


"""simple docstring"""



import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor
class 					__magic_name__		:






       def __init__(   self  :    Any  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Union[str, Any]=1_3  ,     snake_case__  :    Tuple=3_0  ,     snake_case__  :    str=2  ,     snake_case__  :    Union[str, Any]=3  ,     snake_case__  :    Union[str, Any]=True  ,     snake_case__  :    Union[str, Any]=True  ,     snake_case__  :    Optional[Any]=3_2  ,     snake_case__  :    Any=5  ,     snake_case__  :    int=4  ,     snake_case__  :    Dict=3_7  ,     snake_case__  :    Dict="gelu"  ,     snake_case__  :    Union[str, Any]=0.1  ,     snake_case__  :    Union[str, Any]=0.1  ,     snake_case__  :    List[Any]=1_0  ,     snake_case__  :    int=0.02  ,     snake_case__  :    List[str]=None  ,     ):

              '''simple docstring'''






              lowercase		:List[str]       							=				parent
              lowercase		:Optional[Any]       							=				batch_size
              lowercase		:int       							=				image_size
              lowercase		:Union[str, Any]       							=				patch_size
              lowercase		:Dict       							=				num_channels
              lowercase		:int       							=				is_training
              lowercase		:List[Any]       							=				use_labels
              lowercase		:int       							=				hidden_size
              lowercase		:Tuple       							=				num_hidden_layers
              lowercase		:Tuple       							=				num_attention_heads
              lowercase		:Optional[Any]       							=				intermediate_size
              lowercase		:List[Any]       							=				hidden_act
              lowercase		:str       							=				hidden_dropout_prob
              lowercase		:Any       							=				attention_probs_dropout_prob
              lowercase		:Dict       							=				type_sequence_label_size
              lowercase		:Dict       							=				initializer_range
              lowercase		:int       							=				scope

              # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
              lowercase		:Tuple       							=				(image_size // patch_size) ** 2
              lowercase		:int       							=				num_patches + 1






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

              lowercase		:Optional[Any]       							=				None
              if self.use_labels:
                     lowercase		:List[str]       							=				ids_tensor([self.batch_size]  ,     self.type_sequence_label_size				)

              lowercase		:Union[str, Any]       							=				self.get_config()

              return config, pixel_values, labels






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              return ViTMSNConfig(
                  image_size=self.image_size  ,     patch_size=self.patch_size  ,     num_channels=self.num_channels  ,     hidden_size=self.hidden_size  ,     num_hidden_layers=self.num_hidden_layers  ,     num_attention_heads=self.num_attention_heads  ,     intermediate_size=self.intermediate_size  ,     hidden_act=self.hidden_act  ,     hidden_dropout_prob=self.hidden_dropout_prob  ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,     initializer_range=self.initializer_range  ,     )






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    Any  ,     snake_case__  :    str				):

              '''simple docstring'''






              lowercase		:int       							=				ViTMSNModel(config=snake_case__				)
              model.to(snake_case__				)
              model.eval()
              lowercase		:List[Any]       							=				model(snake_case__				)
              self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)				)






       def  __snake_case						(   self  :    int  ,     snake_case__  :    Dict  ,     snake_case__  :    Dict  ,     snake_case__  :    Tuple				):

              '''simple docstring'''






              lowercase		:Any       							=				self.type_sequence_label_size
              lowercase		:str       							=				ViTMSNForImageClassification(snake_case__				)
              model.to(snake_case__				)
              model.eval()
              lowercase		:List[str]       							=				model(snake_case__  ,     labels=snake_case__				)
              print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''				)
              print('''Labels: {labels}'''				)
              self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.type_sequence_label_size)				)

              # test greyscale images
              lowercase		:Any       							=				1
              lowercase		:Optional[int]       							=				ViTMSNForImageClassification(snake_case__				)
              model.to(snake_case__				)
              model.eval()

              lowercase		:Union[str, Any]       							=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
              lowercase		:List[Any]       							=				model(snake_case__				)
              self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.type_sequence_label_size)				)







       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:int       							=				self.prepare_config_and_inputs()
              lowercase							,  lowercase							,  lowercase		:Union[str, Any]       							=				config_and_inputs
              lowercase		:int       							=				{'''pixel_values''': pixel_values}
              return config, inputs_dict







@require_torch
class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Optional[int]        =   (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
       __A			:      Dict        =   (
           {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
           if is_torch_available()
           else {}
       )

       __A			:      List[str]        =   False
       __A			:      Dict        =   False
       __A			:      Any        =   False
       __A			:      Optional[Any]        =   False






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Any       							=				ViTMSNModelTester(self				)
              lowercase		:Tuple       							=				ConfigTester(self  ,     config_class=snake_case__  ,     has_text_modality=snake_case__  ,     hidden_size=3_7				)






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              self.config_tester.run_common_tests()






       @unittest.skip(reason='''ViTMSN does not use inputs_embeds'''				)
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase							,  lowercase		:Dict       							=				self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     lowercase		:List[str]       							=				model_class(snake_case__				)
                     self.assertIsInstance(model.get_input_embeddings()  ,     (nn.Module)				)
                     lowercase		:List[Any]       							=				model.get_output_embeddings()
                     self.assertTrue(x is None or isinstance(snake_case__  ,     nn.Linear				)				)






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase							,  lowercase		:int       							=				self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     lowercase		:List[Any]       							=				model_class(snake_case__				)
                     lowercase		:List[Any]       							=				inspect.signature(model.forward				)
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     lowercase		:Union[str, Any]       							=				[*signature.parameters.keys()]

                     lowercase		:str       							=				['''pixel_values''']
                     self.assertListEqual(arg_names[:1]  ,     snake_case__				)






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*snake_case__				)






       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Optional[Any]       							=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*snake_case__				)







       @slow
       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     lowercase		:List[str]       							=				ViTMSNModel.from_pretrained(snake_case__				)
                     self.assertIsNotNone(snake_case__				)







def       lowerCamelCase					()     ->							Optional[int]:
       lowercase		:str       							=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
       return image




@require_torch
@require_vision
class 					__magic_name__		(			unittest.TestCase     ):






       @cached_property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''				) if is_vision_available() else None







       @slow
       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              torch.manual_seed(2				)
              lowercase		:Optional[int]       							=				ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''				).to(snake_case__				)

              lowercase		:str       							=				self.default_image_processor
              lowercase		:str       							=				prepare_img()
              lowercase		:Any       							=				image_processor(images=snake_case__  ,     return_tensors='''pt'''				).to(snake_case__				)

              # forward pass
              with torch.no_grad():
                     lowercase		:Dict       							=				model(**snake_case__				)

              # verify the logits
              lowercase		:List[Any]       							=				torch.Size((1, 1_0_0_0)				)
              self.assertEqual(outputs.logits.shape  ,     snake_case__				)

              lowercase		:Dict       							=				torch.tensor([-0.08_03, -0.44_54, -0.23_75]				).to(snake_case__				)

              self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,     snake_case__  ,     atol=1e-4				)				)





"""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


UpperCAmelCase      		=			[
    # 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       lowerCamelCase					(a_						:Union[str, Any])     ->							Optional[Any]:
       for pegasus_name, hf_name in PATTERNS:
              lowercase		:List[Any]       							=				k.replace(a_				,				a_)
       return k






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






def       lowerCamelCase					(a_						:str				,				a_						:str)     ->							List[str]:
       # save tokenizer first
       lowercase		:List[str]       							=				Path(a_).parent.name
       lowercase		:List[Any]       							=				task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
       lowercase		:List[Any]       							=				PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''				,				model_max_length=a_)
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(a_)

       # convert model
       lowercase		:Any       							=				get_tf_weights_as_numpy(a_)
       lowercase		:str       							=				task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              lowercase		:int       							=				task_specific_params
       lowercase		:List[Any]       							=				convert_pegasus(a_				,				a_)
       torch_model.save_pretrained(a_)
       lowercase		:Union[str, Any]       							=				torch_model.state_dict()
       sd.pop('''model.decoder.embed_positions.weight''')
       sd.pop('''model.encoder.embed_positions.weight''')
       torch.save(a_				,				Path(a_) / '''pytorch_model.bin''')


if __name__ == "__main__":
 UpperCAmelCase      		=			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.''')
 UpperCAmelCase      		=			parser.parse_args()
 if args.save_dir is None:
  UpperCAmelCase      		=			Path(args.tf_ckpt_path).parent.name
  UpperCAmelCase      		=			os.path.join('''pegasus''', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)

