





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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    for pegasus_name, hf_name in PATTERNS:
        lowerCamelCase__       : List[str]   					=						k.replace(_UpperCAmelCase		,      _UpperCAmelCase					)
    return k


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	PegasusForConditionalGeneration:
    lowerCamelCase__       : int   					=						DEFAULTS.copy()
    cfg_kwargs.update(_UpperCAmelCase					)
    lowerCamelCase__       : str   					=						PegasusConfig(**_UpperCAmelCase					)
    lowerCamelCase__       : Tuple   					=						PegasusForConditionalGeneration(_UpperCAmelCase					)
    lowerCamelCase__       : Optional[int]   					=						torch_model.model.state_dict()
    lowerCamelCase__       : List[str]   					=						{}
    for k, v in tf_weights.items():
        lowerCamelCase__       : Optional[int]   					=						rename_state_dict_key(_UpperCAmelCase					)
        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:
            lowerCamelCase__       : Any   					=						v.T
        lowerCamelCase__       : int   					=						torch.tensor(_UpperCAmelCase		,      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
    lowerCamelCase__       : int   					=						torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]					)
    lowerCamelCase__       : int   					=						mapping['shared.weight']
    lowerCamelCase__       : List[Any]   					=						mapping['shared.weight']
    lowerCamelCase__       : Optional[int]   					=						{k: torch.zeros_like(_UpperCAmelCase					) for k, v in sd.items() if k.endswith('bias'					) and k not in mapping}
    mapping.update(**_UpperCAmelCase					)
    lowerCamelCase__					,				lowerCamelCase__       : Dict   					=						torch_model.model.load_state_dict(_UpperCAmelCase		,      strict=_UpperCAmelCase					)
    lowerCamelCase__       : List[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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase="./ckpt/aeslc/model.ckpt-32000"					)     ->	Dict:
    lowerCamelCase__       : Tuple   					=						tf.train.list_variables(_UpperCAmelCase					)
    lowerCamelCase__       : str   					=						{}
    lowerCamelCase__       : List[Any]   					=						['Adafactor', 'global_step']
    for name, shape in tqdm(_UpperCAmelCase		,      desc='converting tf checkpoint to dict'					):
        lowerCamelCase__       : List[str]   					=						any(pat in name for pat in ignore_name					)
        if skip_key:
            continue
        lowerCamelCase__       : List[str]   					=						tf.train.load_variable(_UpperCAmelCase		,      _UpperCAmelCase					)
        lowerCamelCase__       : Tuple   					=						array
    return tf_weights


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Optional[int]:
    # save tokenizer first
    lowerCamelCase__       : Dict   					=						Path(_UpperCAmelCase					).parent.name
    lowerCamelCase__       : List[Any]   					=						task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
    lowerCamelCase__       : Tuple   					=						PegasusTokenizer.from_pretrained('sshleifer/pegasus'		,      model_max_length=_UpperCAmelCase					)
    assert tok.model_max_length == desired_max_model_length
    tok.save_pretrained(_UpperCAmelCase					)

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


if __name__ == "__main__":
    _UpperCAmelCase							:			Tuple        =							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							:			str        =							parser.parse_args()
    if args.save_dir is None:
        _UpperCAmelCase							:			Any        =							Path(args.tf_ckpt_path).parent.name
        _UpperCAmelCase							:			Any        =							os.path.join("""pegasus""", dataset)
    convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)












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   lowerCAmelCase				(   unittest.TestCase     ):
 def 	A_      ( self    : Any      )     ->      int:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
 def 	A_      ( self    : Optional[Any]      )     ->      Optional[int]:
     lowerCamelCase__					,				lowerCamelCase__       : str   					=						FlaxControlNetModel.from_pretrained(
         'lllyasviel/sd-controlnet-canny'     ,	from_pt=UpperCAmelCase     ,	dtype=jnp.bfloataa      )
     lowerCamelCase__					,				lowerCamelCase__       : int   					=						FlaxStableDiffusionControlNetPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'     ,	controlnet=UpperCAmelCase     ,	from_pt=UpperCAmelCase     ,	dtype=jnp.bfloataa      )
     lowerCamelCase__       : int   					=						controlnet_params

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

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

     lowerCamelCase__       : List[str]   					=						jax.random.PRNGKey(0      )
     lowerCamelCase__       : Optional[int]   					=						jax.random.split(UpperCAmelCase     ,	jax.device_count()      )

     lowerCamelCase__       : List[Any]   					=						replicate(UpperCAmelCase      )
     lowerCamelCase__       : Dict   					=						shard(UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						shard(UpperCAmelCase      )

     lowerCamelCase__       : Any   					=						pipe(
         prompt_ids=UpperCAmelCase     ,	image=UpperCAmelCase     ,	params=UpperCAmelCase     ,	prng_seed=UpperCAmelCase     ,	num_inference_steps=50     ,	jit=UpperCAmelCase     ,	).images
     assert images.shape == (jax.device_count(), 1, 768, 512, 3)

     lowerCamelCase__       : Dict   					=						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
     lowerCamelCase__       : List[str]   					=						images[0, 253:256, 253:256, -1]

     lowerCamelCase__       : Tuple   					=						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
     lowerCamelCase__       : Tuple   					=						jnp.array(
         [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]      )
     print(F"""output_slice: {output_slice}"""      )
     assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2







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

     lowerCamelCase__       : Any   					=						'Chef in the kitchen'
     lowerCamelCase__       : str   					=						jax.device_count()
     lowerCamelCase__       : Union[str, Any]   					=						pipe.prepare_text_inputs([prompts] * num_samples      )

     lowerCamelCase__       : Tuple   					=						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'      )
     lowerCamelCase__       : List[str]   					=						pipe.prepare_image_inputs([pose_image] * num_samples      )

     lowerCamelCase__       : Tuple   					=						jax.random.PRNGKey(0      )
     lowerCamelCase__       : List[str]   					=						jax.random.split(UpperCAmelCase     ,	jax.device_count()      )

     lowerCamelCase__       : Dict   					=						replicate(UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						shard(UpperCAmelCase      )
     lowerCamelCase__       : Tuple   					=						shard(UpperCAmelCase      )

     lowerCamelCase__       : Union[str, Any]   					=						pipe(
         prompt_ids=UpperCAmelCase     ,	image=UpperCAmelCase     ,	params=UpperCAmelCase     ,	prng_seed=UpperCAmelCase     ,	num_inference_steps=50     ,	jit=UpperCAmelCase     ,	).images
     assert images.shape == (jax.device_count(), 1, 768, 512, 3)

     lowerCamelCase__       : Dict   					=						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]      )
     lowerCamelCase__       : int   					=						images[0, 253:256, 253:256, -1]

     lowerCamelCase__       : Optional[int]   					=						jnp.asarray(jax.device_get(image_slice.flatten()      )      )
     lowerCamelCase__       : int   					=						jnp.array(
         [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]      )
     print(F"""output_slice: {output_slice}"""      )
     assert jnp.abs(output_slice - expected_slice      ).max() < 1e-2





