





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


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


if __name__ == "__main__":
       lowercase    : Tuple    	=  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""")
       lowercase    : str    	=  parser.parse_args()
       convert_tf_gptsan_to_pt(args)










import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
       import torch

@is_pipeline_test
class  __A(						unittest.TestCase ):
     __A	  =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     __A	  =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						TextaTextGenerationPipeline(model=A,       tokenizer=A      )
            return generator, ["Something to write", "Something else"]

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						generator('''Something there'''      )
            self.assertEqual(A,       [{'''generated_text''': ANY(A      )}]      )
            # These are encoder decoder, they don't just append to incoming string
            self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''      )      )

            _UpperCamelCase              =						generator(['''This is great !''', '''Something else'''],       num_return_sequences=2,       do_sample=A      )
            self.assertEqual(
                A,       [
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                ],       )

            _UpperCamelCase              =						generator(
                ['''This is great !''', '''Something else'''],       num_return_sequences=2,       batch_size=2,       do_sample=A      )
            self.assertEqual(
                A,       [
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                ],       )

            with self.assertRaises(A      ):
                   generator(4      )

     @require_torch
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						pipeline('''text2text-generation''',       model='''patrickvonplaten/t5-tiny-random''',       framework='''pt'''      )
            # do_sample=False necessary for reproducibility
            _UpperCamelCase              =						generator('''Something there''',       do_sample=A      )
            self.assertEqual(A,       [{'''generated_text''': ''''''}]      )

            _UpperCamelCase              =						3
            _UpperCamelCase              =						generator(
                '''Something there''',       num_return_sequences=A,       num_beams=A,       )
            _UpperCamelCase              =						[
                {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
                {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
                {'''generated_text''': ''''''},
            ]
            self.assertEqual(A,       A      )

            _UpperCamelCase              =						generator('''This is a test''',       do_sample=A,       num_return_sequences=2,       return_tensors=A      )
            self.assertEqual(
                A,       [
                    {'''generated_token_ids''': ANY(torch.Tensor      )},
                    {'''generated_token_ids''': ANY(torch.Tensor      )},
                ],       )
            _UpperCamelCase              =						generator.model.config.eos_token_id
            _UpperCamelCase              =						'''<pad>'''
            _UpperCamelCase              =						generator(
                ['''This is a test''', '''This is a second test'''],       do_sample=A,       num_return_sequences=2,       batch_size=2,       return_tensors=A,       )
            self.assertEqual(
                A,       [
                    [
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                    ],
                    [
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                    ],
                ],       )



     @require_tf
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						pipeline('''text2text-generation''',       model='''patrickvonplaten/t5-tiny-random''',       framework='''tf'''      )
            # do_sample=False necessary for reproducibility
            _UpperCamelCase              =						generator('''Something there''',       do_sample=A      )
            self.assertEqual(A,       [{'''generated_text''': ''''''}]      )






