




'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor

class        _a    (						unittest.TestCase							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=13 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=30 ,_SCREAMING_SNAKE_CASE=400 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5] ,)							->      str:
          _snake_case							=				size if size is not None else {'''height''': 18, '''width''': 18}
          _snake_case							=				parent
          _snake_case							=				batch_size
          _snake_case							=				num_channels
          _snake_case							=				image_size
          _snake_case							=				min_resolution
          _snake_case							=				max_resolution
          _snake_case							=				do_resize
          _snake_case							=				size
          _snake_case							=				do_normalize
          _snake_case							=				image_mean
          _snake_case							=				image_std






     def     _lowercase     (			self   )							->      Optional[int]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }







@require_torch
@require_vision
class        _a    (						__snake_case , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Dict       =						ViTImageProcessor if is_vision_available() else None






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				EfficientFormerImageProcessorTester(self   )






     @property
     def     _lowercase     (			self   )							->      Dict:
          return self.image_proc_tester.prepare_image_processor_dict()






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							=				self.image_processing_class(**self.image_processor_dict   )
          self.assertTrue(hasattr(a_ ,"image_mean"   )   )
          self.assertTrue(hasattr(a_ ,"image_std"   )   )
          self.assertTrue(hasattr(a_ ,"do_normalize"   )   )
          self.assertTrue(hasattr(a_ ,"do_resize"   )   )
          self.assertTrue(hasattr(a_ ,"size"   )   )






     def     _lowercase     (			self   )							->      Optional[Any]:
          pass






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				self.image_processing_class(**self.image_processor_dict   )
          # create random PIL images
          _snake_case							=				prepare_image_inputs(self.image_proc_tester ,equal_resolution=a_   )
          for image in image_inputs:
               self.assertIsInstance(a_ ,Image.Image   )

          # Test not batched input
          _snake_case							=				image_processor(image_inputs[0] ,return_tensors="pt"   ).pixel_values
          self.assertEqual(
              encoded_images.shape ,(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              ) ,)

          # Test batched
          _snake_case							=				image_processor(a_ ,return_tensors="pt"   ).pixel_values
          self.assertEqual(
              encoded_images.shape ,(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              ) ,)






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				self.image_processing_class(**self.image_processor_dict   )
          # create random numpy tensors
          _snake_case							=				prepare_image_inputs(self.image_proc_tester ,equal_resolution=a_ ,numpify=a_   )
          for image in image_inputs:
               self.assertIsInstance(a_ ,np.ndarray   )

          # Test not batched input
          _snake_case							=				image_processor(image_inputs[0] ,return_tensors="pt"   ).pixel_values
          self.assertEqual(
              encoded_images.shape ,(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              ) ,)

          # Test batched
          _snake_case							=				image_processor(a_ ,return_tensors="pt"   ).pixel_values
          self.assertEqual(
              encoded_images.shape ,(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              ) ,)






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				self.image_processing_class(**self.image_processor_dict   )
          # create random PyTorch tensors
          _snake_case							=				prepare_image_inputs(self.image_proc_tester ,equal_resolution=a_ ,torchify=a_   )
          for image in image_inputs:
               self.assertIsInstance(a_ ,torch.Tensor   )

          # Test not batched input
          _snake_case							=				image_processor(image_inputs[0] ,return_tensors="pt"   ).pixel_values
          self.assertEqual(
              encoded_images.shape ,(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              ) ,)

          # Test batched
          _snake_case							=				image_processor(a_ ,return_tensors="pt"   ).pixel_values
          self.assertEqual(
              encoded_images.shape ,(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              ) ,)









'''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)


