




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       A__       =    os.path.join(args.tf_model_dir ,				'''parameters.json'''						)
       A__       =    json.loads(open(lowercase_						).read()						)
       if not params:
              raise ValueError(
                  f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""						)
       if not args.output.endswith('''.pt'''						):
              A__       =    args.output + '''.pt'''
       A__       =    OrderedDict()
       with tf.device('''/CPU:0'''						):
              A__       =    tf.train.load_checkpoint(args.tf_model_dir						)
              A__       =    reader.get_variable_to_shape_map()
              for key_name in shapes.keys():
                     A__       =    reader.get_tensor(lowercase_						).astype(np.floataa						)
                     if key_name.endswith('''/adam_m'''						) or key_name.endswith('''/adam_v'''						):
                            continue
                     if key_name.startswith('''pasts/'''						):
                            if key_name.startswith('''pasts/mlp'''						):
                                   A__       =    int(key_name[9]						)
                            elif key_name.startswith('''pasts/out'''						):
                                   A__       =    8
                            A__       =    '''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                            A__       =    vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                            A__       =    torch.tensor(lowercase_						)
                     elif key_name.startswith('''model/moe'''						):
                            A__       =    int(key_name[9:].split('''/'''						)[0]						)
                            if key_name.endswith('''/switch_gating/kernel'''						):
                                   A__       =    '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
                                   A__       =    vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   A__       =    torch.tensor(lowercase_						)
                            elif key_name.endswith('''/softmlp/kernel'''						):
                                   A__       =    '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
                                   A__       =    vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   A__       =    torch.tensor(lowercase_						)
                            elif key_name.endswith('''/wo/kernel'''						) or key_name.endswith('''/wi/kernel'''						):
                                   A__       =    key_name[-9:-7]
                                   for i in range(16						):
                                          A__       =    '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
                                          A__       =    (
                                              vnp[i].transpose([1, 0]						).copy()
                                          )  # In Mesh-Tensorflow, it is one array, so it is divided
                                          A__       =    torch.tensor(lowercase_						)
                     elif key_name.startswith('''model/mlp'''						):
                            A__       =    int(key_name[9:].split('''/'''						)[0]						)
                            if key_name.endswith('''/p1/kernel'''						):
                                   A__       =    '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
                                   A__       =    vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   A__       =    torch.tensor(lowercase_						)
                            elif key_name.endswith('''/p1/bias'''						):
                                   A__       =    '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
                                   A__       =    vnp.copy()  # same because it is one dimensional
                                   A__       =    torch.tensor(lowercase_						)
                            elif key_name.endswith('''/p2/kernel'''						):
                                   A__       =    '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
                                   A__       =    vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   A__       =    torch.tensor(lowercase_						)
                            elif key_name.endswith('''/p2/bias'''						):
                                   A__       =    '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
                                   A__       =    vnp.copy()  # same because it is one dimensional
                                   A__       =    torch.tensor(lowercase_						)
                     elif key_name.startswith('''model/ln'''						):
                            A__       =    int(key_name[8:].split('''/'''						)[0]						)
                            if key_name.endswith('''/b'''						):
                                   A__       =    '''model.blocks.%d.feed_forward.norm.bias''' % player
                                   A__       =    vnp.copy()  # same because it is one dimensional
                                   A__       =    torch.tensor(lowercase_						)
                            elif key_name.endswith('''/g'''						):
                                   A__       =    '''model.blocks.%d.feed_forward.norm.weight''' % player
                                   A__       =    vnp.copy()  # same because it is one dimensional
                                   A__       =    torch.tensor(lowercase_						)
                     elif key_name.startswith('''model/att'''						):
                            A__       =    int(key_name[9:].split('''/'''						)[0]						)
                            if key_name.endswith('''/qkv/kernel'''						):
                                   A__       =    vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                                   A__       =    state[:, 0, :, :]
                                   A__       =    state[:, 1, :, :]
                                   A__       =    state[:, 2, :, :]
                                   A__       =    (
                                       state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]						)
                                       .transpose([1, 0]						)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   A__       =    (
                                       state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]						)
                                       .transpose([1, 0]						)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   A__       =    (
                                       state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]						)
                                       .transpose([1, 0]						)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   A__       =    '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
                                   A__       =    torch.tensor(lowercase_						)
                                   A__       =    '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
                                   A__       =    torch.tensor(lowercase_						)
                                   A__       =    '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
                                   A__       =    torch.tensor(lowercase_						)
                            elif key_name.endswith('''/o/kernel'''						):
                                   A__       =    '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
                                   A__       =    (
                                       vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]						).transpose([1, 0]						).copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   A__       =    torch.tensor(lowercase_						)
                     elif key_name.startswith('''model/an'''						):
                            A__       =    int(key_name[8:].split('''/'''						)[0]						)
                            if key_name.endswith('''/b'''						):
                                   A__       =    '''model.blocks.%d.self_attn.norm.bias''' % player
                                   A__       =    vnp.copy()  # same because it is one dimensional
                                   A__       =    torch.tensor(lowercase_						)
                            elif key_name.endswith('''/g'''						):
                                   A__       =    '''model.blocks.%d.self_attn.norm.weight''' % player
                                   A__       =    vnp.copy()  # same because it is one dimensional
                                   A__       =    torch.tensor(lowercase_						)
                     elif (
                         key_name.startswith('''model/wte'''						)
                         or key_name.startswith('''model/wpe'''						)
                         or key_name.startswith('''model/ete'''						)
                     ):
                            A__       =    {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
                                key_name[-3:]
                            ]
                            A__       =    '''model.%s.weight''' % nlayer
                            A__       =    vnp.copy()  # same in embedded
                            A__       =    torch.tensor(lowercase_						)
                            if key_name.startswith('''model/wte'''						):
                                   A__       =    '''lm_head.weight'''
                                   A__       =    vnp.copy()  # same in embedded
                                   A__       =    torch.tensor(lowercase_						)
                     elif key_name.startswith('''model/wob'''						):
                            A__       =    '''final_logits_bias'''
                            A__       =    vnp.copy()  # same in embedded
                            A__       =    state.reshape((1, -1)						)
                            A__       =    torch.tensor(lowercase_						)
                     elif key_name == "model/dense/kernel":
                            A__       =    '''model.last_project.weight'''
                            A__       =    vnp.transpose([1, 0]						).copy()  # Mesh-Tensorflow is a diagonal matrix
                            A__       =    torch.tensor(lowercase_						)
                     elif key_name == "model/dense_1/bias":
                            A__       =    '''model.last_project.bias'''
                            A__       =    vnp.copy()  # same because it is one dimensional
                            A__       =    torch.tensor(lowercase_						)
       torch.save(lowercase_ ,				args.output						)


if __name__ == "__main__":
  _lowerCamelCase	:		List[Any]							       =   argparse.ArgumentParser(
      description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
  )
  parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
  parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
  _lowerCamelCase	:		Any							       =   parser.parse_args()
  convert_tf_gptsan_to_pt(args)








import os
import sys
import unittest


_lowerCamelCase	:		Optional[Any]							       =   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_lowerCamelCase	:		Any							       =   os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_lowerCamelCase	:		str							       =   os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")


class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Tuple:





           '''simple docstring'''

           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)

           A__       =    {'''BertModelTest''': '''BertModelTester'''}

           A__       =    {
               '''BlipModelTest''': '''BlipModelTester''',
               '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
               '''BlipTextModelTest''': '''BlipTextModelTester''',
               '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
               '''BlipVQAModelTest''': '''BlipVQAModelTester''',
               '''BlipVisionModelTest''': '''BlipVisionModelTester''',
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[Any]:





           '''simple docstring'''

           A__       =    get_model_to_test_mapping(UpperCAmelCase__)
           A__       =    get_model_to_test_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTest'''],
               '''BertForMultipleChoice''': ['''BertModelTest'''],
               '''BertForNextSentencePrediction''': ['''BertModelTest'''],
               '''BertForPreTraining''': ['''BertModelTest'''],
               '''BertForQuestionAnswering''': ['''BertModelTest'''],
               '''BertForSequenceClassification''': ['''BertModelTest'''],
               '''BertForTokenClassification''': ['''BertModelTest'''],
               '''BertLMHeadModel''': ['''BertModelTest'''],
               '''BertModel''': ['''BertModelTest'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
               '''BlipModel''': ['''BlipModelTest'''],
               '''BlipTextModel''': ['''BlipTextModelTest'''],
               '''BlipVisionModel''': ['''BlipVisionModelTest'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)





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





           '''simple docstring'''

           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTester'''],
               '''BertForMultipleChoice''': ['''BertModelTester'''],
               '''BertForNextSentencePrediction''': ['''BertModelTester'''],
               '''BertForPreTraining''': ['''BertModelTester'''],
               '''BertForQuestionAnswering''': ['''BertModelTester'''],
               '''BertForSequenceClassification''': ['''BertModelTester'''],
               '''BertForTokenClassification''': ['''BertModelTester'''],
               '''BertLMHeadModel''': ['''BertModelTester'''],
               '''BertModel''': ['''BertModelTester'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
               '''BlipModel''': ['''BlipModelTester'''],
               '''BlipTextModel''': ['''BlipTextModelTester'''],
               '''BlipVisionModel''': ['''BlipVisionModelTester'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


