



'''simple docstring'''






import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 							a__( __lowerCamelCase		,			__lowerCamelCase		,			unittest.TestCase     ):
    lowercase__			=   CycleDiffusionPipeline
    lowercase__			=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        """negative_prompt""",
        """height""",
        """width""",
        """negative_prompt_embeds""",
    }
    lowercase__			=   PipelineTesterMixin.required_optional_params - {"""latents"""}
    lowercase__			=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}     )
    lowercase__			=   IMAGE_TO_IMAGE_IMAGE_PARAMS
    lowercase__			=   IMAGE_TO_IMAGE_IMAGE_PARAMS







    def       lowercase_	(				self			:  Any     ):
      torch.manual_seed(0     )
      a						: Any					       =					UNetaDConditionModel(
          block_out_channels=(32, 64)						,      layers_per_block=2						,      sample_size=32						,      in_channels=4						,      out_channels=4						,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,      cross_attention_dim=32						,      )
      a						: List[str]					       =					DDIMScheduler(
          beta_start=0.00085						,      beta_end=0.012						,      beta_schedule='scaled_linear'						,      num_train_timesteps=10_00						,      clip_sample=__lowercase						,      set_alpha_to_one=__lowercase						,      )
      torch.manual_seed(0     )
      a						: int					       =					AutoencoderKL(
          block_out_channels=[32, 64]						,      in_channels=3						,      out_channels=3						,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,      latent_channels=4						,      )
      torch.manual_seed(0     )
      a						: Any					       =					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						,      )
      a						: Any					       =					CLIPTextModel(__lowercase     )
      a						: Optional[int]					       =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

      a						: Union[str, Any]					       =					{
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  Dict						,      __snake_case			:  str=0     ):
      a						: Dict					       =					floats_tensor((1, 3, 32, 32)						,      rng=random.Random(__lowercase     )     ).to(__lowercase     )
      a						: Optional[Any]					       =					image / 2 + 0.5
      if str(__lowercase     ).startswith('mps'     ):
        a						: List[Any]					       =					torch.manual_seed(__lowercase     )
      else:
        a						: List[Any]					       =					torch.Generator(device=__lowercase     ).manual_seed(__lowercase     )
      a						: Union[str, Any]					       =					{
          '''prompt''': '''An astronaut riding an elephant''',
          '''source_prompt''': '''An astronaut riding a horse''',
          '''image''': image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''eta''': 0.1,
          '''strength''': 0.8,
          '''guidance_scale''': 3,
          '''source_guidance_scale''': 1,
          '''output_type''': '''numpy''',
      }
      return inputs







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: List[str]					       =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

      a						: Dict					       =					self.get_dummy_components()
      a						: str					       =					CycleDiffusionPipeline(**__lowercase     )
      a						: Union[str, Any]					       =					pipe.to(__lowercase     )
      pipe.set_progress_bar_config(disable=__lowercase     )

      a						: Tuple					       =					self.get_dummy_inputs(__lowercase     )
      a						: Tuple					       =					pipe(**__lowercase     )
      a						: Union[str, Any]					       =					output.images

      a						: Optional[int]					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: Dict					       =					np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]     )

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







    @unittest.skipIf(torch_device != 'cuda'						,      'This test requires a GPU'     )
    def       lowercase_	(				self			:  List[Any]     ):
      a						: Dict					       =					self.get_dummy_components()
      for name, module in components.items():
        if hasattr(__lowercase						,      'half'     ):
          a						: Any					       =					module.half()
      a						: Union[str, Any]					       =					CycleDiffusionPipeline(**__lowercase     )
      a						: Union[str, Any]					       =					pipe.to(__lowercase     )
      pipe.set_progress_bar_config(disable=__lowercase     )

      a						: Tuple					       =					self.get_dummy_inputs(__lowercase     )
      a						: Optional[int]					       =					pipe(**__lowercase     )
      a						: Optional[Any]					       =					output.images

      a						: str					       =					images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      a						: List[str]					       =					np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]     )

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







    @skip_mps
    def       lowercase_	(				self			:  Tuple     ):
      return super().test_save_load_local()







    @unittest.skip('non-deterministic pipeline'     )
    def       lowercase_	(				self			:  Any     ):
      return super().test_inference_batch_single_identical()







    @skip_mps
    def       lowercase_	(				self			:  int     ):
      return super().test_dict_tuple_outputs_equivalent()







    @skip_mps
    def       lowercase_	(				self			:  Dict     ):
      return super().test_save_load_optional_components()







    @skip_mps
    def       lowercase_	(				self			:  Dict     ):
      return super().test_attention_slicing_forward_pass()

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







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







    def       lowercase_	(				self			:  int     ):
      a						: List[Any]					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: List[str]					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'     )
      a						: List[Any]					       =					init_image.resize((5_12, 5_12)     )

      a						: Union[str, Any]					       =					'''CompVis/stable-diffusion-v1-4'''
      a						: Union[str, Any]					       =					DDIMScheduler.from_pretrained(__lowercase						,      subfolder='scheduler'     )
      a						: Tuple					       =					CycleDiffusionPipeline.from_pretrained(
          __lowercase						,      scheduler=__lowercase						,      safety_checker=__lowercase						,      torch_dtype=torch.floataa						,      revision='fp16'     )

      pipe.to(__lowercase     )
      pipe.set_progress_bar_config(disable=__lowercase     )
      pipe.enable_attention_slicing()

      a						: int					       =					'''A black colored car'''
      a						: str					       =					'''A blue colored car'''

      a						: str					       =					torch.manual_seed(0     )
      a						: Optional[int]					       =					pipe(
          prompt=__lowercase						,      source_prompt=__lowercase						,      image=__lowercase						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__lowercase						,      output_type='np'						,      )
      a						: Dict					       =					output.images

      # the values aren't exactly equal, but the images look the same visually
      assert np.abs(image - expected_image     ).max() < 5e-1







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Optional[int]					       =					load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/cycle-diffusion/black_colored_car.png'     )
      a						: str					       =					load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'     )
      a						: Any					       =					init_image.resize((5_12, 5_12)     )

      a						: Union[str, Any]					       =					'''CompVis/stable-diffusion-v1-4'''
      a						: Optional[int]					       =					DDIMScheduler.from_pretrained(__lowercase						,      subfolder='scheduler'     )
      a						: List[str]					       =					CycleDiffusionPipeline.from_pretrained(__lowercase						,      scheduler=__lowercase						,      safety_checker=__lowercase     )

      pipe.to(__lowercase     )
      pipe.set_progress_bar_config(disable=__lowercase     )
      pipe.enable_attention_slicing()

      a						: Any					       =					'''A black colored car'''
      a						: Dict					       =					'''A blue colored car'''

      a						: str					       =					torch.manual_seed(0     )
      a						: Optional[Any]					       =					pipe(
          prompt=__lowercase						,      source_prompt=__lowercase						,      image=__lowercase						,      num_inference_steps=1_00						,      eta=0.1						,      strength=0.85						,      guidance_scale=3						,      source_guidance_scale=1						,      generator=__lowercase						,      output_type='np'						,      )
      a						: Optional[int]					       =					output.images

      assert np.abs(image - expected_image     ).max() < 2e-2




'''simple docstring'''






# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig
def 				lowerCamelCase__	(						_A			,      _A			,      _A=0    ):
  # Format the message.
  if name is None:
    a						: Tuple					       =					None
  else:
    a						: Dict					       =					'.' * max(0			,      spaces - 2    ) + '# {:' + str(50 - spaces    ) + 's}'
    a						: Tuple					       =					fmt.format(_A    )

  # Print and recurse (if needed).
  if isinstance(_A			,      _A    ):
    if msg is not None:
      print(_A    )
    for k in val.keys():
      recursive_print(_A			,      val[k]			,      spaces + 2    )
  elif isinstance(_A			,      torch.Tensor    ):
    print(_A			,      ':'			,      val.size()    )
  else:
    print(_A			,      ':'			,      _A    )
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A			,      _A    ):
  # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
  # for compatibility with later versions of NVIDIA Megatron-LM.
  # The inverse operation is performed inside Megatron-LM to read checkpoints:
  # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
  # If param is the weight tensor of the self-attention block, the returned tensor
  # will have to be transposed one more time to be read by HuggingFace GPT2.
  a						: str					       =					param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    a						: List[Any]					       =					(num_heads, hidden_size, num_splits) + input_shape[1:]
    a						: int					       =					param.view(*_A    )
    a						: List[str]					       =					param.transpose(0			,      2    )
    a						: Union[str, Any]					       =					param.transpose(1			,      2    ).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    a						: Union[str, Any]					       =					(num_heads, num_splits, hidden_size) + input_shape[1:]
    a						: List[str]					       =					param.view(*_A    )
    a						: Union[str, Any]					       =					param.transpose(0			,      1    ).contiguous()
  a						: List[Any]					       =					param.view(*_A    )
  return param
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  # The converted output model.
  a						: Optional[Any]					       =					{}

  # old versions did not store training args
  a						: Dict					       =					input_state_dict.get('args'			,      _A    )
  if ds_args is not None:
    # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
    # from pprint import pprint
    # pprint(vars(ds_args))

    a						: Union[str, Any]					       =					ds_args.padded_vocab_size
    a						: str					       =					ds_args.max_position_embeddings
    a						: Dict					       =					ds_args.hidden_size
    a						: Union[str, Any]					       =					ds_args.num_layers
    a						: Dict					       =					ds_args.num_attention_heads
    a						: int					       =					ds_args.ffn_hidden_size
    # pprint(config)

  # The number of heads.
  a						: Any					       =					config.n_head
  # The hidden_size per head.
  a						: Tuple					       =					config.n_embd // config.n_head
  # Megatron-LM checkpoint version
  if "checkpoint_version" in input_state_dict.keys():
    a						: Any					       =					input_state_dict['checkpoint_version']
  else:
    a						: Any					       =					0.0

  # The model.
  a						: Optional[int]					       =					input_state_dict['model']
  # The language model.
  a						: Optional[Any]					       =					model['language_model']
  # The embeddings.
  a						: List[str]					       =					lm['embedding']

  # The word embeddings.
  a						: List[Any]					       =					embeddings['word_embeddings']['weight']
  # Truncate the embedding table to vocab_size rows.
  a						: Dict					       =					word_embeddings[: config.vocab_size, :]
  a						: int					       =					word_embeddings

  # The position embeddings.
  a						: Tuple					       =					embeddings['position_embeddings']['weight']
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  a						: List[str]					       =					pos_embeddings.size(0    )
  if n_positions != config.n_positions:
    raise ValueError(
        f"""pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"""    )
  # Store the position embeddings.
  a						: Optional[Any]					       =					pos_embeddings

  # The transformer.
  a						: Union[str, Any]					       =					lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

  # The regex to extract layer names.
  a						: List[Any]					       =					re.compile(r'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'    )

  # The simple map of names for "automated" rules.
  a						: Optional[Any]					       =					{
      'attention.dense': '.attn.c_proj.',
      'self_attention.dense': '.attn.c_proj.',
      'mlp.dense_h_to_4h': '.mlp.c_fc.',
      'mlp.dense_4h_to_h': '.mlp.c_proj.',
  }

  # Extract the layers.
  for key, val in transformer.items():
    # Match the name.
    a						: Tuple					       =					layer_re.match(_A    )

    # Stop if that's not a layer
    if m is None:
      break

    # The index of the layer.
    a						: Union[str, Any]					       =					int(m.group(1    )    )
    # The name of the operation.
    a						: Optional[int]					       =					m.group(2    )
    # Is it a weight or a bias?
    a						: Optional[int]					       =					m.group(3    )

    # The name of the layer.
    a						: Any					       =					f"""transformer.h.{layer_idx}"""

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('layernorm'    ):
      a						: str					       =					'ln_1' if op_name.startswith('input'    ) else 'ln_2'
      a						: Tuple					       =					val

    # Transpose the QKV matrix.
    elif (
        op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
    ) and weight_or_bias == "weight":
      # Insert a tensor of 1x1xDxD bias.
      a						: Dict					       =					torch.tril(torch.ones((n_positions, n_positions)			,      dtype=torch.floataa    )    ).view(
          1			,      1			,      _A			,      _A    )
      a						: Optional[Any]					       =					causal_mask

      # Insert a "dummy" tensor for masked_bias.
      a						: List[Any]					       =					torch.tensor(-1E4			,      dtype=torch.floataa    )
      a						: List[str]					       =					masked_bias

      a						: Union[str, Any]					       =					fix_query_key_value_ordering(_A			,      _A			,      3			,      _A			,      _A    )
      # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
      a						: int					       =					out_val.transpose(0			,      1    ).contiguous()
      # Store.
      a						: int					       =					out_val

    # Transpose the bias.
    elif (
        op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
    ) and weight_or_bias == "bias":
      a						: str					       =					fix_query_key_value_ordering(_A			,      _A			,      3			,      _A			,      _A    )
      # Store. No change of shape.
      a						: List[str]					       =					out_val

    # Transpose the weights.
    elif weight_or_bias == "weight":
      a						: Tuple					       =					megatron_to_transformers[op_name]
      a						: List[str]					       =					val.transpose(0			,      1    )

    # Copy the bias.
    elif weight_or_bias == "bias":
      a						: Dict					       =					megatron_to_transformers[op_name]
      a						: Optional[Any]					       =					val

    # DEBUG.
  assert config.n_layer == layer_idx + 1

  # The final layernorm.
  a						: str					       =					transformer['final_layernorm.weight']
  a						: List[str]					       =					transformer['final_layernorm.bias']

  # For LM head, transformers' wants the matrix to weight embeddings.
  a						: Optional[int]					       =					word_embeddings

  # It should be done!
  return output_state_dict
def 				lowerCamelCase__	(						):
  # Create the argument parser.
  a						: Dict					       =					argparse.ArgumentParser()
  parser.add_argument('--print-checkpoint-structure'			,      action='store_true'    )
  parser.add_argument(
      'path_to_checkpoint'			,      type=_A			,      help='Path to the checkpoint file (.zip archive or direct .pt file)'			,      )
  parser.add_argument(
      '--config_file'			,      default=''			,      type=_A			,      help='An optional config json file describing the pre-trained model.'			,      )
  a						: Union[str, Any]					       =					parser.parse_args()

  # Extract the basename.
  a						: Optional[Any]					       =					os.path.dirname(args.path_to_checkpoint    )

  # Load the model.
  # the .zip is very optional, let's keep it for backward compatibility
  print(f"""Extracting PyTorch state dictionary from {args.path_to_checkpoint}"""    )
  if args.path_to_checkpoint.endswith('.zip'    ):
    with zipfile.ZipFile(args.path_to_checkpoint			,      'r'    ) as checkpoint:
      with checkpoint.open('release/mp_rank_00/model_optim_rng.pt'    ) as pytorch_dict:
        a						: Union[str, Any]					       =					torch.load(_A			,      map_location='cpu'    )
  else:
    a						: Any					       =					torch.load(args.path_to_checkpoint			,      map_location='cpu'    )

  a						: List[Any]					       =					input_state_dict.get('args'			,      _A    )

  # Read the config, or default to the model released by NVIDIA.
  if args.config_file == "":
    if ds_args is not None:
      if ds_args.bias_gelu_fusion:
        a						: int					       =					'gelu_fast'
      elif ds_args.openai_gelu:
        a						: Dict					       =					'gelu_new'
      else:
        a						: Any					       =					'gelu'
    else:
      # in the very early days this used to be "gelu_new"
      a						: Any					       =					'gelu_new'

    # Spell out all parameters in case the defaults change.
    a						: Tuple					       =					GPTaConfig(
        vocab_size=5_0257			,      n_positions=1024			,      n_embd=1024			,      n_layer=24			,      n_head=16			,      n_inner=4096			,      activation_function=_A			,      resid_pdrop=0.1			,      embd_pdrop=0.1			,      attn_pdrop=0.1			,      layer_norm_epsilon=1E-5			,      initializer_range=0.02			,      summary_type='cls_index'			,      summary_use_proj=_A			,      summary_activation=_A			,      summary_proj_to_labels=_A			,      summary_first_dropout=0.1			,      scale_attn_weights=_A			,      use_cache=_A			,      bos_token_id=5_0256			,      eos_token_id=5_0256			,      )
  else:
    a						: str					       =					GPTaConfig.from_json_file(args.config_file    )

  a						: Any					       =					['GPT2LMHeadModel']

  # Convert.
  print('Converting'    )
  a						: Union[str, Any]					       =					convert_megatron_checkpoint(_A			,      _A			,      _A    )

  # Print the structure of converted state dict.
  if args.print_checkpoint_structure:
    recursive_print(_A			,      _A    )

  # Add tokenizer class info to config
  # see https://github.com/huggingface/transformers/issues/13906)
  if ds_args is not None:
    a						: Union[str, Any]					       =					ds_args.tokenizer_type
    if tokenizer_type == "GPT2BPETokenizer":
      a						: Tuple					       =					'gpt2'
    elif tokenizer_type == "PretrainedFromHF":
      a						: List[str]					       =					ds_args.tokenizer_name_or_path
    else:
      raise ValueError(f"""Unrecognized tokenizer_type {tokenizer_type}"""    )
  else:
    a						: Optional[Any]					       =					'gpt2'

  a						: Tuple					       =					AutoTokenizer.from_pretrained(_A    )
  a						: str					       =					type(_A    ).__name__
  a						: List[str]					       =					tokenizer_class

  # Store the config to file.
  print('Saving config'    )
  config.save_pretrained(_A    )

  # Save tokenizer based on args
  print(f"""Adding {tokenizer_class} tokenizer files"""    )
  tokenizer.save_pretrained(_A    )

  # Store the state_dict to file.
  a						: Optional[int]					       =					os.path.join(_A			,      'pytorch_model.bin'    )
  print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""    )
  torch.save(_A			,      _A    )


####################################################################################################

if __name__ == "__main__":
     main()

####################################################################################################