"""simple docstring"""





import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


_UpperCamelCase     :		List[Any]											=					[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]





def        a_  (   _lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

       for pegasus_name, hf_name in PATTERNS:
              lowercase__    :	Optional[Any]     					=			k.replace(_lowerCAmelCase     ,				_lowerCAmelCase  )
       return k





def        a_  (   _lowerCAmelCase						:				dict     ,				_lowerCAmelCase						:				dict  ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			DEFAULTS.copy()
       cfg_kwargs.update(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			PegasusConfig(**_lowerCAmelCase  )
       lowercase__    :	Dict     					=			PegasusForConditionalGeneration(_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			torch_model.model.state_dict()
       lowercase__    :	str     					=			{}
       for k, v in tf_weights.items():
              lowercase__    :	List[Any]     					=			rename_state_dict_key(_lowerCAmelCase  )
              if new_k not in sd:
                     raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""  )

              if "dense" in k or "proj" in new_k:
                     lowercase__    :	str     					=			v.T
              lowercase__    :	List[Any]     					=			torch.tensor(_lowerCAmelCase     ,				dtype=sd[new_k].dtype  )
              assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
       # make sure embedding.padding_idx is respected
       lowercase__    :	Any     					=			torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]  )
       lowercase__    :	List[str]     					=			mapping['shared.weight']
       lowercase__    :	Dict     					=			mapping['shared.weight']
       lowercase__    :	str     					=			{k: torch.zeros_like(_lowerCAmelCase  ) for k, v in sd.items() if k.endswith('bias'  ) and k not in mapping}
       mapping.update(**_lowerCAmelCase  )
       lowercase__					,   lowercase__    :	List[Any]     					=			torch_model.model.load_state_dict(_lowerCAmelCase     ,				strict=_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			[
           k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
       ]
       assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
       assert extra == [], f"""no matches found for the following tf keys {extra}"""
       return torch_model





def        a_  (   _lowerCAmelCase						:				Tuple="./ckpt/aeslc/model.ckpt-32000"  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			tf.train.list_variables(_lowerCAmelCase  )
       lowercase__    :	Tuple     					=			{}
       lowercase__    :	List[Any]     					=			['Adafactor', 'global_step']
       for name, shape in tqdm(_lowerCAmelCase     ,				desc='converting tf checkpoint to dict'  ):
              lowercase__    :	Optional[Any]     					=			any(pat in name for pat in ignore_name  )
              if skip_key:
                     continue
              lowercase__    :	Dict     					=			tf.train.load_variable(_lowerCAmelCase     ,				_lowerCAmelCase  )
              lowercase__    :	Optional[Any]     					=			array
       return tf_weights





def        a_  (   _lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				str  ):





       '''simple docstring'''

       lowercase__    :	str     					=			Path(_lowerCAmelCase  ).parent.name
       lowercase__    :	Optional[int]     					=			task_specific_params[f"""summarization_{dataset}"""]['max_position_embeddings']
       lowercase__    :	Union[str, Any]     					=			PegasusTokenizer.from_pretrained('sshleifer/pegasus'     ,				model_max_length=_lowerCAmelCase  )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(_lowerCAmelCase  )

       # convert model
       lowercase__    :	Tuple     					=			get_tf_weights_as_numpy(_lowerCAmelCase  )
       lowercase__    :	List[str]     					=			task_specific_params[f"""summarization_{dataset}"""]
       if dataset == "large":
              lowercase__    :	str     					=			task_specific_params
       lowercase__    :	Dict     					=			convert_pegasus(_lowerCAmelCase     ,				_lowerCAmelCase  )
       torch_model.save_pretrained(_lowerCAmelCase  )
       lowercase__    :	Dict     					=			torch_model.state_dict()
       sd.pop('model.decoder.embed_positions.weight'  )
       sd.pop('model.encoder.embed_positions.weight'  )
       torch.save(_lowerCAmelCase     ,				Path(_lowerCAmelCase  ) / 'pytorch_model.bin'  )


if __name__ == "__main__":
       _UpperCamelCase     :		str											=					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
       parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
       _UpperCamelCase     :		List[Any]											=					parser.parse_args()
       if args.save_dir is None:
              _UpperCamelCase     :		Dict											=					Path(args.tf_ckpt_path).parent.name
              _UpperCamelCase     :		Tuple											=					os.path.join("pegasus", dataset)
       convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


"""simple docstring"""





import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard


@slow
@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__					,   lowercase__    :	str     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-canny'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	List[str]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	List[Any]     					=			controlnet_params

             lowercase__    :	int     					=			'bird'
             lowercase__    :	List[Any]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Union[str, Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([canny_image] * num_samples							)

             lowercase__    :	List[Any]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	Tuple     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	str     					=			replicate(a							)
             lowercase__    :	List[str]     					=			shard(a							)
             lowercase__    :	Dict     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Any     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	Tuple     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	int     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	Optional[Any]     					=			jnp.array(
                 [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__					,   lowercase__    :	int     					=			FlaxControlNetModel.from_pretrained(
                 'lllyasviel/sd-controlnet-openpose'	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__					,   lowercase__    :	Optional[Any]     					=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
                 'runwayml/stable-diffusion-v1-5'	,    controlnet=a	,    from_pt=a	,    dtype=jnp.bfloataa							)
             lowercase__    :	Optional[Any]     					=			controlnet_params

             lowercase__    :	List[Any]     					=			'Chef in the kitchen'
             lowercase__    :	List[str]     					=			jax.device_count()
             lowercase__    :	Dict     					=			pipe.prepare_text_inputs([prompts] * num_samples							)

             lowercase__    :	Optional[Any]     					=			load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'							)
             lowercase__    :	Optional[int]     					=			pipe.prepare_image_inputs([pose_image] * num_samples							)

             lowercase__    :	List[str]     					=			jax.random.PRNGKey(0							)
             lowercase__    :	str     					=			jax.random.split(a	,    jax.device_count()							)

             lowercase__    :	Optional[Any]     					=			replicate(a							)
             lowercase__    :	Optional[Any]     					=			shard(a							)
             lowercase__    :	List[Any]     					=			shard(a							)

             lowercase__    :	List[Any]     					=			pipe(
                 prompt_ids=a	,    image=a	,    params=a	,    prng_seed=a	,    num_inference_steps=5_0	,    jit=a	,    ).images
             assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

             lowercase__    :	Union[str, Any]     					=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
             lowercase__    :	List[str]     					=			images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

             lowercase__    :	Optional[int]     					=			jnp.asarray(jax.device_get(image_slice.flatten()							)							)
             lowercase__    :	str     					=			jnp.array(
                 [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]							)
             print(f"""output_slice: {output_slice}"""							)
             assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

