



"""simple docstring"""

import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ImageGPTImageProcessor




class  _UpperCAmelCase (    unittest.TestCase):

 def __init__(  self	: List[str]						,  lowercase_	: List[str]						,  lowercase_	: Dict=7						,  lowercase_	: Any=3						,  lowercase_	: Any=18						,  lowercase_	: Dict=30						,  lowercase_	: Tuple=400						,  lowercase_	: Any=True						,  lowercase_	: Optional[int]=None						,  lowercase_	: Optional[Any]=True						,  ):
     snake_case_				:  Optional[Any]       =			size if size is not None else {'''height''': 18, '''width''': 18}
     snake_case_				:  Optional[Any]       =			parent
     snake_case_				:  Optional[int]       =			batch_size
     snake_case_				:  Union[str, Any]       =			num_channels
     snake_case_				:  Union[str, Any]       =			image_size
     snake_case_				:  str       =			min_resolution
     snake_case_				:  Optional[int]       =			max_resolution
     snake_case_				:  Union[str, Any]       =			do_resize
     snake_case_				:  Dict       =			size
     snake_case_				:  Optional[Any]       =			do_normalize






 def 		_snake_case      (  self	: Dict       ):
     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                 [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
             ]       ),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }





@require_torch
@require_vision
class  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    str	      =	ImageGPTImageProcessor if is_vision_available() else None

 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  Any       =			ImageGPTImageProcessingTester(self       )

 @property
 def 		_snake_case      (  self	: Optional[Any]       ):
     return self.image_processor_tester.prepare_image_processor_dict()

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  List[Any]       =			self.image_processing_class(**self.image_processor_dict       )
     self.assertTrue(hasattr(lowercase_						,  '''clusters'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''do_resize'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''size'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''do_normalize'''       )       )

 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  int       =			self.image_processing_class.from_dict(self.image_processor_dict       )
     self.assertEqual(image_processor.size						,  {'''height''': 18, '''width''': 18}       )

     snake_case_				:  Dict       =			self.image_processing_class.from_dict(self.image_processor_dict						,  size=42       )
     self.assertEqual(image_processor.size						,  {'''height''': 42, '''width''': 42}       )

 def 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  Optional[int]       =			self.image_processing_class(**self.image_processor_dict       )
     snake_case_				:  str       =			json.loads(image_processor.to_json_string()       )
     for key, value in self.image_processor_dict.items():
         if key == "clusters":
             self.assertTrue(np.array_equal(lowercase_						,  obj[key]       )       )
         else:
             self.assertEqual(obj[key]						,  lowercase_       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Union[str, Any]       =			self.image_processing_class(**self.image_processor_dict       )

     with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case_				:  Optional[Any]       =			os.path.join(lowercase_						,  '''image_processor.json'''       )
         image_processor_first.to_json_file(lowercase_       )
         snake_case_				:  Optional[int]       =			self.image_processing_class.from_json_file(lowercase_       ).to_dict()

     snake_case_				:  Optional[Any]       =			image_processor_first.to_dict()
     for key, value in image_processor_first.items():
         if key == "clusters":
             self.assertTrue(np.array_equal(lowercase_						,  image_processor_second[key]       )       )
         else:
             self.assertEqual(image_processor_first[key]						,  lowercase_       )

 def 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  Optional[int]       =			self.image_processing_class(**self.image_processor_dict       )

     with tempfile.TemporaryDirectory() as tmpdirname:
         image_processor_first.save_pretrained(lowercase_       )
         snake_case_				:  Any       =			self.image_processing_class.from_pretrained(lowercase_       ).to_dict()

     snake_case_				:  Tuple       =			image_processor_first.to_dict()
     for key, value in image_processor_first.items():
         if key == "clusters":
             self.assertTrue(np.array_equal(lowercase_						,  image_processor_second[key]       )       )
         else:
             self.assertEqual(image_processor_first[key]						,  lowercase_       )






 @unittest.skip('''ImageGPT requires clusters at initialization'''       )
 def 		_snake_case      (  self	: Dict       ):
     pass





def __lowercase    (							):
    snake_case_				:  List[str]       =			load_dataset('''hf-internal-testing/fixtures_image_utils'''	,    split='''test'''					)

    snake_case_				:  Tuple       =			Image.open(dataset[4]['''file''']					)
    snake_case_				:  List[str]       =			Image.open(dataset[5]['''file''']					)

    snake_case_				:  List[Any]       =			[imagea, imagea]

    return images






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

 @slow
 def 		_snake_case      (  self	: str       ):
     snake_case_				:  Optional[Any]       =			ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''       )

     snake_case_				:  List[Any]       =			prepare_images()

     # test non-batched
     snake_case_				:  Dict       =			image_processing(images[0]						,  return_tensors='''pt'''       )

     self.assertIsInstance(encoding.input_ids						,  torch.LongTensor       )
     self.assertEqual(encoding.input_ids.shape						,  (1, 1024)       )

     snake_case_				:  Optional[Any]       =			[306, 191, 191]
     self.assertEqual(encoding.input_ids[0, :3].tolist()						,  lowercase_       )

     # test batched
     snake_case_				:  List[Any]       =			image_processing(lowercase_						,  return_tensors='''pt'''       )

     self.assertIsInstance(encoding.input_ids						,  torch.LongTensor       )
     self.assertEqual(encoding.input_ids.shape						,  (2, 1024)       )

     snake_case_				:  Tuple       =			[303, 13, 13]
     self.assertEqual(encoding.input_ids[1, -3:].tolist()						,  lowercase_       )










"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  Dict       =			'''TransientGlobalSelfAttention'''
    else:
        raise ValueError(
            '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
            ''' attribute with a value from [\'local\', \'transient-global].'''					)

    # Encoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Union[str, Any]       =			tax_global_layer_norm

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Any       =			tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        snake_case_				:  Any       =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        snake_case_				:  Optional[Any]       =			tax_enc_dec_attention_module['''key''']['''kernel''']
        snake_case_				:  str       =			tax_enc_dec_attention_module['''out''']['''kernel''']
        snake_case_				:  Union[str, Any]       =			tax_enc_dec_attention_module['''query''']['''kernel''']
        snake_case_				:  List[str]       =			tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  Dict       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

        if split_mlp_wi:
            snake_case_				:  Tuple       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  List[Any]       =			tax_mlp_wi

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

    flax_model.save_pretrained(_a					)
    print('''T5X Model was sucessfully converted!'''					)


if __name__ == "__main__":
     lowercase__					:						Tuple												=			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     lowercase__					:						Dict												=			parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





