
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A				=(
    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				=(
    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				=[
    '''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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				for tf_name, hf_name in patterns:
								lowerCamelCase_						=			k.replace(lowerCamelCase__	, lowerCamelCase__    )
				return k





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			BigBirdPegasusConfig(**lowerCamelCase__    )
				lowerCamelCase_						=			BigBirdPegasusForConditionalGeneration(lowerCamelCase__    )
				lowerCamelCase_						=			torch_model.state_dict()
				lowerCamelCase_						=			{}

				# separating decoder weights
				lowerCamelCase_						=			{k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"    )}
				lowerCamelCase_						=			{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"    ):
								lowerCamelCase_						=			[k.endswith(lowerCamelCase__    ) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase__    ):
												continue
								lowerCamelCase_						=			DECODER_PATTERNS
								lowerCamelCase_						=			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"]    ):
												lowerCamelCase_						=			v.T
								lowerCamelCase_						=			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"    ):
								lowerCamelCase_						=			[k.endswith(lowerCamelCase__    ) for ending in KEYS_TO_IGNORE]
								if any(lowerCamelCase__    ):
												continue
								lowerCamelCase_						=			REMAINING_PATTERNS
								lowerCamelCase_						=			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"]    ):
												lowerCamelCase_						=			v.T
								lowerCamelCase_						=			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}'

				lowerCamelCase_						=			mapping["model.embed_positions.weight"]
				lowerCamelCase_						=			mapping.pop("model.embed_positions.weight"    )
				lowerCamelCase_	,    lowerCamelCase_						=			torch_model.load_state_dict(lowerCamelCase__	, strict=lowerCamelCase__    )
				lowerCamelCase_						=			[
				    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 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			tf.train.list_variables(lowerCamelCase__    )
				lowerCamelCase_						=			{}
				lowerCamelCase_						=			["global_step"]
				for name, shape in tqdm(lowerCamelCase__	, desc="converting tf checkpoint to dict"    ):
								lowerCamelCase_						=			any(pat in name for pat in ignore_name    )
								if skip_key:
												continue
								lowerCamelCase_						=			tf.train.load_variable(lowerCamelCase__	, lowerCamelCase__    )
								lowerCamelCase_						=			array
				return tf_weights





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			get_tf_weights_as_numpy(lowerCamelCase__    )
				lowerCamelCase_						=			convert_bigbird_pegasus(lowerCamelCase__	, lowerCamelCase__    )
				torch_model.save_pretrained(lowerCamelCase__    )


if __name__ == "__main__":
			__A				=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				=parser.parse_args()
			__A				={}
			convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				={'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''XGLMTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''XGLMTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XGLMForCausalLM''',
			    '''XGLMModel''',
			    '''XGLMPreTrainedModel''',
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''FlaxXGLMForCausalLM''',
			    '''FlaxXGLMModel''',
			    '''FlaxXGLMPreTrainedModel''',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXGLMForCausalLM''',
			    '''TFXGLMModel''',
			    '''TFXGLMPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure)


