





'''simple docstring'''






import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							[tensor.shape for tensor in tensor_list]
      return all(shape == shapes[0] for shape in shapes[1:]  )




class 			UpperCAmelCase					(      snake_case_							,		snake_case_							,		snake_case_							,		unittest.TestCase       ):
      _lowercase:      Dict											=       StableDiffusionLatentUpscalePipeline
      _lowercase:      Dict											=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          '''height''',
          '''width''',
          '''cross_attention_kwargs''',
          '''negative_prompt_embeds''',
          '''prompt_embeds''',
      }
      _lowercase:      Any											=       PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
      _lowercase:      Tuple											=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      _lowercase:      Union[str, Any]											=       frozenset(
          []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      _lowercase:      Optional[int]											=       frozenset([]       )

      _lowercase:      Optional[Any]											=       True




      @property
      def     lowercase__   (      self      :			Optional[int]  )    ->				Optional[Any]:
            _lowerCAmelCase												=							1
            _lowerCAmelCase												=							4
            _lowerCAmelCase												=							(16, 16)

            _lowerCAmelCase												=							floats_tensor((batch_size, num_channels) + sizes      ,      rng=random.Random(0  )  ).to(__snake_case  )
            return image




      def     lowercase__   (      self      :			str  )    ->				Optional[Any]:
            torch.manual_seed(0  )
            _lowerCAmelCase												=							UNetaDConditionModel(
                act_fn="""gelu"""      ,      attention_head_dim=8      ,      norm_num_groups=__snake_case      ,      block_out_channels=[32, 32, 64, 64]      ,      time_cond_proj_dim=1_60      ,      conv_in_kernel=1      ,      conv_out_kernel=1      ,      cross_attention_dim=32      ,      down_block_types=(
                    """KDownBlock2D""",
                    """KCrossAttnDownBlock2D""",
                    """KCrossAttnDownBlock2D""",
                    """KCrossAttnDownBlock2D""",
                )      ,      in_channels=8      ,      mid_block_type=__snake_case      ,      only_cross_attention=__snake_case      ,      out_channels=5      ,      resnet_time_scale_shift="""scale_shift"""      ,      time_embedding_type="""fourier"""      ,      timestep_post_act="""gelu"""      ,      up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")      ,      )
            _lowerCAmelCase												=							AutoencoderKL(
                block_out_channels=[32, 32, 64, 64]      ,      in_channels=3      ,      out_channels=3      ,      down_block_types=[
                    """DownEncoderBlock2D""",
                    """DownEncoderBlock2D""",
                    """DownEncoderBlock2D""",
                    """DownEncoderBlock2D""",
                ]      ,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,      latent_channels=4      ,      )
            _lowerCAmelCase												=							EulerDiscreteScheduler(prediction_type="""sample"""  )
            _lowerCAmelCase												=							CLIPTextConfig(
                bos_token_id=0      ,      eos_token_id=2      ,      hidden_size=32      ,      intermediate_size=37      ,      layer_norm_eps=1E-0_5      ,      num_attention_heads=4      ,      num_hidden_layers=5      ,      pad_token_id=1      ,      vocab_size=10_00      ,      hidden_act="""quick_gelu"""      ,      projection_dim=5_12      ,      )
            _lowerCAmelCase												=							CLIPTextModel(__snake_case  )
            _lowerCAmelCase												=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

            _lowerCAmelCase												=							{
                """unet""": model.eval(),
                """vae""": vae.eval(),
                """scheduler""": scheduler,
                """text_encoder""": text_encoder,
                """tokenizer""": tokenizer,
            }

            return components




      def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			List[str]=0  )    ->				List[Any]:
            if str(__snake_case  ).startswith("""mps"""  ):
                  _lowerCAmelCase												=							torch.manual_seed(__snake_case  )
            else:
                  _lowerCAmelCase												=							torch.Generator(device=__snake_case  ).manual_seed(__snake_case  )
            _lowerCAmelCase												=							{
                """prompt""": """A painting of a squirrel eating a burger""",
                """image""": self.dummy_image.cpu(),
                """generator""": generator,
                """num_inference_steps""": 2,
                """output_type""": """numpy""",
            }
            return inputs




      def     lowercase__   (      self      :			List[str]  )    ->				List[Any]:
            _lowerCAmelCase												=							"""cpu"""

            _lowerCAmelCase												=							self.get_dummy_components()
            _lowerCAmelCase												=							self.pipeline_class(**__snake_case  )
            pipe.to(__snake_case  )
            pipe.set_progress_bar_config(disable=__snake_case  )

            _lowerCAmelCase												=							self.get_dummy_inputs(__snake_case  )
            _lowerCAmelCase												=							pipe(**__snake_case  ).images
            _lowerCAmelCase												=							image[0, -3:, -3:, -1]

            self.assertEqual(image.shape      ,      (1, 2_56, 2_56, 3)  )
            _lowerCAmelCase												=							np.array(
                [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]  )
            _lowerCAmelCase												=							np.abs(image_slice.flatten() - expected_slice  ).max()
            self.assertLessEqual(__snake_case      ,      1E-3  )




      def     lowercase__   (      self      :			str  )    ->				Any:
            super().test_attention_slicing_forward_pass(expected_max_diff=7E-3  )




      def     lowercase__   (      self      :			List[Any]  )    ->				str:
            super().test_cpu_offload_forward_pass(expected_max_diff=3E-3  )




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Union[str, Any]:
            super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3  )




      def     lowercase__   (      self      :			int  )    ->				Optional[Any]:
            super().test_inference_batch_single_identical(expected_max_diff=7E-3  )




      def     lowercase__   (      self      :			Tuple  )    ->				Any:
            super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3  )




      def     lowercase__   (      self      :			List[str]  )    ->				str:
            super().test_save_load_local(expected_max_difference=3E-3  )




      def     lowercase__   (      self      :			List[str]  )    ->				Any:
            super().test_save_load_optional_components(expected_max_difference=3E-3  )




      def     lowercase__   (      self      :			Optional[int]  )    ->				str:
            _lowerCAmelCase												=							[
                """DDIMScheduler""",
                """DDPMScheduler""",
                """PNDMScheduler""",
                """HeunDiscreteScheduler""",
                """EulerAncestralDiscreteScheduler""",
                """KDPM2DiscreteScheduler""",
                """KDPM2AncestralDiscreteScheduler""",
                """DPMSolverSDEScheduler""",
            ]
            _lowerCAmelCase												=							self.get_dummy_components()
            _lowerCAmelCase												=							self.pipeline_class(**__snake_case  )

            # make sure that PNDM does not need warm-up
            pipe.scheduler.register_to_config(skip_prk_steps=__snake_case  )

            pipe.to(__snake_case  )
            pipe.set_progress_bar_config(disable=__snake_case  )
            _lowerCAmelCase												=							self.get_dummy_inputs(__snake_case  )
            _lowerCAmelCase												=							2

            _lowerCAmelCase												=							[]
            for scheduler_enum in KarrasDiffusionSchedulers:
                  if scheduler_enum.name in skip_schedulers:
                        # no sigma schedulers are not supported
                        # no schedulers
                        continue

                  _lowerCAmelCase												=							getattr(__snake_case      ,      scheduler_enum.name  )
                  _lowerCAmelCase												=							scheduler_cls.from_config(pipe.scheduler.config  )
                  _lowerCAmelCase												=							pipe(**__snake_case  )[0]
                  outputs.append(__snake_case  )

            assert check_same_shape(__snake_case  )




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




      def     lowercase__   (      self      :			Optional[int]  )    ->				int:
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()




      def     lowercase__   (      self      :			List[str]  )    ->				Optional[int]:
            _lowerCAmelCase												=							torch.manual_seed(33  )

            _lowerCAmelCase												=							StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""      ,      torch_dtype=torch.floataa  )
            pipe.to("""cuda"""  )

            _lowerCAmelCase												=							StableDiffusionLatentUpscalePipeline.from_pretrained(
                """stabilityai/sd-x2-latent-upscaler"""      ,      torch_dtype=torch.floataa  )
            upscaler.to("""cuda"""  )

            _lowerCAmelCase												=							"""a photo of an astronaut high resolution, unreal engine, ultra realistic"""

            _lowerCAmelCase												=							pipe(__snake_case      ,      generator=__snake_case      ,      output_type="""latent"""  ).images

            _lowerCAmelCase												=							upscaler(
                prompt=__snake_case      ,      image=__snake_case      ,      num_inference_steps=20      ,      guidance_scale=0      ,      generator=__snake_case      ,      output_type="""np"""      ,      ).images[0]

            _lowerCAmelCase												=							load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"""  )
            assert np.abs((expected_image - image).mean()  ) < 5E-2




      def     lowercase__   (      self      :			Optional[int]  )    ->				List[Any]:
            _lowerCAmelCase												=							torch.manual_seed(33  )

            _lowerCAmelCase												=							StableDiffusionLatentUpscalePipeline.from_pretrained(
                """stabilityai/sd-x2-latent-upscaler"""      ,      torch_dtype=torch.floataa  )
            upscaler.to("""cuda"""  )

            _lowerCAmelCase												=							"""the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

            _lowerCAmelCase												=							load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"""  )

            _lowerCAmelCase												=							upscaler(
                prompt=__snake_case      ,      image=__snake_case      ,      num_inference_steps=20      ,      guidance_scale=0      ,      generator=__snake_case      ,      output_type="""np"""      ,      ).images[0]

            _lowerCAmelCase												=							load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"""  )
            assert np.abs((expected_image - image).max()  ) < 5E-2













'''simple docstring'''






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        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      def get_masked_lm_array(lowerCAmelCase  ):
            _lowerCAmelCase												=							f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            # Self-attention
            _lowerCAmelCase												=							layer.attention.self

            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_query_dense/kernel"""							,     self_attn.query.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_query_dense/bias"""							,     self_attn.query.bias.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_key_dense/kernel"""							,     self_attn.key.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_key_dense/bias"""							,     self_attn.key.bias.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_value_dense/kernel"""							,     self_attn.value.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_value_dense/bias"""							,     self_attn.value.bias.data.shape  )

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

            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_output_dense/kernel"""							,     self_output.dense.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_output_dense/bias"""							,     self_output.dense.bias.data.shape  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/beta"""  )

            # Intermediate
            _lowerCAmelCase												=							layer.intermediate

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/bias"""  )

            # Output
            _lowerCAmelCase												=							layer.output

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/bias"""  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/beta"""  )

      # Embeddings
      _lowerCAmelCase												=							get_encoder_array("""_position_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_type_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/gamma"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/beta"""  )

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

      _lowerCAmelCase												=							get_masked_lm_array("""dense/kernel"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""dense/bias"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/gamma"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/beta"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""embedding_table"""  )

      # Pooling
      _lowerCAmelCase												=							BertPooler(config=lowerCAmelCase  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/kernel"""  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/bias"""  )

      # Export final model
      model.save_pretrained(lowerCAmelCase  )

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

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


if __name__ == "__main__":
      A__    :       str              =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.''',
      )
      A__    :       Dict              =parser.parse_args()
      convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






