




'''simple docstring'''





import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
   import jax
   import jax.numpy as jnp

   from transformers.generation import (
       FlaxForcedBOSTokenLogitsProcessor,
       FlaxForcedEOSTokenLogitsProcessor,
       FlaxLogitsProcessorList,
       FlaxMinLengthLogitsProcessor,
       FlaxTemperatureLogitsWarper,
       FlaxTopKLogitsWarper,
       FlaxTopPLogitsWarper,
   )

@require_flax
class        _a    (						unittest.TestCase							):






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      int:
          _snake_case							=				jnp.ones((batch_size, length)   ) / length
          return scores






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				None
          _snake_case							=				20

          _snake_case							=				self._get_uniform_logits(batch_size=2 ,length=_SCREAMING_SNAKE_CASE   )

          # tweak scores to not be uniform anymore
          _snake_case							=				scores.at[1, 5].set((1 / length) + 0.1   )  # peak, 1st batch
          _snake_case							=				scores.at[1, 10].set((1 / length) - 0.4   )  # valley, 1st batch

          # compute softmax
          _snake_case							=				jax.nn.softmax(_SCREAMING_SNAKE_CASE ,axis=-1   )

          _snake_case							=				FlaxTemperatureLogitsWarper(temperature=0.5   )
          _snake_case							=				FlaxTemperatureLogitsWarper(temperature=1.3   )

          _snake_case							=				jax.nn.softmax(temp_dist_warper_sharper(_SCREAMING_SNAKE_CASE ,scores.copy() ,cur_len=_SCREAMING_SNAKE_CASE   ) ,axis=-1   )
          _snake_case							=				jax.nn.softmax(temp_dist_warper_smoother(_SCREAMING_SNAKE_CASE ,scores.copy() ,cur_len=_SCREAMING_SNAKE_CASE   ) ,axis=-1   )

          # uniform distribution stays uniform
          self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_sharp[0, :] ,atol=1e-3   )   )
          self.assertTrue(jnp.allclose(probs[0, :] ,warped_prob_smooth[0, :] ,atol=1e-3   )   )

          # sharp peaks get higher, valleys get lower
          self.assertLess(probs[1, :].max() ,warped_prob_sharp[1, :].max()   )
          self.assertGreater(probs[1, :].min() ,warped_prob_sharp[1, :].min()   )

          # smooth peaks get lower, valleys get higher
          self.assertGreater(probs[1, :].max() ,warped_prob_smooth[1, :].max()   )
          self.assertLess(probs[1, :].min() ,warped_prob_smooth[1, :].min()   )






     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				None
          _snake_case							=				10
          _snake_case							=				2

          # create ramp distribution
          _snake_case							=				np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE   )[None, :] ,(batch_size, vocab_size)   ).copy()
          _snake_case							=				ramp_logits[1:, : vocab_size // 2] + vocab_size

          _snake_case							=				FlaxTopKLogitsWarper(3   )

          _snake_case							=				top_k_warp(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )

          # check that correct tokens are filtered
          self.assertListEqual(jnp.isinf(scores[0]   ).tolist() ,7 * [True] + 3 * [False]   )
          self.assertListEqual(jnp.isinf(scores[1]   ).tolist() ,2 * [True] + 3 * [False] + 5 * [True]   )

          # check special case
          _snake_case							=				5
          _snake_case							=				FlaxTopKLogitsWarper(top_k=1 ,filter_value=0.0 ,min_tokens_to_keep=3   )

          _snake_case							=				np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE   )[None, :] ,(batch_size, length)   ).copy()
          _snake_case							=				top_k_warp_safety_check(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )

          # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
          self.assertListEqual((scores == 0.0).sum(axis=-1   ).tolist() ,[2, 2]   )






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				None
          _snake_case							=				10
          _snake_case							=				2

          # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
          _snake_case							=				np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.1_5, 0.3, 0.3, 0.2_5]]   )   )

          _snake_case							=				FlaxTopPLogitsWarper(0.8   )
          _snake_case							=				np.exp(top_p_warp(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )   )

          # dist should be filtered to keep min num values so that sum is >= top_p
          # exp (-inf) => 0
          _snake_case							=				np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.2_5]]   )
          self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,atol=1e-3   )   )

          # check edge cases with negative and extreme logits
          _snake_case							=				np.broadcast_to(np.arange(_SCREAMING_SNAKE_CASE   )[None, :] ,(batch_size, vocab_size)   ).copy() - (
              vocab_size // 2
          )

          # make ramp_logits more extreme
          _snake_case							=				ramp_logits[1] * 1_0_0.0

          # make sure at least 2 tokens are kept
          _snake_case							=				FlaxTopPLogitsWarper(0.9 ,min_tokens_to_keep=2 ,filter_value=0.0   )
          _snake_case							=				top_p_warp(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )

          # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
          self.assertListEqual((filtered_dist != 0.0).sum(axis=-1   ).tolist() ,[3, 2]   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				20
          _snake_case							=				4
          _snake_case							=				0

          _snake_case							=				FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_SCREAMING_SNAKE_CASE   )

          # check that min length is applied at length 5
          _snake_case							=				ids_tensor((batch_size, 20) ,vocab_size=20   )
          _snake_case							=				5
          _snake_case							=				self._get_uniform_logits(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				min_dist_processor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
          self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() ,4 * [-float("inf"   )]   )

          # check that min length is not applied anymore at length 15
          _snake_case							=				self._get_uniform_logits(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				15
          _snake_case							=				min_dist_processor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
          self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE   ).any()   )






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				20
          _snake_case							=				4
          _snake_case							=				0

          _snake_case							=				FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE   )

          # check that all scores are -inf except the bos_token_id score
          _snake_case							=				ids_tensor((batch_size, 1) ,vocab_size=20   )
          _snake_case							=				1
          _snake_case							=				self._get_uniform_logits(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				logits_processor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
          self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]   ).all()   )
          self.assertListEqual(scores[:, bos_token_id].tolist() ,4 * [0]   )  # score for bos_token_id shold be zero

          # check that bos_token_id is not forced if current length is greater than 1
          _snake_case							=				3
          _snake_case							=				self._get_uniform_logits(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				logits_processor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
          self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE   ).any()   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				20
          _snake_case							=				4
          _snake_case							=				0
          _snake_case							=				5

          _snake_case							=				FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE   )

          # check that all scores are -inf except the eos_token_id when max_length is reached
          _snake_case							=				ids_tensor((batch_size, 4) ,vocab_size=20   )
          _snake_case							=				4
          _snake_case							=				self._get_uniform_logits(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				logits_processor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
          self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]   ).all()   )
          self.assertListEqual(scores[:, eos_token_id].tolist() ,4 * [0]   )  # score for eos_token_id should be zero

          # check that eos_token_id is not forced if max_length is not reached
          _snake_case							=				3
          _snake_case							=				self._get_uniform_logits(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				logits_processor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
          self.assertFalse(jnp.isinf(_SCREAMING_SNAKE_CASE   ).any()   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				4
          _snake_case							=				10
          _snake_case							=				15
          _snake_case							=				2
          _snake_case							=				1
          _snake_case							=				15

          # dummy input_ids and scores
          _snake_case							=				ids_tensor((batch_size, sequence_length) ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				input_ids.copy()

          _snake_case							=				self._get_uniform_logits(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				scores.copy()

          # instantiate all dist processors
          _snake_case							=				FlaxTemperatureLogitsWarper(temperature=0.5   )
          _snake_case							=				FlaxTopKLogitsWarper(3   )
          _snake_case							=				FlaxTopPLogitsWarper(0.8   )

          # instantiate all logits processors
          _snake_case							=				FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				10

          # no processor list
          _snake_case							=				temp_dist_warp(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				top_k_warp(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				top_p_warp(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				min_dist_proc(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				bos_dist_proc(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				eos_dist_proc(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )

          # with processor list
          _snake_case							=				FlaxLogitsProcessorList(
              [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]   )
          _snake_case							=				processor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )

          # scores should be equal
          self.assertTrue(jnp.allclose(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,atol=1e-3   )   )

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist()   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				4
          _snake_case							=				10
          _snake_case							=				15
          _snake_case							=				2
          _snake_case							=				1
          _snake_case							=				15

          # dummy input_ids and scores
          _snake_case							=				ids_tensor((batch_size, sequence_length) ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				input_ids.copy()

          _snake_case							=				self._get_uniform_logits(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				scores.copy()

          # instantiate all dist processors
          _snake_case							=				FlaxTemperatureLogitsWarper(temperature=0.5   )
          _snake_case							=				FlaxTopKLogitsWarper(3   )
          _snake_case							=				FlaxTopPLogitsWarper(0.8   )

          # instantiate all logits processors
          _snake_case							=				FlaxMinLengthLogitsProcessor(min_length=10 ,eos_token_id=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				FlaxForcedBOSTokenLogitsProcessor(bos_token_id=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				FlaxForcedEOSTokenLogitsProcessor(max_length=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				10

          # no processor list
          def run_no_processor_list(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ):
               _snake_case							=				temp_dist_warp(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
               _snake_case							=				top_k_warp(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
               _snake_case							=				top_p_warp(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
               _snake_case							=				min_dist_proc(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
               _snake_case							=				bos_dist_proc(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
               _snake_case							=				eos_dist_proc(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
               return scores

          # with processor list
          def run_processor_list(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ):
               _snake_case							=				FlaxLogitsProcessorList(
                   [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]   )
               _snake_case							=				processor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,cur_len=_SCREAMING_SNAKE_CASE   )
               return scores

          _snake_case							=				jax.jit(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				jax.jit(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				jitted_run_no_processor_list(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				jitted_run_processor_list(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          # scores should be equal
          self.assertTrue(jnp.allclose(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,atol=1e-3   )   )

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist() ,input_ids_comp.tolist()   )








'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





def      __a     (       _UpperCamelCase:   Tuple   )						->			Union[str, Any]:

     """simple docstring"""




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


if __name__ == "__main__":
   UpperCamelCase_						:      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''')
   UpperCamelCase_						:      Any       =						parser.parse_args()
   convert_tf_gptsan_to_pt(args)


