


import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->str:
   UpperCAmelCase      					=  checkpoints.load_tax_checkpoint(lowerCAmelCase_ )
   UpperCAmelCase      					=  flatten_dict(lowerCAmelCase_ )
   return flax_params
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Optional[Any]:
   UpperCAmelCase      					=  {}

   UpperCAmelCase      					=  {
       """token_embedder""": """embeddings""",
       """encoder_norm""": """layernorm""",
       """kernel""": """weight""",
       """.out""": """.output""",
       """scale""": """weight""",
       """embedders_0.pos_embedding""": """row_embedder.weight""",
       """embedders_1.pos_embedding""": """column_embedder.weight""",
   }

   UpperCAmelCase      					=  {
       """query""": """attention.query""",
       """key""": """attention.key""",
       """value""": """attention.value""",
       """output.dense""": """output""",
       """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
       """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
       """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
       """mlp.""": """mlp.DenseReluDense.""",
       """pre_mlp_layer_norm""": """mlp.layer_norm""",
       """self_attention.o""": """self_attention.attention.o""",
       """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
       """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
       """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
       """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
   }

   for key in flax_dict.keys():
      if "target" in key:
         # remove the first prefix from the key
         UpperCAmelCase      					=  """.""".join(key[1:] )

         # rename the key
         for old, new in CONVERSION_MAPPING.items():
            UpperCAmelCase      					=  new_key.replace(lowerCAmelCase_	,    lowerCAmelCase_ )

         if "decoder" in new_key:
            for old, new in DECODER_CONVERSION_MAPPING.items():
               UpperCAmelCase      					=  new_key.replace(lowerCAmelCase_	,    lowerCAmelCase_ )

         if "layers" in new_key and "decoder" not in new_key:
            # use regex to replace the layer number
            UpperCAmelCase      					=  re.sub(R"""layers_(\d+)"""	,    R"""layer.\1"""	,    lowerCAmelCase_ )
            UpperCAmelCase      					=  new_key.replace("""encoder"""	,    """encoder.encoder""" )

         elif "layers" in new_key and "decoder" in new_key:
            # use regex to replace the layer number
            UpperCAmelCase      					=  re.sub(R"""layers_(\d+)"""	,    R"""layer.\1"""	,    lowerCAmelCase_ )

         UpperCAmelCase      					=  flax_dict[key]

   UpperCAmelCase      					=  {}
   # convert converted_dict into torch format
   for key in converted_dict.keys():
      if ("embed_tokens" not in key) and ("embedder" not in key):
         UpperCAmelCase      					=  torch.from_numpy(converted_dict[key].T )
      else:
         UpperCAmelCase      					=  torch.from_numpy(converted_dict[key] )

   return converted_torch_dict
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_=False	,    lowerCAmelCase_=False )    ->List[str]:
   UpperCAmelCase      					=  get_flax_param(lowerCAmelCase_ )

   if not use_large:
      UpperCAmelCase      					=  PixaStructVisionConfig()
      UpperCAmelCase      					=  PixaStructTextConfig()
   else:
      UpperCAmelCase      					=  PixaStructVisionConfig(
          hidden_size=1_5_3_6	,    d_ff=3_9_6_8	,    num_attention_heads=2_4	,    num_hidden_layers=1_8 )
      UpperCAmelCase      					=  PixaStructTextConfig(hidden_size=1_5_3_6	,    d_ff=3_9_6_8	,    num_heads=2_4	,    num_layers=1_8 )
   UpperCAmelCase      					=  PixaStructConfig(
       vision_config=encoder_config.to_dict()	,    text_config=decoder_config.to_dict()	,    is_vqa=lowerCAmelCase_ )

   UpperCAmelCase      					=  PixaStructForConditionalGeneration(lowerCAmelCase_ )

   UpperCAmelCase      					=  rename_and_convert_flax_params(lowerCAmelCase_ )
   model.load_state_dict(lowerCAmelCase_ )

   UpperCAmelCase      					=  AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer""" )
   UpperCAmelCase      					=  PixaStructImageProcessor()
   UpperCAmelCase      					=  PixaStructProcessor(image_processor=lowerCAmelCase_	,    tokenizer=lowerCAmelCase_ )

   if use_large:
      UpperCAmelCase      					=  4_0_9_6

   UpperCAmelCase      					=  True

   # mkdir if needed
   os.makedirs(lowerCAmelCase_	,    exist_ok=lowerCAmelCase_ )

   model.save_pretrained(lowerCAmelCase_ )
   processor.save_pretrained(lowerCAmelCase_ )

   print("""Model saved in {}""".format(lowerCAmelCase_ ) )


if __name__ == "__main__":
 __a	 =      argparse.ArgumentParser()
 parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
 parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
 __a	 =      parser.parse_args()

 convert_pixastruct_original_pytorch_checkpoint_to_hf(
     args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
 )







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 	__lowercase					:





      def __init__(		self    :       str       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Optional[int]=3       ,							__lowerCamelCase    :       Union[str, Any]=3_2       ,							__lowerCamelCase    :       List[str]=3       ,							__lowerCamelCase    :       Dict=1_0       ,							__lowerCamelCase    :       Optional[int]=[1_0, 2_0, 3_0, 4_0]       ,							__lowerCamelCase    :       Optional[int]=[1, 1, 2, 1]       ,							__lowerCamelCase    :       int=True       ,							__lowerCamelCase    :       str=True       ,							__lowerCamelCase    :       str="relu"       ,							__lowerCamelCase    :       str=3       ,							__lowerCamelCase    :       int=None       ,							)     -> Dict:






         """simple docstring"""







         UpperCAmelCase      					=  parent
         UpperCAmelCase      					=  batch_size
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  embeddings_size
         UpperCAmelCase      					=  hidden_sizes
         UpperCAmelCase      					=  depths
         UpperCAmelCase      					=  is_training
         UpperCAmelCase      					=  use_labels
         UpperCAmelCase      					=  hidden_act
         UpperCAmelCase      					=  num_labels
         UpperCAmelCase      					=  scope
         UpperCAmelCase      					=  len(__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[str]							)     -> Optional[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         UpperCAmelCase      					=  None
         if self.use_labels:
            UpperCAmelCase      					=  ids_tensor([self.batch_size]       ,							self.num_labels							)

         UpperCAmelCase      					=  self.get_config()

         return config, pixel_values, labels





      def 							_lowercase					(		self    :       List[Any]							)     -> Dict:






         """simple docstring"""







         return ResNetConfig(
             num_channels=self.num_channels       ,							embeddings_size=self.embeddings_size       ,							hidden_sizes=self.hidden_sizes       ,							depths=self.depths       ,							hidden_act=self.hidden_act       ,							num_labels=self.num_labels       ,							image_size=self.image_size       ,							)





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       List[str]       ,							__lowerCamelCase    :       Tuple							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetModel(config=__lowerCamelCase							)
         UpperCAmelCase      					=  model(__lowerCamelCase							)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)       ,							)





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       Any       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       int							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.num_labels
         UpperCAmelCase      					=  TFResNetForImageClassification(__lowerCamelCase							)
         UpperCAmelCase      					=  model(__lowerCamelCase       ,							labels=__lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)							)





      def 							_lowercase					(		self    :       Optional[int]							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  self.prepare_config_and_inputs()
         UpperCAmelCase					,	UpperCAmelCase					,	UpperCAmelCase      					=  config_and_inputs
         UpperCAmelCase      					=  {"""pixel_values""": pixel_values}
         return config, inputs_dict






@require_tf
class 	__lowercase					(	__snake_case			,       __snake_case			,       unittest.TestCase	):
      UpperCamelCase										=							(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
      UpperCamelCase										=							(
          {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
          if is_tf_available()
          else {}
      )

      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False
      UpperCamelCase										=							False





      def 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetModelTester(self							)
         UpperCAmelCase      					=  ConfigTester(self       ,							config_class=__lowerCamelCase       ,							has_text_modality=__lowerCamelCase							)





      def 							_lowercase					(		self    :       str							)     -> Dict:






         """simple docstring"""







         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()





      def 							_lowercase					(		self    :       str							)     -> Dict:






         """simple docstring"""







         return





      @unittest.skip(reason="""ResNet does not use inputs_embeds"""							)
      def 							_lowercase					(		self    :       Optional[int]							)     -> Optional[int]:






         """simple docstring"""







         pass





      @unittest.skip(reason="""ResNet does not support input and output embeddings"""							)
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Tuple:






         """simple docstring"""







         pass





      def 							_lowercase					(		self    :       Optional[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            UpperCAmelCase      					=  inspect.signature(model.call							)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase      					=  [*signature.parameters.keys()]

            UpperCAmelCase      					=  ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       str							)     -> List[Any]:






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[Any]							)     -> List[str]:






         """simple docstring"""







         def check_hidden_states_output(__lowerCamelCase    :       str       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       Union[str, Any]							):
            UpperCAmelCase      					=  model_class(__lowerCamelCase							)
            UpperCAmelCase      					=  model(**self._prepare_for_class(__lowerCamelCase       ,							__lowerCamelCase							)							)

            UpperCAmelCase      					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase      					=  self.model_tester.num_stages
            self.assertEqual(len(__lowerCamelCase							)       ,							expected_num_stages + 1							)

            # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]							)       ,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,							)

         UpperCAmelCase					,	UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase      					=  ["""basic""", """bottleneck"""]
         for model_class in self.all_model_classes:
            for layer_type in layers_type:
               UpperCAmelCase      					=  layer_type
               UpperCAmelCase      					=  True
               check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCAmelCase      					=  True

               check_hidden_states_output(__lowerCamelCase       ,							__lowerCamelCase       ,							__lowerCamelCase							)





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> int:






         """simple docstring"""







         UpperCAmelCase      					=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase							)





      @slow
      def 							_lowercase					(		self    :       Tuple							)     -> List[str]:






         """simple docstring"""







         for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase      					=  TFResNetModel.from_pretrained(__lowerCamelCase							)
            self.assertIsNotNone(__lowerCamelCase							)

def 	_UpperCamelCase					(    )    ->int:
   UpperCAmelCase      					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







@require_tf
@require_vision
class 	__lowercase					(	unittest.TestCase	):





      @cached_property
      def 							_lowercase					(		self    :       List[Any]							)     -> Dict:






         """simple docstring"""







         return (
             AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
             if is_vision_available()
             else None
         )





      @slow
      def 							_lowercase					(		self    :       Dict							)     -> str:






         """simple docstring"""







         UpperCAmelCase      					=  TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

         UpperCAmelCase      					=  self.default_image_processor
         UpperCAmelCase      					=  prepare_img()
         UpperCAmelCase      					=  image_processor(images=__lowerCamelCase       ,							return_tensors="""tf"""							)

         # forward pass
         UpperCAmelCase      					=  model(**__lowerCamelCase							)

         # verify the logits
         UpperCAmelCase      					=  tf.TensorShape((1, 1_0_0_0)							)
         self.assertEqual(outputs.logits.shape       ,							__lowerCamelCase							)

         UpperCAmelCase      					=  tf.constant([-11.1_069, -9.7_877, -8.3_777]							)

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



