any]:




       """simple docstring"""


       return 8







      @property
      def       __lowerCAmelCase					(      self      :						Tuple  )    ->str:




       """simple docstring"""


       a		  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )
       return tokenizer







      @property
      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->List[Any]:




       """simple docstring"""


       torch.manual_seed(0  )
       a		  =      CLIPTextConfig(
           bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=self.text_embedder_hidden_size ,					projection_dim=self.text_embedder_hidden_size ,					intermediate_size=37 ,					layer_norm_eps=1e-0_5 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1_000 ,					)
       return CLIPTextModelWithProjection(__UpperCAmelCase  )







      @property
      def       __lowerCAmelCase					(      self      :						Dict  )    ->Union[str, Any]:




       """simple docstring"""


       torch.manual_seed(0  )

       a		  =      {
           '''num_attention_heads''': 2,
           '''attention_head_dim''': 16,
           '''embedding_dim''': self.time_input_dim,
           '''num_embeddings''': 32,
           '''embedding_proj_dim''': self.text_embedder_hidden_size,
           '''time_embed_dim''': self.time_embed_dim,
           '''num_layers''': 1,
           '''clip_embed_dim''': self.time_input_dim * 2,
           '''additional_embeddings''': 0,
           '''time_embed_act_fn''': '''gelu''',
           '''norm_in_type''': '''layer''',
           '''encoder_hid_proj_type''': None,
           '''added_emb_type''': None,
       }

       a		  =      PriorTransformer(**__UpperCAmelCase  )
       return model







      @property
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       torch.manual_seed(0  )

       a		  =      {
           '''param_shapes''': (
               (self.renderer_dim, 93),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
               (self.renderer_dim, 8),
           ),
           '''d_latent''': self.time_input_dim,
           '''d_hidden''': self.renderer_dim,
           '''n_output''': 12,
           '''background''': (
               0.1,
               0.1,
               0.1,
           ),
       }
       a		  =      ShapERenderer(**__UpperCAmelCase  )
       return model







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




       """simple docstring"""


       a		  =      self.dummy_prior
       a		  =      self.dummy_text_encoder
       a		  =      self.dummy_tokenizer
       a		  =      self.dummy_renderer

       a		  =      HeunDiscreteScheduler(
           beta_schedule='''exp''' ,					num_train_timesteps=1_024 ,					prediction_type='''sample''' ,					use_karras_sigmas=__UpperCAmelCase ,					clip_sample=__UpperCAmelCase ,					clip_sample_range=1.0 ,					)
       a		  =      {
           '''prior''': prior,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''renderer''': renderer,
           '''scheduler''': scheduler,
       }

       return components







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						Optional[Any] ,					__UpperCAmelCase      :						str=0  )    ->Optional[int]:




       """simple docstring"""


       if str(__UpperCAmelCase  ).startswith('''mps'''  ):
        a		  =      torch.manual_seed(__UpperCAmelCase  )
       else:
        a		  =      torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
       a		  =      {
           '''prompt''': '''horse''',
           '''generator''': generator,
           '''num_inference_steps''': 1,
           '''frame_size''': 32,
           '''output_type''': '''np''',
       }
       return inputs







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




       """simple docstring"""


       a		  =      '''cpu'''

       a		  =      self.get_dummy_components()

       a		  =      self.pipeline_class(**__UpperCAmelCase  )
       a		  =      pipe.to(__UpperCAmelCase  )

       pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

       a		  =      pipe(**self.get_dummy_inputs(__UpperCAmelCase  )  )
       a		  =      output.images[0]
       a		  =      image[0, -3:, -3:, -1]

       assert image.shape == (20, 32, 32, 3)

       a		  =      np.array(
           [
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
               0.00039216,
           ]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Optional[Any]:




       """simple docstring"""


       self._test_inference_batch_consistent(batch_sizes=[1, 2]  )







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Tuple:




       """simple docstring"""


       a		  =      torch_device == '''cpu'''
       a		  =      True

       self._test_inference_batch_single_identical(
           batch_size=2 ,					test_max_difference=__UpperCAmelCase ,					relax_max_difference=__UpperCAmelCase ,					)







      def       __lowerCAmelCase					(      self      :						str  )    ->Optional[int]:




       """simple docstring"""


       a		  =      self.get_dummy_components()
       a		  =      self.pipeline_class(**__UpperCAmelCase  )
       a		  =      pipe.to(__UpperCAmelCase  )
       pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

       a		  =      1
       a		  =      2

       a		  =      self.get_dummy_inputs(__UpperCAmelCase  )

       for key in inputs.keys():
        if key in self.batch_params:
         a		  =      batch_size * [inputs[key]]

       a		  =      pipe(**__UpperCAmelCase ,					num_images_per_prompt=__UpperCAmelCase  )[0]

       assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class 		lowercase_					(	unittest.TestCase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						int  )    ->Any:




       """simple docstring"""


       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







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




       """simple docstring"""


       a		  =      load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/shap_e/test_shap_e_np_out.npy'''  )
       a		  =      ShapEPipeline.from_pretrained('''openai/shap-e'''  )
       a		  =      pipe.to(__UpperCAmelCase  )
       pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

       a		  =      torch.Generator(device=__UpperCAmelCase  ).manual_seed(0  )

       a		  =      pipe(
           '''a shark''' ,					generator=__UpperCAmelCase ,					guidance_scale=15.0 ,					num_inference_steps=64 ,					frame_size=64 ,					output_type='''np''' ,					).images[0]

       assert images.shape == (20, 64, 64, 3)

       assert_mean_pixel_difference(__UpperCAmelCase ,					__UpperCAmelCase  )


