
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A		: Tuple								=     logging.get_logger(__name__)

A		: int								=     {
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}



class    a_ (			_a    ):
			a	:			Tuple					   =							'''blenderbot-small'''
			a	:			List[str]					   =							['''past_key_values''']
			a	:			Optional[Any]					   =							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






			def __init__(      self				,			__UpperCamelCase=50_265				,			__UpperCamelCase=512				,			__UpperCamelCase=8				,			__UpperCamelCase=2_048				,			__UpperCamelCase=16				,			__UpperCamelCase=8				,			__UpperCamelCase=2_048				,			__UpperCamelCase=16				,			__UpperCamelCase=0.0				,			__UpperCamelCase=0.0				,			__UpperCamelCase=True				,			__UpperCamelCase=True				,			__UpperCamelCase="gelu"				,			__UpperCamelCase=512				,			__UpperCamelCase=0.1				,			__UpperCamelCase=0.0				,			__UpperCamelCase=0.0				,			__UpperCamelCase=0.02				,			__UpperCamelCase=1				,			__UpperCamelCase=False				,			__UpperCamelCase=0				,			__UpperCamelCase=1				,			__UpperCamelCase=2				,			__UpperCamelCase=2				,			**__UpperCamelCase				,			):
									_lowercase					       =					vocab_size
									_lowercase					       =					max_position_embeddings
									_lowercase					       =					d_model
									_lowercase					       =					encoder_ffn_dim
									_lowercase					       =					encoder_layers
									_lowercase					       =					encoder_attention_heads
									_lowercase					       =					decoder_ffn_dim
									_lowercase					       =					decoder_layers
									_lowercase					       =					decoder_attention_heads
									_lowercase					       =					dropout
									_lowercase					       =					attention_dropout
									_lowercase					       =					activation_dropout
									_lowercase					       =					activation_function
									_lowercase					       =					init_std
									_lowercase					       =					encoder_layerdrop
									_lowercase					       =					decoder_layerdrop
									_lowercase					       =					use_cache
									_lowercase					       =					encoder_layers
									_lowercase					       =					scale_embedding  # scale factor will be sqrt(d_model) if True

									super().__init__(
									    pad_token_id=__UpperCamelCase				,			bos_token_id=__UpperCamelCase				,			eos_token_id=__UpperCamelCase				,			is_encoder_decoder=__UpperCamelCase				,			decoder_start_token_id=__UpperCamelCase				,			forced_eos_token_id=__UpperCamelCase				,			**__UpperCamelCase				,			)







class    a_ (			_a    ):






			@property
			def    UpperCamelCase_				(      self  ):
									if self.task in ["default", "seq2seq-lm"]:
															_lowercase					       =					OrderedDict(
															    [
															        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
															        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
															    ]  )

															if self.use_past:
																					_lowercase					       =					{0: """batch"""}
																					_lowercase					       =					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
															else:
																					_lowercase					       =					{0: """batch""", 1: """decoder_sequence"""}
																					_lowercase					       =					{0: """batch""", 1: """decoder_sequence"""}

															if self.use_past:
																					self.fill_with_past_key_values_(__UpperCamelCase				,			direction="""inputs"""  )
									elif self.task == "causal-lm":
															# TODO: figure this case out.
															_lowercase					       =					OrderedDict(
															    [
															        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
															        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
															    ]  )
															if self.use_past:
																					_lowercase       ,						_lowercase					       =					self.num_layers
																					for i in range(__UpperCamelCase  ):
																											_lowercase					       =					{0: """batch""", 2: """past_sequence + sequence"""}
																											_lowercase					       =					{0: """batch""", 2: """past_sequence + sequence"""}
									else:
															_lowercase					       =					OrderedDict(
															    [
															        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
															        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
															        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
															        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
															    ]  )

									return common_inputs






			@property
			def    UpperCamelCase_				(      self  ):
									if self.task in ["default", "seq2seq-lm"]:
															_lowercase					       =					super().outputs
									else:
															_lowercase					       =					super(__UpperCamelCase				,			self  ).outputs
															if self.use_past:
																					_lowercase       ,						_lowercase					       =					self.num_layers
																					for i in range(__UpperCamelCase  ):
																											_lowercase					       =					{0: """batch""", 2: """past_sequence + sequence"""}
																											_lowercase					       =					{0: """batch""", 2: """past_sequence + sequence"""}
									return common_outputs






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = -1				,			__UpperCamelCase = -1				,			__UpperCamelCase = False				,			__UpperCamelCase = None				,			):
									_lowercase					       =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )

									# Generate decoder inputs
									_lowercase					       =					seq_length if not self.use_past else 1
									_lowercase					       =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									_lowercase					       =					dict(**__UpperCamelCase				,			**__UpperCamelCase  )

									if self.use_past:
															if not is_torch_available():
																					raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""  )
															else:
																					import torch
															_lowercase       ,						_lowercase					       =					common_inputs["""input_ids"""].shape
															_lowercase					       =					common_inputs["""decoder_input_ids"""].shape[1]
															_lowercase       ,						_lowercase					       =					self.num_attention_heads
															_lowercase					       =					(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															_lowercase					       =					decoder_seq_length + 3
															_lowercase					       =					(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															_lowercase					       =					torch.cat(
															    [common_inputs["""decoder_attention_mask"""], torch.ones(__UpperCamelCase				,			__UpperCamelCase  )]				,			dim=1  )

															_lowercase					       =					[]
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															_lowercase       ,						_lowercase					       =					self.num_layers
															_lowercase					       =					min(__UpperCamelCase				,			__UpperCamelCase  )
															_lowercase					       =					max(__UpperCamelCase				,			__UpperCamelCase  ) - min_num_layers
															_lowercase					       =					"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

															for _ in range(__UpperCamelCase  ):
																					common_inputs["past_key_values"].append(
																					    (
																					        torch.zeros(__UpperCamelCase  ),
																					        torch.zeros(__UpperCamelCase  ),
																					        torch.zeros(__UpperCamelCase  ),
																					        torch.zeros(__UpperCamelCase  ),
																					    )  )
															# TODO: test this.
															_lowercase					       =					encoder_shape if remaining_side_name == """encoder""" else decoder_shape
															for _ in range(__UpperCamelCase				,			__UpperCamelCase  ):
																					common_inputs["past_key_values"].append((torch.zeros(__UpperCamelCase  ), torch.zeros(__UpperCamelCase  ))  )
									return common_inputs






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = -1				,			__UpperCamelCase = -1				,			__UpperCamelCase = False				,			__UpperCamelCase = None				,			):
									_lowercase					       =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )

									if self.use_past:
															if not is_torch_available():
																					raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""  )
															else:
																					import torch
															_lowercase       ,						_lowercase					       =					common_inputs["""input_ids"""].shape
															# Not using the same length for past_key_values
															_lowercase					       =					seqlen + 2
															_lowercase       ,						_lowercase					       =					self.num_layers
															_lowercase       ,						_lowercase					       =					self.num_attention_heads
															_lowercase					       =					(
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)

															_lowercase					       =					common_inputs["""attention_mask"""].dtype
															_lowercase					       =					torch.cat(
															    [common_inputs["""attention_mask"""], torch.ones(__UpperCamelCase				,			__UpperCamelCase				,			dtype=__UpperCamelCase  )]				,			dim=1  )
															_lowercase					       =					[
															    (torch.zeros(__UpperCamelCase  ), torch.zeros(__UpperCamelCase  )) for _ in range(__UpperCamelCase  )
															]
									return common_inputs






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = -1				,			__UpperCamelCase = -1				,			__UpperCamelCase = False				,			__UpperCamelCase = None				,			):
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									_lowercase					       =					compute_effective_axis_dimension(
									    __UpperCamelCase				,			fixed_dimension=OnnxConfig.default_fixed_batch				,			num_token_to_add=0  )

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									_lowercase					       =					tokenizer.num_special_tokens_to_add(__UpperCamelCase  )
									_lowercase					       =					compute_effective_axis_dimension(
									    __UpperCamelCase				,			fixed_dimension=OnnxConfig.default_fixed_sequence				,			num_token_to_add=__UpperCamelCase  )

									# Generate dummy inputs according to compute batch and sequence
									_lowercase					       =					[""" """.join([tokenizer.unk_token]  ) * seq_length] * batch_size
									_lowercase					       =					dict(tokenizer(__UpperCamelCase				,			return_tensors=__UpperCamelCase  )  )
									return common_inputs






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = -1				,			__UpperCamelCase = -1				,			__UpperCamelCase = False				,			__UpperCamelCase = None				,			):
									if self.task in ["default", "seq2seq-lm"]:
															_lowercase					       =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
															    __UpperCamelCase				,			batch_size=__UpperCamelCase				,			seq_length=__UpperCamelCase				,			is_pair=__UpperCamelCase				,			framework=__UpperCamelCase  )

									elif self.task == "causal-lm":
															_lowercase					       =					self._generate_dummy_inputs_for_causal_lm(
															    __UpperCamelCase				,			batch_size=__UpperCamelCase				,			seq_length=__UpperCamelCase				,			is_pair=__UpperCamelCase				,			framework=__UpperCamelCase  )
									else:
															_lowercase					       =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
															    __UpperCamelCase				,			batch_size=__UpperCamelCase				,			seq_length=__UpperCamelCase				,			is_pair=__UpperCamelCase				,			framework=__UpperCamelCase  )

									return common_inputs






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									if self.task in ["default", "seq2seq-lm"]:
															_lowercase					       =					super()._flatten_past_key_values_(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )
									else:
															_lowercase					       =					super(__UpperCamelCase				,			self  )._flatten_past_key_values_(
															    __UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )

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


A		: 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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     List[Any]:
						for pegasus_name, hf_name in PATTERNS:
												_lowercase					       =					k.replace(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						return k




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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	str       )  ->     str:
						# save tokenizer first
						_lowercase					       =					Path(SCREAMING_SNAKE_CASE_       ).parent.name
						_lowercase					       =					task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
						_lowercase					       =					PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""		,   model_max_length=SCREAMING_SNAKE_CASE_       )
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(SCREAMING_SNAKE_CASE_       )

						# convert model
						_lowercase					       =					get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					task_specific_params[f"""summarization_{dataset}"""]
						if dataset == "large":
												_lowercase					       =					task_specific_params
						_lowercase					       =					convert_pegasus(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						torch_model.save_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					torch_model.state_dict()
						sd.pop("""model.decoder.embed_positions.weight"""       )
						sd.pop("""model.encoder.embed_positions.weight"""       )
						torch.save(SCREAMING_SNAKE_CASE_		,   Path(SCREAMING_SNAKE_CASE_       ) / """pytorch_model.bin"""       )


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