

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
 import os

 import jax.numpy as jnp
 from jax import jit

 from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
 from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

 SCREAMING_SNAKE_CASE		:Any						=     """0.12"""  # assumed parallelism: 8


if is_torch_available():
 import torch




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			)->						List[Any]:



    """simple docstring"""






    if rng is None:
        UpperCamelCase_			  =						random.Random()

    UpperCamelCase_			  =						1
    for dim in shape:
        total_dims *= dim

    UpperCamelCase_			  =						[]
    for _ in range(SCREAMING_SNAKE_CASE_			):
        values.append(rng.randint(0			,						vocab_size - 1			)			)

    UpperCamelCase_			  =						np.array(SCREAMING_SNAKE_CASE_			,						dtype=jnp.intaa			).reshape(SCREAMING_SNAKE_CASE_			)

    return output




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						ids_tensor(SCREAMING_SNAKE_CASE_			,						vocab_size=2			,						rng=SCREAMING_SNAKE_CASE_			)
    # make sure that at least one token is attended to for each batch
    UpperCamelCase_			  =						1
    return attn_mask






@require_flax
class 						__magic_name__					:
     UpperCamelCase_	:List[str]										=      None
     UpperCamelCase_	:List[str]										=      ()






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_  ,       UpperCamelCase_			  =						self.model_tester.prepare_config_and_inputs_for_common()

         # cut to half length & take max batch_size 3
         UpperCamelCase_			  =						2
         UpperCamelCase_			  =						inputs["input_ids"].shape[-1] // 2
         UpperCamelCase_			  =						inputs["input_ids"][:max_batch_size, :sequence_length]

         UpperCamelCase_			  =						jnp.ones_like(_lowercase	)
         UpperCamelCase_			  =						attention_mask[:max_batch_size, :sequence_length]

         # generate max 5 tokens
         UpperCamelCase_			  =						input_ids.shape[-1] + 5
         if config.eos_token_id is not None and config.pad_token_id is None:
             # hack to allow generate for models such as GPT2 as is done in `generate()`
             UpperCamelCase_			  =						config.eos_token_id
         return config, input_ids, attention_mask, max_length






     @is_pt_flax_cross_test
     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						self._get_input_ids_and_config()
         UpperCamelCase_			  =						False
         UpperCamelCase_			  =						max_length
         UpperCamelCase_			  =						0

         for model_class in self.all_generative_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model_class.__name__[4:]  # Skip the "Flax" at the beginning
             UpperCamelCase_			  =						getattr(_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						pt_model_class(_lowercase	).eval()
             UpperCamelCase_			  =						load_flax_weights_in_pytorch_model(_lowercase		,						flax_model.params	)

             UpperCamelCase_			  =						flax_model.generate(_lowercase	).sequences
             UpperCamelCase_			  =						pt_model.generate(torch.tensor(_lowercase		,						dtype=torch.long	)	)

             if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                 UpperCamelCase_			  =						flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

             self.assertListEqual(pt_generation_outputs.numpy().tolist()		,						flax_generation_outputs.tolist()	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						self._get_input_ids_and_config()
         UpperCamelCase_			  =						False
         UpperCamelCase_			  =						max_length

         for model_class in self.all_generative_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model.generate(_lowercase	).sequences
             self.assertEqual(generation_outputs.shape[-1]		,						_lowercase	)

             UpperCamelCase_			  =						jit(model.generate	)
             UpperCamelCase_			  =						jit_generate(_lowercase	).sequences

             self.assertListEqual(generation_outputs.tolist()		,						jit_generation_outputs.tolist()	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						self._get_input_ids_and_config()
         UpperCamelCase_			  =						True
         UpperCamelCase_			  =						max_length

         for model_class in self.all_generative_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model.generate(_lowercase	).sequences
             self.assertEqual(generation_outputs.shape[-1]		,						_lowercase	)

             UpperCamelCase_			  =						jit(model.generate	)
             UpperCamelCase_			  =						jit_generate(_lowercase	).sequences

             self.assertListEqual(generation_outputs.tolist()		,						jit_generation_outputs.tolist()	)






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						self._get_input_ids_and_config()
         UpperCamelCase_			  =						False
         UpperCamelCase_			  =						max_length
         UpperCamelCase_			  =						2

         for model_class in self.all_generative_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model.generate(_lowercase	).sequences
             self.assertEqual(generation_outputs.shape[-1]		,						_lowercase	)

             UpperCamelCase_			  =						jit(model.generate	)
             UpperCamelCase_			  =						jit_generate(_lowercase	).sequences

             self.assertListEqual(generation_outputs.tolist()		,						jit_generation_outputs.tolist()	)






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						self._get_input_ids_and_config()
         UpperCamelCase_			  =						False
         UpperCamelCase_			  =						max_length
         UpperCamelCase_			  =						2
         UpperCamelCase_			  =						2

         for model_class in self.all_generative_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model.generate(_lowercase	).sequences
             self.assertEqual(generation_outputs.shape[0]		,						input_ids.shape[0] * config.num_return_sequences	)






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						self._get_input_ids_and_config()
         UpperCamelCase_			  =						True
         UpperCamelCase_			  =						max_length
         UpperCamelCase_			  =						0.8
         UpperCamelCase_			  =						10
         UpperCamelCase_			  =						0.3
         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						8
         UpperCamelCase_			  =						9

         for model_class in self.all_generative_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model.generate(_lowercase	).sequences
             self.assertEqual(generation_outputs.shape[-1]		,						_lowercase	)

             UpperCamelCase_			  =						jit(model.generate	)
             UpperCamelCase_			  =						jit_generate(_lowercase	).sequences

             self.assertListEqual(generation_outputs.tolist()		,						jit_generation_outputs.tolist()	)






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						self._get_input_ids_and_config()
         UpperCamelCase_			  =						max_length
         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						8
         UpperCamelCase_			  =						9

         for model_class in self.all_generative_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model.generate(_lowercase	).sequences
             self.assertEqual(generation_outputs.shape[-1]		,						_lowercase	)

             UpperCamelCase_			  =						jit(model.generate	)
             UpperCamelCase_			  =						jit_generate(_lowercase	).sequences

             self.assertListEqual(generation_outputs.tolist()		,						jit_generation_outputs.tolist()	)






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						self._get_input_ids_and_config()
         UpperCamelCase_			  =						max_length
         UpperCamelCase_			  =						2
         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						8
         UpperCamelCase_			  =						9

         for model_class in self.all_generative_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model.generate(_lowercase	).sequences
             self.assertEqual(generation_outputs.shape[-1]		,						_lowercase	)

             UpperCamelCase_			  =						jit(model.generate	)
             UpperCamelCase_			  =						jit_generate(_lowercase	).sequences

             self.assertListEqual(generation_outputs.tolist()		,						jit_generation_outputs.tolist()	)






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						self._get_input_ids_and_config()

         # pad attention mask on the left
         UpperCamelCase_			  =						attention_mask.at[(0, 0)].set(0	)

         UpperCamelCase_			  =						False
         UpperCamelCase_			  =						max_length

         for model_class in self.all_generative_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model.generate(_lowercase		,						attention_mask=_lowercase	).sequences
             self.assertEqual(generation_outputs.shape[-1]		,						_lowercase	)

             UpperCamelCase_			  =						jit(model.generate	)
             UpperCamelCase_			  =						jit_generate(_lowercase		,						attention_mask=_lowercase	).sequences

             self.assertListEqual(generation_outputs.tolist()		,						jit_generation_outputs.tolist()	)






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						self._get_input_ids_and_config()

         # pad attention mask on the left
         UpperCamelCase_			  =						attention_mask.at[(0, 0)].set(0	)

         UpperCamelCase_			  =						True
         UpperCamelCase_			  =						max_length

         for model_class in self.all_generative_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model.generate(_lowercase		,						attention_mask=_lowercase	).sequences
             self.assertEqual(generation_outputs.shape[-1]		,						_lowercase	)

             UpperCamelCase_			  =						jit(model.generate	)
             UpperCamelCase_			  =						jit_generate(_lowercase		,						attention_mask=_lowercase	).sequences

             self.assertListEqual(generation_outputs.tolist()		,						jit_generation_outputs.tolist()	)






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						self._get_input_ids_and_config()

         # pad attention mask on the left
         UpperCamelCase_			  =						attention_mask.at[(0, 0)].set(0	)

         UpperCamelCase_			  =						2
         UpperCamelCase_			  =						max_length

         for model_class in self.all_generative_model_classes:
             UpperCamelCase_			  =						model_class(_lowercase	)

             UpperCamelCase_			  =						model.generate(_lowercase		,						attention_mask=_lowercase	).sequences
             self.assertEqual(generation_outputs.shape[-1]		,						_lowercase	)

             UpperCamelCase_			  =						jit(model.generate	)
             UpperCamelCase_			  =						jit_generate(_lowercase		,						attention_mask=_lowercase	).sequences

             self.assertListEqual(generation_outputs.tolist()		,						jit_generation_outputs.tolist()	)






@require_flax
class 						__magic_name__					(							unittest.TestCase				):






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"	)
         UpperCamelCase_			  =						FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"	)

         UpperCamelCase_			  =						"Hello world"
         UpperCamelCase_			  =						tokenizer(_lowercase		,						return_tensors="np"	).input_ids

         # typos are quickly detected (the correct argument is `do_sample`)
         with self.assertRaisesRegex(_lowercase		,						"do_samples"	):
             model.generate(_lowercase		,						do_samples=_lowercase	)

         # arbitrary arguments that will not be used anywhere are also not accepted
         with self.assertRaisesRegex(_lowercase		,						"foo"	):
             UpperCamelCase_			  =						{"foo": "bar"}
             model.generate(_lowercase		,						**_lowercase	)



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    UpperCamelCase_			  =						SwinvaConfig()
    UpperCamelCase_			  =						swinva_name.split("_"			)

    UpperCamelCase_			  =						name_split[1]
    if "to" in name_split[3]:
        UpperCamelCase_			  =						int(name_split[3][-3:]			)
    else:
        UpperCamelCase_			  =						int(name_split[3]			)
    if "to" in name_split[2]:
        UpperCamelCase_			  =						int(name_split[2][-2:]			)
    else:
        UpperCamelCase_			  =						int(name_split[2][6:]			)

    if model_size == "tiny":
        UpperCamelCase_			  =						9_6
        UpperCamelCase_			  =						(2, 2, 6, 2)
        UpperCamelCase_			  =						(3, 6, 1_2, 2_4)
    elif model_size == "small":
        UpperCamelCase_			  =						9_6
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(3, 6, 1_2, 2_4)
    elif model_size == "base":
        UpperCamelCase_			  =						1_2_8
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(4, 8, 1_6, 3_2)
    else:
        UpperCamelCase_			  =						1_9_2
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(6, 1_2, 2_4, 4_8)

    if "to" in swinva_name:
        UpperCamelCase_			  =						(1_2, 1_2, 1_2, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        UpperCamelCase_			  =						2_1_8_4_1
        UpperCamelCase_			  =						"huggingface/label-files"
        UpperCamelCase_			  =						"imagenet-22k-id2label.json"
        UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
        UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
        UpperCamelCase_			  =						idalabel
        UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    else:
        UpperCamelCase_			  =						1_0_0_0
        UpperCamelCase_			  =						"huggingface/label-files"
        UpperCamelCase_			  =						"imagenet-1k-id2label.json"
        UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
        UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
        UpperCamelCase_			  =						idalabel
        UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    UpperCamelCase_			  =						img_size
    UpperCamelCase_			  =						num_classes
    UpperCamelCase_			  =						embed_dim
    UpperCamelCase_			  =						depths
    UpperCamelCase_			  =						num_heads
    UpperCamelCase_			  =						window_size

    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    if "patch_embed.proj" in name:
        UpperCamelCase_			  =						name.replace("patch_embed.proj"			,						"embeddings.patch_embeddings.projection"			)
    if "patch_embed.norm" in name:
        UpperCamelCase_			  =						name.replace("patch_embed.norm"			,						"embeddings.norm"			)
    if "layers" in name:
        UpperCamelCase_			  =						"encoder." + name
    if "attn.proj" in name:
        UpperCamelCase_			  =						name.replace("attn.proj"			,						"attention.output.dense"			)
    if "attn" in name:
        UpperCamelCase_			  =						name.replace("attn"			,						"attention.self"			)
    if "norm1" in name:
        UpperCamelCase_			  =						name.replace("norm1"			,						"layernorm_before"			)
    if "norm2" in name:
        UpperCamelCase_			  =						name.replace("norm2"			,						"layernorm_after"			)
    if "mlp.fc1" in name:
        UpperCamelCase_			  =						name.replace("mlp.fc1"			,						"intermediate.dense"			)
    if "mlp.fc2" in name:
        UpperCamelCase_			  =						name.replace("mlp.fc2"			,						"output.dense"			)
    if "q_bias" in name:
        UpperCamelCase_			  =						name.replace("q_bias"			,						"query.bias"			)
    if "k_bias" in name:
        UpperCamelCase_			  =						name.replace("k_bias"			,						"key.bias"			)
    if "v_bias" in name:
        UpperCamelCase_			  =						name.replace("v_bias"			,						"value.bias"			)
    if "cpb_mlp" in name:
        UpperCamelCase_			  =						name.replace("cpb_mlp"			,						"continuous_position_bias_mlp"			)
    if name == "norm.weight":
        UpperCamelCase_			  =						"layernorm.weight"
    if name == "norm.bias":
        UpperCamelCase_			  =						"layernorm.bias"

    if "head" in name:
        UpperCamelCase_			  =						name.replace("head"			,						"classifier"			)
    else:
        UpperCamelCase_			  =						"swinv2." + name

    return name




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        UpperCamelCase_			  =						orig_state_dict.pop(SCREAMING_SNAKE_CASE_			)

        if "mask" in key:
            continue
        elif "qkv" in key:
            UpperCamelCase_			  =						key.split("."			)
            UpperCamelCase_			  =						int(key_split[1]			)
            UpperCamelCase_			  =						int(key_split[3]			)
            UpperCamelCase_			  =						model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                UpperCamelCase_			  =						val[:dim, :]
                UpperCamelCase_			  =						val[dim : dim * 2, :]
                UpperCamelCase_			  =						val[-dim:, :]
            else:
                UpperCamelCase_			  =						val[:dim]
                UpperCamelCase_			  =						val[
                    dim : dim * 2
                ]
                UpperCamelCase_			  =						val[-dim:]
        else:
            UpperCamelCase_			  =						val

    return orig_state_dict




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						timm.create_model(SCREAMING_SNAKE_CASE_			,						pretrained=SCREAMING_SNAKE_CASE_			)
    timm_model.eval()

    UpperCamelCase_			  =						get_swinva_config(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						SwinvaForImageClassification(SCREAMING_SNAKE_CASE_			)
    model.eval()

    UpperCamelCase_			  =						convert_state_dict(timm_model.state_dict()			,						SCREAMING_SNAKE_CASE_			)
    model.load_state_dict(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						"http://images.cocodataset.org/val2017/000000039769.jpg"

    UpperCamelCase_			  =						AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"			,						"-"			)			)			)
    UpperCamelCase_			  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,						stream=SCREAMING_SNAKE_CASE_			).raw			)
    UpperCamelCase_			  =						image_processor(images=SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			)

    UpperCamelCase_			  =						timm_model(inputs["pixel_values"]			)
    UpperCamelCase_			  =						model(**SCREAMING_SNAKE_CASE_			).logits

    assert torch.allclose(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						atol=1E-3			)

    print(f"Saving model {swinva_name} to {pytorch_dump_folder_path}"			)
    model.save_pretrained(SCREAMING_SNAKE_CASE_			)

    print(f"Saving image processor to {pytorch_dump_folder_path}"			)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE_			)

    model.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			,						organization="nandwalritik"			,						commit_message="Add model"			,						)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Tuple						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--swinv2_name""",
     default="""swinv2_tiny_patch4_window8_256""",
     type=str,
     help="""Name of the Swinv2 timm model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )

 SCREAMING_SNAKE_CASE		:int						=     parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
