
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


_UpperCAmelCase					: List[str]      =       [
    # tf -> hf
    ("/", "."),
    ("layer_", "layers."),
    ("kernel", "weight"),
    ("beta", "bias"),
    ("gamma", "weight"),
    ("pegasus", "model"),
]
_UpperCAmelCase					: Optional[Any]      =       [
    (".output.dense", ".fc2"),
    ("intermediate.LayerNorm", "final_layer_norm"),
    ("intermediate.dense", "fc1"),
]

_UpperCAmelCase					: Union[str, Any]      =       (
    INIT_COMMON
    + [
        ("attention.self.LayerNorm", "self_attn_layer_norm"),
        ("attention.output.dense", "self_attn.out_proj"),
        ("attention.self", "self_attn"),
        ("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
        ("attention.encdec_output.dense", "encoder_attn.out_proj"),
        ("attention.encdec", "encoder_attn"),
        ("key", "k_proj"),
        ("value", "v_proj"),
        ("query", "q_proj"),
        ("decoder.LayerNorm", "decoder.layernorm_embedding"),
    ]
    + END_COMMON
)

_UpperCAmelCase					: int      =       (
    INIT_COMMON
    + [
        ("embeddings.word_embeddings", "shared.weight"),
        ("embeddings.position_embeddings", "embed_positions.weight"),
        ("attention.self.LayerNorm", "self_attn_layer_norm"),
        ("attention.output.dense", "self_attn.output"),
        ("attention.self", "self_attn.self"),
        ("encoder.LayerNorm", "encoder.layernorm_embedding"),
    ]
    + END_COMMON
)

_UpperCAmelCase					: Optional[int]      =       [
    "encdec/key/bias",
    "encdec/query/bias",
    "encdec/value/bias",
    "self/key/bias",
    "self/query/bias",
    "self/value/bias",
    "encdec_output/dense/bias",
    "attention/output/dense/bias",
]


def       A       (					lowercase    ,       lowercase )   ->  Union[str, Any]:
		'''simple docstring'''

		for tf_name, hf_name in patterns:
				UpperCamelCase		=     k.replace(lowercase    ,       lowercase )
		return k


def       A       (					lowercase    ,       lowercase )   ->  BigBirdPegasusForConditionalGeneration:
		'''simple docstring'''

		UpperCamelCase		=     BigBirdPegasusConfig(**lowercase )
		UpperCamelCase		=     BigBirdPegasusForConditionalGeneration(lowercase )
		UpperCamelCase		=     torch_model.state_dict()
		UpperCamelCase		=     {}

		# separating decoder weights
		UpperCamelCase		=     {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder' )}
		UpperCamelCase		=     {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder' )}

		for k, v in tqdm(decoder_weights.items()    ,       'tf -> hf conversion' ):
				UpperCamelCase		=     [k.endswith(lowercase ) for ending in KEYS_TO_IGNORE]
				if any(lowercase ):
						continue
				UpperCamelCase		=     DECODER_PATTERNS
				UpperCamelCase		=     rename_state_dict_key(lowercase    ,       lowercase )
				if new_k not in state_dict:
						raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})''' )
				if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
						UpperCamelCase		=     v.T
				UpperCamelCase		=     torch.from_numpy(lowercase )
				assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

		for k, v in tqdm(remaining_weights.items()    ,       'tf -> hf conversion' ):
				UpperCamelCase		=     [k.endswith(lowercase ) for ending in KEYS_TO_IGNORE]
				if any(lowercase ):
						continue
				UpperCamelCase		=     REMAINING_PATTERNS
				UpperCamelCase		=     rename_state_dict_key(lowercase    ,       lowercase )
				if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
						raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})''' )
				if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
						UpperCamelCase		=     v.T
				UpperCamelCase		=     torch.from_numpy(lowercase )
				if k != "pegasus/embeddings/position_embeddings":
						assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

		UpperCamelCase		=     mapping['model.embed_positions.weight']
		UpperCamelCase		=     mapping.pop('model.embed_positions.weight' )
		UpperCamelCase	,  UpperCamelCase		=     torch_model.load_state_dict(lowercase    ,       strict=lowercase )
		UpperCamelCase		=     [
		    k
		    for k in missing
		    if k
		    not in [
		        'final_logits_bias',
		        'model.encoder.embed_tokens.weight',
		        'model.decoder.embed_tokens.weight',
		        'lm_head.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       (					lowercase )   ->  Dict:
		'''simple docstring'''

		UpperCamelCase		=     tf.train.list_variables(lowercase )
		UpperCamelCase		=     {}
		UpperCamelCase		=     ['global_step']
		for name, shape in tqdm(lowercase    ,       desc='converting tf checkpoint to dict' ):
				UpperCamelCase		=     any(pat in name for pat in ignore_name )
				if skip_key:
						continue
				UpperCamelCase		=     tf.train.load_variable(lowercase    ,       lowercase )
				UpperCamelCase		=     array
		return tf_weights


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     get_tf_weights_as_numpy(lowercase )
		UpperCamelCase		=     convert_bigbird_pegasus(lowercase    ,       lowercase )
		torch_model.save_pretrained(lowercase )


if __name__ == "__main__":
					_UpperCAmelCase					: Dict      =       argparse.ArgumentParser()
					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					: Union[str, Any]      =       parser.parse_args()
					_UpperCAmelCase					: List[str]      =       {}
					convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


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



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]       )

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



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-1



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_dpmpp_2m'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe(
				    [prompt]				,      generator=A_				,      guidance_scale=7.5				,      num_inference_steps=15				,      output_type='np'				,      use_karras_sigmas=A_				,      )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]       )

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