




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch







def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[str]  ):

					'''simple docstring'''





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


if __name__ == "__main__":
					UpperCamelCase__					    =    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__					    =    parser.parse_args()
					convert_tf_gptsan_to_pt(args)










import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:
					class     a__					:






											@staticmethod
											def        __SCREAMING_SNAKE_CASE(     *_A	,    **_A    ):




																"""simple docstring"""



																pass
@is_pipeline_test
@require_torch
@require_vision
class     a__					(      unittest.TestCase   ):
						_a							:					Any      =					MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="hf-internal-testing/tiny-vilt-random-vqa"    )
											__lowerCAmelCase            = [
											    {
											        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
											        "question": "How many cats are there?",
											    },
											    {
											        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
											        "question": "How many cats are there?",
											    },
											]
											return vqa_pipeline, examples






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = vqa_pipeline(_A	,    top_k=1    )
											self.assertEqual(
											    _A	,    [
											        [{"score": ANY(_A    ), "answer": ANY(_A    )}],
											        [{"score": ANY(_A    ), "answer": ANY(_A    )}],
											    ]	,    )






						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="hf-internal-testing/tiny-vilt-random-vqa"    )
											__lowerCAmelCase            = "./tests/fixtures/tests_samples/COCO/000000039769.png"
											__lowerCAmelCase            = "How many cats are there?"

											__lowerCAmelCase            = vqa_pipeline(image=_A	,    question="How many cats are there?"	,    top_k=2    )
											self.assertEqual(
											    _A	,    [{"score": ANY(_A    ), "answer": ANY(_A    )}, {"score": ANY(_A    ), "answer": ANY(_A    )}]    )

											__lowerCAmelCase            = vqa_pipeline({"image": image, "question": question}	,    top_k=2    )
											self.assertEqual(
											    _A	,    [{"score": ANY(_A    ), "answer": ANY(_A    )}, {"score": ANY(_A    ), "answer": ANY(_A    )}]    )






						@slow
						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="dandelin/vilt-b32-finetuned-vqa"    )
											__lowerCAmelCase            = "./tests/fixtures/tests_samples/COCO/000000039769.png"
											__lowerCAmelCase            = "How many cats are there?"

											__lowerCAmelCase            = vqa_pipeline(image=_A	,    question=_A	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]    )

											__lowerCAmelCase            = vqa_pipeline({"image": image, "question": question}	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]    )

											__lowerCAmelCase            = vqa_pipeline(
											    [{"image": image, "question": question}, {"image": image, "question": question}]	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [[{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]] * 2	,    )






						@require_tf
						@unittest.skip("Visual question answering not implemented in TF"    )
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											pass




