import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					def get_masked_lm_array(__snake_case						):
										_UpperCamelCase			     =     f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
										_UpperCamelCase			     =     tf.train.load_variable(__snake_case  ,     __snake_case						)

										if "kernel" in name:
															_UpperCamelCase			     =     array.transpose()

										return torch.from_numpy(__snake_case						)

					def get_encoder_array(__snake_case						):
										_UpperCamelCase			     =     f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
										_UpperCamelCase			     =     tf.train.load_variable(__snake_case  ,     __snake_case						)

										if "kernel" in name:
															_UpperCamelCase			     =     array.transpose()

										return torch.from_numpy(__snake_case						)

					def get_encoder_layer_array(__snake_case  ,     __snake_case						):
										_UpperCamelCase			     =     f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
										_UpperCamelCase			     =     tf.train.load_variable(__snake_case  ,     __snake_case						)

										if "kernel" in name:
															_UpperCamelCase			     =     array.transpose()

										return torch.from_numpy(__snake_case						)

					def get_encoder_attention_layer_array(__snake_case  ,     __snake_case  ,     __snake_case						):
										_UpperCamelCase			     =     f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
										_UpperCamelCase			     =     tf.train.load_variable(__snake_case  ,     __snake_case						)
										_UpperCamelCase			     =     array.reshape(__snake_case						)

										if "kernel" in name:
															_UpperCamelCase			     =     array.transpose()

										return torch.from_numpy(__snake_case						)

					print(f"""Loading model based on config from {config_path}..."""						)
					_UpperCamelCase			     =     BertConfig.from_json_file(__snake_case						)
					_UpperCamelCase			     =     BertForMaskedLM(__snake_case						)

					# Layers
					for layer_index in range(0  ,     config.num_hidden_layers						):
										_UpperCamelCase			     =     model.bert.encoder.layer[layer_index]

										# Self-attention
										_UpperCamelCase			     =     layer.attention.self

										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_query_dense/kernel'''  ,     self_attn.query.weight.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_query_dense/bias'''  ,     self_attn.query.bias.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_key_dense/kernel'''  ,     self_attn.key.weight.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_key_dense/bias'''  ,     self_attn.key.bias.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_value_dense/kernel'''  ,     self_attn.value.weight.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_value_dense/bias'''  ,     self_attn.value.bias.data.shape						)

										# Self-attention Output
										_UpperCamelCase			     =     layer.attention.output

										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_output_dense/kernel'''  ,     self_output.dense.weight.data.shape						)
										_UpperCamelCase			     =     get_encoder_attention_layer_array(
										    __snake_case  ,     '''_output_dense/bias'''  ,     self_output.dense.bias.data.shape						)

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_attention_layer_norm/gamma'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_attention_layer_norm/beta'''						)

										# Intermediate
										_UpperCamelCase			     =     layer.intermediate

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_intermediate_dense/kernel'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_intermediate_dense/bias'''						)

										# Output
										_UpperCamelCase			     =     layer.output

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_dense/kernel'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_dense/bias'''						)

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_layer_norm/gamma'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_layer_norm/beta'''						)

					# Embeddings
					_UpperCamelCase			     =     get_encoder_array('''_position_embedding_layer/embeddings'''						)
					_UpperCamelCase			     =     get_encoder_array('''_type_embedding_layer/embeddings'''						)
					_UpperCamelCase			     =     get_encoder_array('''_embedding_norm_layer/gamma'''						)
					_UpperCamelCase			     =     get_encoder_array('''_embedding_norm_layer/beta'''						)

					# LM Head
					_UpperCamelCase			     =     model.cls.predictions.transform

					_UpperCamelCase			     =     get_masked_lm_array('''dense/kernel'''						)
					_UpperCamelCase			     =     get_masked_lm_array('''dense/bias'''						)

					_UpperCamelCase			     =     get_masked_lm_array('''layer_norm/gamma'''						)
					_UpperCamelCase			     =     get_masked_lm_array('''layer_norm/beta'''						)

					_UpperCamelCase			     =     get_masked_lm_array('''embedding_table'''						)

					# Pooling
					_UpperCamelCase			     =     BertPooler(config=__snake_case						)
					_UpperCamelCase			     =     get_encoder_array('''_pooler_layer/kernel'''						)
					_UpperCamelCase			     =     get_encoder_array('''_pooler_layer/bias'''						)

					# Export final model
					model.save_pretrained(__snake_case						)

					# Integration test - should load without any errors ;)
					_UpperCamelCase			     =     BertForMaskedLM.from_pretrained(__snake_case						)
					print(new_model.eval()						)

					print('''Model conversion was done sucessfully!'''						)


if __name__ == "__main__":
					_lowerCAmelCase       =		argparse.ArgumentParser()
					parser.add_argument(
					    "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping checkpoint path."
					)
					parser.add_argument(
					    "--bert_config_file",
					    type=str,
					    required=True,
					    help="The config json file corresponding to the BERT model. This specifies the model architecture.",
					)
					parser.add_argument(
					    "--pytorch_dump_path",
					    type=str,
					    required=True,
					    help="Path to the output PyTorch model.",
					)
					_lowerCAmelCase       =		parser.parse_args()
					convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

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     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     ShapEPipeline
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     [
							    "num_images_per_prompt",
							    "num_inference_steps",
							    "generator",
							    "latents",
							    "guidance_scale",
							    "frame_size",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase				   =     False







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return 32







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return 32







							@property
							def  UpperCamelCase_						(   self      :     List[str]		):
												return self.time_input_dim * 4







							@property
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												return 8







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







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												torch.manual_seed(0		)
												_UpperCamelCase			     =     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-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=1000 ,			)
												return CLIPTextModelWithProjection(_A		)







							@property
							def  UpperCamelCase_						(   self      :     int		):
												torch.manual_seed(0		)

												_UpperCamelCase			     =     {
												    '''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,
												}

												_UpperCamelCase			     =     PriorTransformer(**_A		)
												return model







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												torch.manual_seed(0		)

												_UpperCamelCase			     =     {
												    '''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,
												    ),
												}
												_UpperCamelCase			     =     ShapERenderer(**_A		)
												return model







							def  UpperCamelCase_						(   self      :     str		):
												_UpperCamelCase			     =     self.dummy_prior
												_UpperCamelCase			     =     self.dummy_text_encoder
												_UpperCamelCase			     =     self.dummy_tokenizer
												_UpperCamelCase			     =     self.dummy_renderer

												_UpperCamelCase			     =     HeunDiscreteScheduler(
												    beta_schedule='''exp''' ,			num_train_timesteps=1024 ,			prediction_type='''sample''' ,			use_karras_sigmas=_A ,			clip_sample=_A ,			clip_sample_range=1.0 ,			)
												_UpperCamelCase			     =     {
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     Tuple ,			_A      :     Optional[int]=0		):
												if str(_A		).startswith('''mps'''		):
																	_UpperCamelCase			     =     torch.manual_seed(_A		)
												else:
																	_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(_A		)
												_UpperCamelCase			     =     {
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''frame_size''': 32,
												    '''output_type''': '''np''',
												}
												return inputs







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     '''cpu'''

												_UpperCamelCase			     =     self.get_dummy_components()

												_UpperCamelCase			     =     self.pipeline_class(**_A		)
												_UpperCamelCase			     =     pipe.to(_A		)

												pipe.set_progress_bar_config(disable=_A		)

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

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

												_UpperCamelCase			     =     np.array(
												    [
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												        0.0003_9216,
												    ]		)

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







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







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     torch_device == '''cpu'''
												_UpperCamelCase			     =     True

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







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     self.get_dummy_components()
												_UpperCamelCase			     =     self.pipeline_class(**_A		)
												_UpperCamelCase			     =     pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     1
												_UpperCamelCase			     =     2

												_UpperCamelCase			     =     self.get_dummy_inputs(_A		)

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

												_UpperCamelCase			     =     pipe(**_A ,			num_images_per_prompt=_A		)[0]

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




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







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







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/shap_e/test_shap_e_np_out.npy'''		)
												_UpperCamelCase			     =     ShapEPipeline.from_pretrained('''openai/shap-e'''		)
												_UpperCamelCase			     =     pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(0		)

												_UpperCamelCase			     =     pipe(
												    '''a shark''' ,			generator=_A ,			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(_A ,			_A		)
