

"""simple docstring"""




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_				:      List[str]           =			[
    # 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 						A							(  snake_case__     ):




			'''simple docstring'''


			for pegasus_name, hf_name in PATTERNS:
						SCREAMING_SNAKE_CASE__										=   k.replace(snake_case__		,					snake_case__     )
			return k

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   DEFAULTS.copy()
			cfg_kwargs.update(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   PegasusConfig(**snake_case__     )
			SCREAMING_SNAKE_CASE__										=   PegasusForConditionalGeneration(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   torch_model.model.state_dict()
			SCREAMING_SNAKE_CASE__										=   {}
			for k, v in tf_weights.items():
						SCREAMING_SNAKE_CASE__										=   rename_state_dict_key(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:
									SCREAMING_SNAKE_CASE__										=   v.T
						SCREAMING_SNAKE_CASE__										=   torch.tensor(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
			SCREAMING_SNAKE_CASE__										=   torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]     )
			SCREAMING_SNAKE_CASE__										=   mapping["""shared.weight"""]
			SCREAMING_SNAKE_CASE__										=   mapping["""shared.weight"""]
			SCREAMING_SNAKE_CASE__										=   {k: torch.zeros_like(snake_case__     ) for k, v in sd.items() if k.endswith("""bias"""     ) and k not in mapping}
			mapping.update(**snake_case__     )
			SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   torch_model.model.load_state_dict(snake_case__		,					strict=snake_case__     )
			SCREAMING_SNAKE_CASE__										=   [
			    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 						A							(  snake_case__="./ckpt/aeslc/model.ckpt-32000"     ):




			'''simple docstring'''


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

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   Path(snake_case__     ).parent.name
			SCREAMING_SNAKE_CASE__										=   task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
			SCREAMING_SNAKE_CASE__										=   PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""		,					model_max_length=snake_case__     )
			assert tok.model_max_length == desired_max_model_length
			tok.save_pretrained(snake_case__     )

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


if __name__ == "__main__":
						A_				:      Dict           =			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_				:      int           =			parser.parse_args()
						if args.save_dir is None:
												A_				:      Union[str, Any]           =			Path(args.tf_ckpt_path).parent.name
												A_				:      Optional[int]           =			os.path.join("pegasus", dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor





class  lowerCamelCase    (unittest.TestCase      ):

			def __init__(       self					:    Tuple     ,					__UpperCAmelCase					:    str     ,					__UpperCAmelCase					:    Any=7     ,					__UpperCAmelCase					:    Union[str, Any]=3     ,					__UpperCAmelCase					:    Any=1_8     ,					__UpperCAmelCase					:    Tuple=3_0     ,					__UpperCAmelCase					:    List[Any]=4_0_0     ,					__UpperCAmelCase					:    Dict=True     ,					__UpperCAmelCase					:    int=None     ,					__UpperCAmelCase					:    List[Any]=True     ,					__UpperCAmelCase					:    Any=None     ,					__UpperCAmelCase					:    Any=True     ,					) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   size if size is not None else {"""shortest_edge""": 2_0}
						SCREAMING_SNAKE_CASE__										=   crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   num_channels
						SCREAMING_SNAKE_CASE__										=   image_size
						SCREAMING_SNAKE_CASE__										=   min_resolution
						SCREAMING_SNAKE_CASE__										=   max_resolution
						SCREAMING_SNAKE_CASE__										=   do_resize
						SCREAMING_SNAKE_CASE__										=   size
						SCREAMING_SNAKE_CASE__										=   do_center_crop
						SCREAMING_SNAKE_CASE__										=   crop_size
						SCREAMING_SNAKE_CASE__										=   do_flip_channel_order





			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Any:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_flip_channel_order": self.do_flip_channel_order,
						}





@require_torch
@require_vision
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Tuple       		=				MobileViTImageProcessor if is_vision_available() else None

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   MobileViTImageProcessingTester(self							)

			@property
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     int:
						return self.image_processor_tester.prepare_image_processor_dict()

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_resize"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""size"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_center_crop"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""center_crop"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""do_flip_channel_order"""							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class.from_dict(self.image_processor_dict							)
						self.assertEqual(image_processor.size     ,					{"""shortest_edge""": 2_0}							)
						self.assertEqual(image_processor.crop_size     ,					{"""height""": 1_8, """width""": 1_8}							)

						SCREAMING_SNAKE_CASE__										=   self.image_processing_class.from_dict(self.image_processor_dict     ,					size=4_2     ,					crop_size=8_4							)
						self.assertEqual(image_processor.size     ,					{"""shortest_edge""": 4_2}							)
						self.assertEqual(image_processor.crop_size     ,					{"""height""": 8_4, """width""": 8_4}							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     Dict:
						pass

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     List[str]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						SCREAMING_SNAKE_CASE__										=   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					Image.Image							)

						# Test not batched input
						SCREAMING_SNAKE_CASE__										=   image_processing(image_inputs[0]     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Optional[int]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE__										=   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCAmelCase     ,					numpify=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					np.ndarray							)

						# Test not batched input
						SCREAMING_SNAKE_CASE__										=   image_processing(image_inputs[0]     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)





			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     str:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE__										=   self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE__										=   prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCAmelCase     ,					torchify=__UpperCAmelCase							)
						for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase     ,					torch.Tensor							)

						# Test not batched input
						SCREAMING_SNAKE_CASE__										=   image_processing(image_inputs[0]     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   image_processing(__UpperCAmelCase     ,					return_tensors="""pt"""							).pixel_values
						self.assertEqual(
						    encoded_images.shape     ,					(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )     ,					)


