





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       A__							(   SCREAMING_SNAKE_CASE__)       ->   Any:
  __snake_case:						str			  =						checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__)
  __snake_case:						int			  =						flatten_dict(SCREAMING_SNAKE_CASE__)
  return flax_params







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   str:
  __snake_case:						str			  =						{}

  __snake_case:						List[Any]			  =						{
      """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""",
  }

  __snake_case:						Dict			  =						{
      """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
      __snake_case:						List[Any]			  =						""".""".join(key[1:])

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        __snake_case:						Dict			  =						new_key.replace(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          __snake_case:						int			  =						new_key.replace(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        __snake_case:						int			  =						re.sub(r"""layers_(\d+)""" , r"""layer.\1""" , SCREAMING_SNAKE_CASE__)
        __snake_case:						Union[str, Any]			  =						new_key.replace("""encoder""" , """encoder.encoder""")

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

      __snake_case:						Optional[Any]			  =						flax_dict[key]

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

  return converted_torch_dict







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False)       ->   int:
  __snake_case:						Dict			  =						get_flax_param(SCREAMING_SNAKE_CASE__)

  if not use_large:
    __snake_case:						Optional[Any]			  =						PixaStructVisionConfig()
    __snake_case:						Optional[int]			  =						PixaStructTextConfig()
  else:
    __snake_case:						Optional[Any]			  =						PixaStructVisionConfig(
        hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18)
    __snake_case:						Optional[Any]			  =						PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18)
  __snake_case:						str			  =						PixaStructConfig(
      vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=SCREAMING_SNAKE_CASE__)

  __snake_case:						Union[str, Any]			  =						PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE__)

  __snake_case:						Optional[Any]			  =						rename_and_convert_flax_params(SCREAMING_SNAKE_CASE__)
  model.load_state_dict(SCREAMING_SNAKE_CASE__)

  __snake_case:						Tuple			  =						AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer""")
  __snake_case:						Union[str, Any]			  =						PixaStructImageProcessor()
  __snake_case:						Union[str, Any]			  =						PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__)

  if use_large:
    __snake_case:						Dict			  =						4096

  __snake_case:						Tuple			  =						True

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

  model.save_pretrained(SCREAMING_SNAKE_CASE__)
  processor.save_pretrained(SCREAMING_SNAKE_CASE__)

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


if __name__ == "__main__":
      __UpperCAmelCase	:       Dict    	=							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.")
      __UpperCAmelCase	:       List[str]    	=							parser.parse_args()

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













import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset

__UpperCAmelCase	:       Any    	=							random.Random()







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=1.0 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None)       ->   Any:
  if rng is None:
    __snake_case:						Dict			  =						global_rng

  __snake_case:						str			  =						[]
  for batch_idx in range(shape[0]):
    values.append([])
    for _ in range(shape[1]):
      values[-1].append(rng.random() * scale)

  return values







class    __snake_case	(							unittest.TestCase      ):





       '''simple docstring'''







       def __init__(  self			:   int							,    A			:   List[str]							,    A			:   List[Any]=7							,    A			:   Optional[int]=400							,    A			:   List[Any]=2_000							,    A			:   Dict=2_048							,    A			:   Tuple=128							,    A			:   List[Any]=1							,    A			:   Tuple=512							,    A			:   str=30							,    A			:   Optional[Any]=44_100							,    ):
         __snake_case:						Dict			  =						parent
         __snake_case:						Optional[Any]			  =						batch_size
         __snake_case:						Optional[int]			  =						min_seq_length
         __snake_case:						Optional[Any]			  =						max_seq_length
         __snake_case:						List[str]			  =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         __snake_case:						Any			  =						spectrogram_length
         __snake_case:						Any			  =						feature_size
         __snake_case:						Union[str, Any]			  =						num_audio_channels
         __snake_case:						Any			  =						hop_length
         __snake_case:						List[str]			  =						chunk_length
         __snake_case:						Any			  =						sampling_rate




       def 							UpperCAmelCase__			(  self			:   List[Any]		):
         return {
             "spectrogram_length": self.spectrogram_length,
             "feature_size": self.feature_size,
             "num_audio_channels": self.num_audio_channels,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "sampling_rate": self.sampling_rate,
         }






       def 							UpperCAmelCase__			(  self			:   List[str]							,    A			:   str=False							,    A			:   int=False		):
         def _flatten(A			:   Dict		):
           return list(itertools.chain(*A		)		)

         if equal_length:
           __snake_case:						List[str]			  =						[floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
         else:
           # make sure that inputs increase in size
           __snake_case:						int			  =						[
               floats_list((x, self.feature_size)		)
               for x in range(self.min_seq_length							,    self.max_seq_length							,    self.seq_length_diff		)
           ]
         if numpify:
           __snake_case:						Tuple			  =						[np.asarray(A		) for x in speech_inputs]
         return speech_inputs







@require_torch
@require_torchaudio
class    __snake_case	(							__lowerCamelCase       ,      unittest.TestCase      ):





       '''simple docstring'''







       lowerCAmelCase__						=					TvltFeatureExtractor




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




       def 							UpperCAmelCase__			(  self			:   int		):
         __snake_case:						Tuple			  =						self.feature_extraction_class(**self.feat_extract_dict		)
         self.assertTrue(hasattr(A							,    """spectrogram_length"""		)		)
         self.assertTrue(hasattr(A							,    """feature_size"""		)		)
         self.assertTrue(hasattr(A							,    """num_audio_channels"""		)		)
         self.assertTrue(hasattr(A							,    """hop_length"""		)		)
         self.assertTrue(hasattr(A							,    """chunk_length"""		)		)
         self.assertTrue(hasattr(A							,    """sampling_rate"""		)		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         __snake_case:						Optional[Any]			  =						self.feature_extraction_class(**self.feat_extract_dict		)

         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						Tuple			  =						feat_extract_first.save_pretrained(A		)[0]
           check_json_file_has_correct_format(A		)
           __snake_case:						int			  =						self.feature_extraction_class.from_pretrained(A		)

         __snake_case:						List[str]			  =						feat_extract_first.to_dict()
         __snake_case:						str			  =						feat_extract_second.to_dict()
         __snake_case:						List[Any]			  =						dict_first.pop("""mel_filters"""		)
         __snake_case:						str			  =						dict_second.pop("""mel_filters"""		)
         self.assertTrue(np.allclose(A							,    A		)		)
         self.assertEqual(A							,    A		)




       def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
         __snake_case:						str			  =						self.feature_extraction_class(**self.feat_extract_dict		)

         with tempfile.TemporaryDirectory() as tmpdirname:
           __snake_case:						str			  =						os.path.join(A							,    """feat_extract.json"""		)
           feat_extract_first.to_json_file(A		)
           __snake_case:						List[Any]			  =						self.feature_extraction_class.from_json_file(A		)

         __snake_case:						Dict			  =						feat_extract_first.to_dict()
         __snake_case:						Any			  =						feat_extract_second.to_dict()
         __snake_case:						int			  =						dict_first.pop("""mel_filters"""		)
         __snake_case:						int			  =						dict_second.pop("""mel_filters"""		)
         self.assertTrue(np.allclose(A							,    A		)		)
         self.assertEqual(A							,    A		)




       def 							UpperCAmelCase__			(  self			:   Any		):
         # Initialize feature_extractor
         __snake_case:						Union[str, Any]			  =						self.feature_extraction_class(**self.feat_extract_dict		)

         # create three inputs of length 800, 1000, and 1200
         __snake_case:						Dict			  =						[floats_list((1, x)		)[0] for x in range(800							,    1_400							,    200		)]
         __snake_case:						str			  =						[np.asarray(A		) for speech_input in speech_inputs]

         # Test not batched input
         __snake_case:						int			  =						feature_extractor(np_speech_inputs[0]							,    return_tensors="""np"""							,    sampling_rate=44_100		).audio_values

         self.assertTrue(encoded_audios.ndim == 4		)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

         # Test batched
         __snake_case:						Optional[int]			  =						feature_extractor(A							,    return_tensors="""np"""							,    sampling_rate=44_100		).audio_values

         self.assertTrue(encoded_audios.ndim == 4		)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

         # Test audio masking
         __snake_case:						Union[str, Any]			  =						feature_extractor(
             A							,    return_tensors="""np"""							,    sampling_rate=44_100							,    mask_audio=A		).audio_values

         self.assertTrue(encoded_audios.ndim == 4		)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

         # Test 2-D numpy arrays are batched.
         __snake_case:						Any			  =						[floats_list((1, x)		)[0] for x in (800, 800, 800)]
         __snake_case:						Union[str, Any]			  =						np.asarray(A		)
         __snake_case:						List[Any]			  =						feature_extractor(A							,    return_tensors="""np"""							,    sampling_rate=44_100		).audio_values
         self.assertTrue(encoded_audios.ndim == 4		)
         self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
         self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
         self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)




       def 							UpperCAmelCase__			(  self			:   Union[str, Any]							,    A			:   List[str]		):
         __snake_case:						Tuple			  =						load_dataset("""hf-internal-testing/librispeech_asr_dummy"""							,    """clean"""							,    split="""validation"""		)
         # automatic decoding with librispeech
         __snake_case:						List[Any]			  =						ds.sort("""id"""		).select(range(A		)		)[:num_samples]["""audio"""]

         return [x["array"] for x in speech_samples]






       def 							UpperCAmelCase__			(  self			:   Optional[int]		):
         __snake_case:						Dict			  =						self._load_datasamples(1		)
         __snake_case:						Optional[int]			  =						TvltFeatureExtractor()
         __snake_case:						Optional[Any]			  =						feature_extractor(A							,    return_tensors="""pt"""		).audio_values

         self.assertEquals(audio_values.shape							,    (1, 1, 192, 128)		)

         __snake_case:						str			  =						torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
         self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]							,    A							,    atol=1E-4		)		)






