


'''simple docstring'''

from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    Optional[int]   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    Tuple=12   ,						UpperCamelCase    :    Tuple=7   ,						UpperCamelCase    :    List[str]=True   ,						UpperCamelCase    :    Dict=True   ,						UpperCamelCase    :    int=True   ,						UpperCamelCase    :    str=99   ,						UpperCamelCase    :    Tuple=32   ,						UpperCamelCase    :    Dict=32   ,						UpperCamelCase    :    List[str]=2   ,						UpperCamelCase    :    int=4   ,						UpperCamelCase    :    Optional[int]=37   ,						UpperCamelCase    :    Optional[Any]=0.1   ,						UpperCamelCase    :    str=0.1   ,						UpperCamelCase    :    List[str]=512   ,						UpperCamelCase    :    Optional[int]=0.02   ,						UpperCamelCase    :    List[Any]=0   ,						UpperCamelCase    :    Union[str, Any]=None   ,						):

             '''simple docstring'''







             lowercase__      			= parent
             lowercase__      			= batch_size
             lowercase__      			= seq_length
             lowercase__      			= is_training
             lowercase__      			= use_input_mask
             lowercase__      			= use_labels
             lowercase__      			= vocab_size
             lowercase__      			= hidden_size
             lowercase__      			= projection_dim
             lowercase__      			= num_hidden_layers
             lowercase__      			= num_attention_heads
             lowercase__      			= intermediate_size
             lowercase__      			= dropout
             lowercase__      			= attention_dropout
             lowercase__      			= max_position_embeddings
             lowercase__      			= initializer_range
             lowercase__      			= scope
             lowercase__      			= bos_token_id



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size )

             lowercase__      			= None
             if self.use_input_mask:
                    lowercase__      			= random_attention_mask([self.batch_size, self.seq_length] )

             if input_mask is not None:
                    lowercase__      			= input_mask.numpy()
                    lowercase__			,lowercase__      			= input_mask.shape
                    lowercase__      			= np.random.randint(1   ,						seq_length - 1   ,						size=(batch_size,) )
                    for batch_idx, start_index in enumerate(UpperCamelCase ):
                           lowercase__      			= 1
                           lowercase__      			= 0

             lowercase__      			= self.get_config()

             return config, input_ids, tf.convert_to_tensor(UpperCamelCase )



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             return BlipTextConfig(
                 vocab_size=self.vocab_size   ,						hidden_size=self.hidden_size   ,						projection_dim=self.projection_dim   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						dropout=self.dropout   ,						attention_dropout=self.attention_dropout   ,						max_position_embeddings=self.max_position_embeddings   ,						initializer_range=self.initializer_range   ,						bos_token_id=self.bos_token_id   ,						)



      def       UpperCamelCase__		(self    :    List[str]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= TFBlipTextModel(config=UpperCamelCase )
             lowercase__      			= model(UpperCamelCase   ,						attention_mask=UpperCamelCase   ,						training=UpperCamelCase )
             lowercase__      			= model(UpperCamelCase   ,						training=UpperCamelCase )
             self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size) )
             self.parent.assertEqual(result.pooler_output.shape   ,						(self.batch_size, self.hidden_size) )






      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             lowercase__      			= self.prepare_config_and_inputs()
             lowercase__			,lowercase__			,lowercase__      			= config_and_inputs
             lowercase__      			= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
             return config, inputs_dict






@require_tf
class   __lowerCAmelCase						(lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     str  					=							(TFBlipTextModel,) if is_tf_available() else ()
      lowerCAmelCase__					:     Union[str, Any]  					=							False
      lowerCAmelCase__					:     int  					=							False
      lowerCAmelCase__					:     Tuple  					=							False



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= BlipTextModelTester(self )
             lowercase__      			= ConfigTester(self   ,						config_class=UpperCamelCase   ,						hidden_size=37 )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             pass



      @unittest.skip(reason='''Blip does not use inputs_embeds''' )
      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             pass



      @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             pass



      @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             pass



      @slow
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__      			= TFBlipTextModel.from_pretrained(UpperCamelCase )
                    self.assertIsNotNone(UpperCamelCase )






      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    Any=True ):

             '''simple docstring'''







             super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase )






'''simple docstring'''







import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    str ,	__a      :    Optional[Any]=None ):






   """simple docstring"""

   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   _a				:	str			=   nn.Parameter(__a )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      _a				:	Any			=   nn.Parameter(__a )





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Optional[Any] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Tuple			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	Dict			=   np.asarray(weights[2] )

   set_param(
       torch_layer.self_attention.query_key ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.value ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.output.dense ,	torch.tensor(__a ).view(-1 ,	__a ).contiguous().transpose(0 ,	1 ) ,	)





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    Optional[int] ,	__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	str			=   np.asarray(weights[2] )
   _a				:	int			=   np.asarray(weights[3] )

   set_param(
       torch_layer.self_attention.query ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.key ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.value ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.output.dense ,	torch.tensor(__a ).view(-1 ,	__a ).contiguous().transpose(0 ,	1 ) ,	)





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Any ,	__a      :    Optional[Any] ):






   """simple docstring"""

   _a				:	List[str]			=   weights[0][0][0]
   _a				:	List[Any]			=   np.asarray(layer_norm_a[0] )
   _a				:	List[str]			=   np.asarray(layer_norm_a[1] )
   set_param(
       torch_block.attention.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # lsh weights + output
   _a				:	List[str]			=   weights[0][1]
   if len(__a ) < 4:
      set_layer_weights_in_torch_lsh(__a ,	torch_block.attention ,	__a )
   else:
      set_layer_weights_in_torch_local(__a ,	torch_block.attention ,	__a )

   # intermediate weighs
   _a				:	Optional[Any]			=   weights[2][0][1][2]

   # Chunked Feed Forward
   if len(__a ) == 4:
      _a				:	Union[str, Any]			=   intermediate_weights[2]

   # layernorm 2
   _a				:	Any			=   np.asarray(intermediate_weights[0][0] )
   _a				:	List[Any]			=   np.asarray(intermediate_weights[0][1] )
   set_param(
       torch_block.feed_forward.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # intermediate dense
   _a				:	Any			=   np.asarray(intermediate_weights[1][0] )
   _a				:	Any			=   np.asarray(intermediate_weights[1][1] )
   set_param(
       torch_block.feed_forward.dense.dense ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)

   # intermediate out
   _a				:	Optional[int]			=   np.asarray(intermediate_weights[4][0] )
   _a				:	int			=   np.asarray(intermediate_weights[4][1] )
   set_param(
       torch_block.feed_forward.output.dense ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Dict ,	__a      :    Dict ,	__a      :    List[Any] ):






   """simple docstring"""

   _a				:	Optional[int]			=   torch_model.reformer

   # word embeds
   _a				:	Tuple			=   np.asarray(weights[1] )
   set_param(
       torch_model_reformer.embeddings.word_embeddings ,	torch.tensor(__a ) ,	)

   if isinstance(weights[3] ,	__a ):
      _a				:	Any			=   torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights ) ):
         _a				:	List[Any]			=   np.asarray(weights[3][emb_idx][0] )
         assert (
             position_embeddings.weights[emb_idx].shape == emb_weights.shape
         ), f"""{position_embeddings[emb_idx]} emb does not match"""
         _a				:	Any			=   nn.Parameter(torch.tensor(__a ) )

   _a				:	List[str]			=   weights[5]
   assert len(torch_model_reformer.encoder.layers ) * 4 == len(
       __a ), "HF and trax model do not have the same number of layers"
   for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
      _a				:	Tuple			=   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(__a ,	__a ,	__a )

   # output layer norm
   _a				:	Optional[Any]			=   np.asarray(weights[7][0] )
   _a				:	int			=   np.asarray(weights[7][1] )
   set_param(
       torch_model_reformer.encoder.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # output embeddings
   _a				:	List[str]			=   np.asarray(weights[9][0] )
   _a				:	int			=   np.asarray(weights[9][1] )
   set_param(
       torch_model.lm_head.decoder ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Tuple ,	__a      :    Optional[Any] ,	__a      :    Dict ):






   """simple docstring"""

   _a				:	List[Any]			=   ReformerConfig.from_json_file(__a )
   print(f"""Building PyTorch model from configuration: {config}""" )
   _a				:	int			=   ReformerModelWithLMHead(__a )

   with open(__a ,	'rb' ) as f:
      _a				:	Optional[Any]			=   pickle.load(__a )['weights']

   set_model_weights_in_torch(__a ,	__a ,	config.hidden_size )

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""" )
   torch.save(model.state_dict() ,	__a )


if __name__ == "__main__":
  __lowerCAmelCase         =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained Reformer model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __lowerCAmelCase         =    parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



