
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		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Tuple       					=ShapEPipeline
							lowercase_       :       List[Any]       					=['''prompt''']
							lowercase_       :       int       					=['''prompt''']
							lowercase_       :       Union[str, Any]       					=[
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       Optional[int]       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return self.time_input_dim * 4




							@property
							def  A__      (				self):
												return 8




							@property
							def  A__      (				self):
												lowercase							=						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
												return tokenizer




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						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=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,)
												return CLIPTextModelWithProjection(A__)




							@property
							def  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''num_attention_heads''': 2,
												    '''attention_head_dim''': 1_6,
												    '''embedding_dim''': self.time_input_dim,
												    '''num_embeddings''': 3_2,
												    '''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,
												}

												lowercase							=						PriorTransformer(**A__)
												return model




							@property
							def  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''param_shapes''': (
												        (self.renderer_dim, 9_3),
												        (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''': 1_2,
												    '''background''': (
												        0.1,
												        0.1,
												        0.1,
												    ),
												}
												lowercase							=						ShapERenderer(**A__)
												return model




							def  A__      (				self):
												lowercase							=						self.dummy_prior
												lowercase							=						self.dummy_text_encoder
												lowercase							=						self.dummy_tokenizer
												lowercase							=						self.dummy_renderer

												lowercase							=						HeunDiscreteScheduler(
												    beta_schedule='''exp'''   ,num_train_timesteps=1_0_2_4   ,prediction_type='''sample'''   ,use_karras_sigmas=A__   ,clip_sample=A__   ,clip_sample_range=1.0   ,)
												lowercase							=						{
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''frame_size''': 3_2,
												    '''output_type''': '''np''',
												}
												return inputs




							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

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

												assert image.shape == (2_0, 3_2, 3_2, 3)

												lowercase							=						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  A__      (				self):
												# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
												self._test_inference_batch_consistent(batch_sizes=[1, 2])




							def  A__      (				self):
												lowercase							=						torch_device == '''cpu'''
												lowercase							=						True

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





							def  A__      (				self):
												lowercase							=						self.get_dummy_components()
												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)
												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						1
												lowercase							=						2

												lowercase							=						self.get_dummy_inputs(A__)

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

												lowercase							=						pipe(**A__   ,num_images_per_prompt=A__)[0]

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







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




							def  A__      (				self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





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

												lowercase							=						torch.Generator(device=A__).manual_seed(0)

												lowercase							=						pipe(
												    '''a shark'''   ,generator=A__   ,guidance_scale=15.0   ,num_inference_steps=6_4   ,frame_size=6_4   ,output_type='''np'''   ,).images[0]

												assert images.shape == (2_0, 6_4, 6_4, 3)

												assert_mean_pixel_difference(A__   ,A__)






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						('''dense.weight''', '''attention.self.query''', '''attention.self.key''', '''attention.self.value''')

					lowercase							=						(
					    ('''layer.''', '''layer_'''),
					    ('''word_embeddings.weight''', '''word_embeddings'''),
					    ('''position_embeddings.weight''', '''position_embeddings'''),
					    ('''token_type_embeddings.weight''', '''token_type_embeddings'''),
					    ('''.''', '''/'''),
					    ('''LayerNorm/weight''', '''LayerNorm/gamma'''),
					    ('''LayerNorm/bias''', '''LayerNorm/beta'''),
					    ('''weight''', '''kernel'''),
					)

					if not os.path.isdir(lowerCAmelCase__		):
										os.makedirs(lowerCAmelCase__		)

					lowercase							=						model.state_dict()

					def to_tf_var_name(lowerCAmelCase__		):
										for patt, repl in iter(lowerCAmelCase__		):
															lowercase							=						name.replace(lowerCAmelCase__ ,			lowerCAmelCase__		)
										return f'bert/{name}'

					def create_tf_var(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
										lowercase							=						tf.dtypes.as_dtype(tensor.dtype		)
										lowercase							=						tf.get_variable(dtype=lowerCAmelCase__ ,			shape=tensor.shape ,			name=lowerCAmelCase__ ,			initializer=tf.zeros_initializer()		)
										session.run(tf.variables_initializer([tf_var]		)		)
										session.run(lowerCAmelCase__		)
										return tf_var

					tf.reset_default_graph()
					with tf.Session() as session:
										for var_name in state_dict:
															lowercase							=						to_tf_var_name(lowerCAmelCase__		)
															lowercase							=						state_dict[var_name].numpy()
															if any(x in var_name for x in tensors_to_transpose		):
																				lowercase							=						torch_tensor.T
															lowercase							=						create_tf_var(tensor=lowerCAmelCase__ ,			name=lowerCAmelCase__ ,			session=lowerCAmelCase__		)
															tf.keras.backend.set_value(lowerCAmelCase__ ,			lowerCAmelCase__		)
															lowercase							=						session.run(lowerCAmelCase__		)
															print(f'Successfully created {tf_name}: {np.allclose(lowerCAmelCase__ ,			lowerCAmelCase__		)}'		)

										lowercase							=						tf.train.Saver(tf.trainable_variables()		)
										saver.save(lowerCAmelCase__ ,			os.path.join(lowerCAmelCase__ ,			model_name.replace('''-''' ,			'''_'''		) + '''.ckpt'''		)		)



def        UpperCamelCase    (							lowerCAmelCase__=None		):
					'''simple docstring'''
					lowercase							=						argparse.ArgumentParser()
					parser.add_argument('''--model_name''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''model name e.g. bert-base-uncased'''		)
					parser.add_argument(
					    '''--cache_dir''' ,			type=lowerCAmelCase__ ,			default=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory containing pytorch model'''		)
					parser.add_argument('''--pytorch_model_path''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''/path/to/<pytorch-model-name>.bin'''		)
					parser.add_argument('''--tf_cache_dir''' ,			type=lowerCAmelCase__ ,			required=lowerCAmelCase__ ,			help='''Directory in which to save tensorflow model'''		)
					lowercase							=						parser.parse_args(lowerCAmelCase__		)

					lowercase							=						BertModel.from_pretrained(
					    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path		) ,			cache_dir=args.cache_dir ,			)

					convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase__ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name		)


if __name__ == "__main__":
							main()




