




'''simple docstring'''






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      return params[f"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE="attention"		)    ->   Union[str, Any]:
      snake_case__       :   Dict	      =    np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]		)
      snake_case__       :   Dict	      =    k_tmp.reshape(k_tmp.shape[0]				,      k_tmp.shape[1] * k_tmp.shape[2]		)
      snake_case__       :   Tuple	      =    np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]		)
      snake_case__       :   Union[str, Any]	      =    o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]				,      o_tmp.shape[2]		)
      snake_case__       :   Optional[Any]	      =    np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]		)
      snake_case__       :   List[Any]	      =    q_tmp.reshape(q_tmp.shape[0]				,      q_tmp.shape[1] * q_tmp.shape[2]		)
      snake_case__       :   str	      =    np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]		)
      snake_case__       :   Optional[Any]	      =    v_tmp.reshape(v_tmp.shape[0]				,      v_tmp.shape[1] * v_tmp.shape[2]		)
      return k, o, q, v






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=False		)    ->   List[str]:


      if split_mlp_wi:
            snake_case__       :   Any	      =    params[f"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
            snake_case__       :   str	      =    params[f"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
            snake_case__       :   int	      =    (wi_a, wi_a)
      else:
            snake_case__       :   str	      =    params[f"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

      snake_case__       :   Optional[int]	      =    params[f"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
      return wi, wo






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   List[str]:
      return params[f"{prefix}/{prefix}/{layer_name}/scale"][:, i]






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      *, __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE = False		)    ->   Dict:
      snake_case__       :   Optional[Any]	      =    traverse_util.flatten_dict(variables['target']		)
      snake_case__       :   Optional[Any]	      =    {'''/'''.join(_lowercase		): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      snake_case__       :   List[str]	      =    '''encoder/encoder/mlp/wi_0/kernel''' in old
      print('Split MLP:'				,      _lowercase		)

      snake_case__       :   int	      =    collections.OrderedDict()

      # Shared embeddings.
      snake_case__       :   Optional[Any]	      =    old['''token_embedder/embedding''']

      # Encoder.
      for i in range(_lowercase		):
            # Block i, layer 0 (Self Attention).
            snake_case__       :   str	      =    tax_layer_norm_lookup(_lowercase				,      _lowercase				,      'encoder'				,      'pre_attention_layer_norm'		)
            snake_case__       :   List[str]	      =    tax_attention_lookup(_lowercase				,      _lowercase				,      'encoder'				,      'attention'		)
            snake_case__       :   Optional[Any]	      =    layer_norm
            snake_case__       :   str	      =    k.T
            snake_case__       :   Tuple	      =    o.T
            snake_case__       :   Union[str, Any]	      =    q.T
            snake_case__       :   Dict	      =    v.T

            # Block i, layer 1 (MLP).
            snake_case__       :   str	      =    tax_layer_norm_lookup(_lowercase				,      _lowercase				,      'encoder'				,      'pre_mlp_layer_norm'		)
            snake_case__       :   Dict	      =    tax_mlp_lookup(_lowercase				,      _lowercase				,      'encoder'				,      _lowercase		)
            snake_case__       :   List[Any]	      =    layer_norm
            if split_mlp_wi:
                  snake_case__       :   int	      =    wi[0].T
                  snake_case__       :   str	      =    wi[1].T
            else:
                  snake_case__       :   Optional[int]	      =    wi.T
            snake_case__       :   List[Any]	      =    wo.T
            if scalable_attention:
                  # convert the rel_embedding of each layer
                  snake_case__       :   List[str]	      =    tax_relpos_bias_lookup(
                      _lowercase				,      _lowercase				,      'encoder'		).T

      snake_case__       :   Optional[int]	      =    old['''encoder/encoder_norm/scale''']

      if not scalable_attention:
            snake_case__       :   str	      =    tax_relpos_bias_lookup(
                _lowercase				,      0				,      'encoder'		).T
            snake_case__       :   Optional[int]	      =    tax_relpos_bias_lookup(
                _lowercase				,      0				,      'decoder'		).T

      if not is_encoder_only:
            # Decoder.
            for i in range(_lowercase		):
                  # Block i, layer 0 (Self Attention).
                  snake_case__       :   Union[str, Any]	      =    tax_layer_norm_lookup(_lowercase				,      _lowercase				,      'decoder'				,      'pre_self_attention_layer_norm'		)
                  snake_case__       :   List[str]	      =    tax_attention_lookup(_lowercase				,      _lowercase				,      'decoder'				,      'self_attention'		)
                  snake_case__       :   Union[str, Any]	      =    layer_norm
                  snake_case__       :   str	      =    k.T
                  snake_case__       :   Dict	      =    o.T
                  snake_case__       :   Optional[int]	      =    q.T
                  snake_case__       :   Union[str, Any]	      =    v.T

                  # Block i, layer 1 (Cross Attention).
                  snake_case__       :   Union[str, Any]	      =    tax_layer_norm_lookup(_lowercase				,      _lowercase				,      'decoder'				,      'pre_cross_attention_layer_norm'		)
                  snake_case__       :   Optional[Any]	      =    tax_attention_lookup(_lowercase				,      _lowercase				,      'decoder'				,      'encoder_decoder_attention'		)
                  snake_case__       :   Tuple	      =    layer_norm
                  snake_case__       :   int	      =    k.T
                  snake_case__       :   Optional[Any]	      =    o.T
                  snake_case__       :   Tuple	      =    q.T
                  snake_case__       :   Union[str, Any]	      =    v.T

                  # Block i, layer 2 (MLP).
                  snake_case__       :   Optional[int]	      =    tax_layer_norm_lookup(_lowercase				,      _lowercase				,      'decoder'				,      'pre_mlp_layer_norm'		)
                  snake_case__       :   Optional[int]	      =    tax_mlp_lookup(_lowercase				,      _lowercase				,      'decoder'				,      _lowercase		)
                  snake_case__       :   List[Any]	      =    layer_norm
                  if split_mlp_wi:
                        snake_case__       :   int	      =    wi[0].T
                        snake_case__       :   Any	      =    wi[1].T
                  else:
                        snake_case__       :   Any	      =    wi.T
                  snake_case__       :   Dict	      =    wo.T

                  if scalable_attention:
                        # convert the rel_embedding of each layer
                        snake_case__       :   List[str]	      =    tax_relpos_bias_lookup(_lowercase				,      _lowercase				,      'decoder'		).T

            snake_case__       :   List[str]	      =    old['''decoder/decoder_norm/scale''']

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  snake_case__       :   Optional[Any]	      =    old['''decoder/logits_dense/kernel'''].T

      return new






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Tuple:
      snake_case__       :   List[str]	      =    collections.OrderedDict([(k, torch.from_numpy(v.copy()		)) for (k, v) in converted_params.items()]		)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            snake_case__       :   Tuple	      =    state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  snake_case__       :   Any	      =    state_dict['''shared.weight''']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('Using shared word embeddings as lm_head.'		)
                  snake_case__       :   Tuple	      =    state_dict['''shared.weight''']

      return state_dict






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   List[str]:
      snake_case__       :   int	      =    checkpoints.load_tax_checkpoint(_lowercase		)
      snake_case__       :   List[Any]	      =    convert_tax_to_pytorch(
          _lowercase				,      num_layers=config.num_layers				,      is_encoder_only=_lowercase				,      scalable_attention=_lowercase		)
      snake_case__       :   Optional[Any]	      =    make_state_dict(_lowercase				,      _lowercase		)
      model.load_state_dict(_lowercase				,      strict=_lowercase		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE = False				,      __SCREAMING_SNAKE_CASE = False				,      )    ->   Optional[int]:
      snake_case__       :   Optional[Any]	      =    MTaConfig.from_json_file(_lowercase		)
      print(f"Building PyTorch model from configuration: {config}"		)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            snake_case__       :   Any	      =    UMTaEncoderModel(_lowercase		)
      else:
            snake_case__       :   List[Any]	      =    UMTaForConditionalGeneration(_lowercase		)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(_lowercase				,      _lowercase				,      _lowercase				,      _lowercase				,      _lowercase		)

      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}"		)
      model.save_pretrained(_lowercase		)

      # Verify that we can load the checkpoint.
      model.from_pretrained(_lowercase		)
      print('Done'		)


if __name__ == "__main__":
      A_			      =		argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
      # Required parameters
      parser.add_argument(
          "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
      )
      parser.add_argument(
          "--scalable_attention",
          action="store_true",
          help="Whether the model uses scaled attention (umt5 model)",
          default=False,
      )
      A_			      =		parser.parse_args()
      convert_tax_checkpoint_to_pytorch(
          args.tax_checkpoint_path,
          args.config_file,
          args.pytorch_dump_path,
          args.is_encoder_only,
          args.scalable_attention,
      )






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, TensorType

__a							     =					logging.get_logger(__name__)

__a							     =					{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''imagegpt'''
    lowerCAmelCase         = ['''past_key_values''']
    lowerCAmelCase         = {
        '''hidden_size''': '''n_embd''',
        '''max_position_embeddings''': '''n_positions''',
        '''num_attention_heads''': '''n_head''',
        '''num_hidden_layers''': '''n_layer''',
    }





    def __init__( self  ,_SCREAMING_SNAKE_CASE=512 + 1  ,_SCREAMING_SNAKE_CASE=32 * 32  ,_SCREAMING_SNAKE_CASE=512  ,_SCREAMING_SNAKE_CASE=24  ,_SCREAMING_SNAKE_CASE=8  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE="quick_gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=1e-5  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=False  ,**_SCREAMING_SNAKE_CASE  ,)					->						Optional[int]:
         UpperCAmelCase_     :				Optional[int]  =     vocab_size
         UpperCAmelCase_     :				Union[str, Any]  =     n_positions
         UpperCAmelCase_     :				Union[str, Any]  =     n_embd
         UpperCAmelCase_     :				Any  =     n_layer
         UpperCAmelCase_     :				Optional[Any]  =     n_head
         UpperCAmelCase_     :				Union[str, Any]  =     n_inner
         UpperCAmelCase_     :				List[Any]  =     activation_function
         UpperCAmelCase_     :				List[str]  =     resid_pdrop
         UpperCAmelCase_     :				str  =     embd_pdrop
         UpperCAmelCase_     :				Optional[Any]  =     attn_pdrop
         UpperCAmelCase_     :				Dict  =     layer_norm_epsilon
         UpperCAmelCase_     :				Union[str, Any]  =     initializer_range
         UpperCAmelCase_     :				Dict  =     scale_attn_weights
         UpperCAmelCase_     :				Any  =     use_cache
         UpperCAmelCase_     :				List[str]  =     scale_attn_by_inverse_layer_idx
         UpperCAmelCase_     :				Tuple  =     reorder_and_upcast_attn
         UpperCAmelCase_     :				int  =     tie_word_embeddings

         super().__init__(tie_word_embeddings=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )


class     __a(	_a  ):






    """simple docstring"""





    @property
    def 				a__ ( self    )					->						Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
             ]    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = 1  ,_SCREAMING_SNAKE_CASE = -1  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = 3  ,_SCREAMING_SNAKE_CASE = 32  ,_SCREAMING_SNAKE_CASE = 32  ,)					->						Mapping[str, Any]:
         UpperCAmelCase_     :				Any  =     self._generate_dummy_images(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[int]  =     dict(preprocessor(images=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE    )    )

         return inputs