
'''simple docstring'''
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCAmelCase					:    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
)

lowerCAmelCase					:    Optional[Any]           = (
    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
)

lowerCAmelCase					:    Union[str, Any]           = [
    '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_(     A						:		Optional[int]		,     A						:		List[str]):
							for tf_name, hf_name in patterns:
														UpperCamelCase   			=     k.replace(A		,     A)
							return k







def 		A_(     A						:		dict		,     A						:		dict):
							UpperCamelCase   			=     BigBirdPegasusConfig(**A)
							UpperCamelCase   			=     BigBirdPegasusForConditionalGeneration(A)
							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(A) for ending in KEYS_TO_IGNORE]
														if any(A):
																					continue
														UpperCamelCase   			=     DECODER_PATTERNS
														UpperCamelCase   			=     rename_state_dict_key(A		,     A)
														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(A)
														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(A) for ending in KEYS_TO_IGNORE]
														if any(A):
																					continue
														UpperCamelCase   			=     REMAINING_PATTERNS
														UpperCamelCase   			=     rename_state_dict_key(A		,     A)
														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(A)
														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(A		,     strict=A)
							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_(     A						:		List[str]):
							UpperCamelCase   			=     tf.train.list_variables(A)
							UpperCamelCase   			=     {}
							UpperCamelCase   			=     ['global_step']
							for name, shape in tqdm(A		,     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(A		,     A)
														UpperCamelCase   			=     array
							return tf_weights







def 		A_(     A						:		str		,     A						:		str		,     A						:		dict):
							UpperCamelCase   			=     get_tf_weights_as_numpy(A)
							UpperCamelCase   			=     convert_bigbird_pegasus(A		,     A)
							torch_model.save_pretrained(A)


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = 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.')
						lowerCAmelCase					:    Dict           = parser.parse_args()
						lowerCAmelCase					:    Any           = {}
						convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""swin"""

		lowerCamelCase     							=						{
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}



		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			int=224  ,	UpperCamelCase__				:			Optional[int]=4  ,	UpperCamelCase__				:			str=3  ,	UpperCamelCase__				:			str=96  ,	UpperCamelCase__				:			List[Any]=[2, 2, 6, 2]  ,	UpperCamelCase__				:			Optional[Any]=[3, 6, 12, 24]  ,	UpperCamelCase__				:			Optional[Any]=7  ,	UpperCamelCase__				:			Tuple=4.0  ,	UpperCamelCase__				:			Tuple=True  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.0  ,	UpperCamelCase__				:			List[str]=0.1  ,	UpperCamelCase__				:			Optional[Any]="gelu"  ,	UpperCamelCase__				:			Optional[Any]=False  ,	UpperCamelCase__				:			Tuple=0.02  ,	UpperCamelCase__				:			List[Any]=1e-5  ,	UpperCamelCase__				:			Any=32  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			int=None  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Optional[int]:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       Any     			=						image_size
				snake_case  :       Optional[int]     			=						patch_size
				snake_case  :       List[Any]     			=						num_channels
				snake_case  :       Union[str, Any]     			=						embed_dim
				snake_case  :       str     			=						depths
				snake_case  :       str     			=						len(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						num_heads
				snake_case  :       List[Any]     			=						window_size
				snake_case  :       Optional[int]     			=						mlp_ratio
				snake_case  :       Union[str, Any]     			=						qkv_bias
				snake_case  :       Optional[int]     			=						hidden_dropout_prob
				snake_case  :       Optional[int]     			=						attention_probs_dropout_prob
				snake_case  :       Optional[int]     			=						drop_path_rate
				snake_case  :       List[Any]     			=						hidden_act
				snake_case  :       int     			=						use_absolute_embeddings
				snake_case  :       str     			=						layer_norm_eps
				snake_case  :       Optional[Any]     			=						initializer_range
				snake_case  :       Any     			=						encoder_stride
				# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				snake_case  :       str     			=						int(embed_dim * 2 ** (len(UpperCamelCase__      ) - 1)      )
				snake_case  :       int     			=						['''stem'''] + [f'stage{idx}' for idx in range(1  ,	len(UpperCamelCase__      ) + 1      )]
				snake_case			,snake_case  :       List[Any]     			=						get_aligned_output_features_output_indices(
				    out_features=UpperCamelCase__  ,	out_indices=UpperCamelCase__  ,	stage_names=self.stage_names      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			float:






				"""simple docstring"""







				return 1e-4


