




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A   :  int					=      (
    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
)

__A   :  str					=      (
    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
)

__A   :  List[str]					=      [
    "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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Any:






		"""simple docstring"""


		for tf_name, hf_name in patterns:
				__lowercase				:		Union[str, Any]												=				k.replace(lowerCamelCase__,  lowerCamelCase__				)
		return k

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->BigBirdPegasusForConditionalGeneration:






		"""simple docstring"""


		__lowercase				:		List[Any]												=				BigBirdPegasusConfig(**lowerCamelCase__				)
		__lowercase				:		List[Any]												=				BigBirdPegasusForConditionalGeneration(lowerCamelCase__				)
		__lowercase				:		Union[str, Any]												=				torch_model.state_dict()
		__lowercase				:		Dict												=				{}

		# separating decoder weights
		__lowercase				:		Optional[Any]												=				{k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"				)}
		__lowercase				:		Any												=				{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"				):
				__lowercase				:		Union[str, Any]												=				[k.endswith(lowerCamelCase__				) for ending in KEYS_TO_IGNORE]
				if any(lowerCamelCase__				):
						continue
				__lowercase				:		List[str]												=				DECODER_PATTERNS
				__lowercase				:		Optional[Any]												=				rename_state_dict_key(lowerCamelCase__,  lowerCamelCase__				)
				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"]				):
						__lowercase				:		List[str]												=				v.T
				__lowercase				:		int												=				torch.from_numpy(lowerCamelCase__				)
				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"				):
				__lowercase				:		Optional[int]												=				[k.endswith(lowerCamelCase__				) for ending in KEYS_TO_IGNORE]
				if any(lowerCamelCase__				):
						continue
				__lowercase				:		Optional[Any]												=				REMAINING_PATTERNS
				__lowercase				:		Any												=				rename_state_dict_key(lowerCamelCase__,  lowerCamelCase__				)
				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"]				):
						__lowercase				:		Optional[Any]												=				v.T
				__lowercase				:		int												=				torch.from_numpy(lowerCamelCase__				)
				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}'

		__lowercase				:		List[str]												=				mapping["model.embed_positions.weight"]
		__lowercase				:		Optional[Any]												=				mapping.pop("model.embed_positions.weight"				)
		__lowercase				:		Union[str, Any]												=				torch_model.load_state_dict(lowerCamelCase__,  strict=lowerCamelCase__				)
		__lowercase				:		str												=				[
		    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 						snake_case__       (					_lowerCamelCase				)			->Dict:






		"""simple docstring"""


		__lowercase				:		Tuple												=				tf.train.list_variables(lowerCamelCase__				)
		__lowercase				:		Tuple												=				{}
		__lowercase				:		Tuple												=				["global_step"]
		for name, shape in tqdm(lowerCamelCase__,  desc="converting tf checkpoint to dict"				):
				__lowercase				:		Any												=				any(pat in name for pat in ignore_name				)
				if skip_key:
						continue
				__lowercase				:		str												=				tf.train.load_variable(lowerCamelCase__,  lowerCamelCase__				)
				__lowercase				:		Tuple												=				array
		return tf_weights

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Any:






		"""simple docstring"""


		__lowercase				:		Optional[Any]												=				get_tf_weights_as_numpy(lowerCamelCase__				)
		__lowercase				:		Tuple												=				convert_bigbird_pegasus(lowerCamelCase__,  lowerCamelCase__				)
		torch_model.save_pretrained(lowerCamelCase__				)


if __name__ == "__main__":
						__A   :  Optional[int]					=      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.')
						__A   :  List[Any]					=      parser.parse_args()
						__A   :  Union[str, Any]					=      {}
						convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






"""simple docstring"""







import os
import sys
import unittest


__A   :  Optional[Any]					=      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__A   :  Optional[Any]					=      os.path.join(git_repo_path, 'src', 'diffusers')




class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	str						):
						__lowercase				:		int												=				find_backend("    if not is_torch_available():"						)
						self.assertEqual(lowercase__    ,   "torch"						)

						# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
						# self.assertEqual(backend_with_underscore, "tensorflow_text")

						__lowercase				:		int												=				find_backend("    if not (is_torch_available() and is_transformers_available()):"						)
						self.assertEqual(lowercase__    ,   "torch_and_transformers"						)

						# double_backend_with_underscore = find_backend(
						#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
						# )
						# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

						__lowercase				:		str												=				find_backend(
						    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"						)
						self.assertEqual(lowercase__    ,   "torch_and_transformers_and_onnx"						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		List[Any]												=				read_init()
						# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
						self.assertIn("torch"    ,   lowercase__						)
						self.assertIn("torch_and_transformers"    ,   lowercase__						)
						self.assertIn("flax_and_transformers"    ,   lowercase__						)
						self.assertIn("torch_and_transformers_and_onnx"    ,   lowercase__						)

						# Likewise, we can't assert on the exact content of a key
						self.assertIn("UNet2DModel"    ,   objects["torch"]						)
						self.assertIn("FlaxUNet2DConditionModel"    ,   objects["flax"]						)
						self.assertIn("StableDiffusionPipeline"    ,   objects["torch_and_transformers"]						)
						self.assertIn("FlaxStableDiffusionPipeline"    ,   objects["flax_and_transformers"]						)
						self.assertIn("LMSDiscreteScheduler"    ,   objects["torch_and_scipy"]						)
						self.assertIn("OnnxStableDiffusionPipeline"    ,   objects["torch_and_transformers_and_onnx"]						)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Tuple												=				create_dummy_object("CONSTANT"    ,   "'torch'"						)
						self.assertEqual(lowercase__    ,   "\nCONSTANT = None\n"						)

						__lowercase				:		Union[str, Any]												=				create_dummy_object("function"    ,   "'torch'"						)
						self.assertEqual(
						    lowercase__    ,   "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"						)

						__lowercase				:		Tuple												=				"\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
						__lowercase				:		Dict												=				create_dummy_object("FakeClass"    ,   "'torch'"						)
						self.assertEqual(lowercase__    ,   lowercase__						)



				def       snake_case    (     self					:	List[Any]						):
						__lowercase				:		Optional[int]												=				"# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
						__lowercase				:		List[str]												=				create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}						)
						self.assertEqual(dummy_files["torch"]    ,   lowercase__						)
