From 66ca3418872943de9167074992b94f1172c6f474 Mon Sep 17 00:00:00 2001
From: Zeref996 <825276847@qq.com>
Date: Mon, 9 Dec 2024 08:50:45 +0000
Subject: [PATCH] plt add nlp case, test=model

---
 .../layoutlmv2/layoutlmv2_model.py            | 41 -------------------
 .../transformers/layoutxlm/layoutxlm_model.py | 38 -----------------
 2 files changed, 79 deletions(-)
 delete mode 100644 framework/e2e/PaddleLT_new/layerNLPcase/debug/case_bug/transformers/layoutlmv2/layoutlmv2_model.py
 delete mode 100644 framework/e2e/PaddleLT_new/layerNLPcase/debug/case_bug/transformers/layoutxlm/layoutxlm_model.py

diff --git a/framework/e2e/PaddleLT_new/layerNLPcase/debug/case_bug/transformers/layoutlmv2/layoutlmv2_model.py b/framework/e2e/PaddleLT_new/layerNLPcase/debug/case_bug/transformers/layoutlmv2/layoutlmv2_model.py
deleted file mode 100644
index 5eeb853370..0000000000
--- a/framework/e2e/PaddleLT_new/layerNLPcase/debug/case_bug/transformers/layoutlmv2/layoutlmv2_model.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import paddle
-import numpy as np
-from paddlenlp.transformers import LayoutLMv2Model, LayoutLMv2Tokenizer
-
-def LayerCase():
-    """模型库中间态"""
-    model = LayoutLMv2Model.from_pretrained('layoutlmv2-base-uncased')
-    return model
-
-def create_inputspec():
-    inputspec = (
-        paddle.static.InputSpec(shape=(-1, 13), dtype=paddle.float32, stop_gradient=False),
-        paddle.static.InputSpec(shape=(-1, 13, 4), dtype=paddle.int64, stop_gradient=False),
-        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False),
-        paddle.static.InputSpec(shape=(-1, 13), dtype=paddle.float32, stop_gradient=False),
-        )
-    return inputspec
-
-
-def create_tensor_inputs():
-    tokenizer = LayoutLMv2Tokenizer.from_pretrained('layoutlmv2-base-uncased')
-    inputs_dict = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
-    inputs = (
-        paddle.to_tensor([inputs_dict['input_ids']], stop_gradient=False),
-        paddle.to_tensor(np.random.random((1, 13, 4)).astype("int64"), stop_gradient=False),
-        paddle.to_tensor(np.random.random((1, 3, 224, 224)), stop_gradient=False),
-        paddle.to_tensor([inputs_dict['token_type_ids']], stop_gradient=False),
-    )
-    return inputs
-
-
-def create_numpy_inputs():
-    tokenizer = LayoutLMv2Tokenizer.from_pretrained('layoutlmv2-base-uncased')
-    inputs_dict = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
-    inputs = (
-        np.array([inputs_dict['input_ids']]),
-        np.random.random((1, 13, 4)).astype("int64"),
-        np.random.random((1, 3, 224, 224)),
-        np.array([inputs_dict['token_type_ids']]),
-    )
-    return inputs
diff --git a/framework/e2e/PaddleLT_new/layerNLPcase/debug/case_bug/transformers/layoutxlm/layoutxlm_model.py b/framework/e2e/PaddleLT_new/layerNLPcase/debug/case_bug/transformers/layoutxlm/layoutxlm_model.py
deleted file mode 100644
index 894c31cce0..0000000000
--- a/framework/e2e/PaddleLT_new/layerNLPcase/debug/case_bug/transformers/layoutxlm/layoutxlm_model.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import paddle
-import numpy as np
-from paddlenlp.transformers import LayoutXLMModel, LayoutXLMTokenizer
-
-def LayerCase():
-    """模型库中间态"""
-    model = LayoutXLMModel.from_pretrained('layoutxlm-base-uncased')
-    return model
-
-def create_inputspec():
-    inputspec = (
-        paddle.static.InputSpec(shape=(-1, 15), dtype=paddle.float32, stop_gradient=False),
-        paddle.static.InputSpec(shape=(-1, 15, 4), dtype=paddle.int64, stop_gradient=False),
-        paddle.static.InputSpec(shape=(-1, 3, 224, 224), dtype=paddle.float32, stop_gradient=False),
-        )
-    return inputspec
-
-
-def create_tensor_inputs():
-    tokenizer = LayoutXLMTokenizer.from_pretrained('layoutxlm-base-uncased')
-    inputs_dict = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
-    inputs = (
-        paddle.to_tensor([inputs_dict['input_ids']], stop_gradient=False),
-        paddle.to_tensor(np.random.random((1, 15, 4)).astype("int64"), stop_gradient=False),
-        paddle.to_tensor(np.random.random((1, 3, 224, 224)), stop_gradient=False),
-    )
-    return inputs
-
-
-def create_numpy_inputs():
-    tokenizer = LayoutXLMTokenizer.from_pretrained('layoutxlm-base-uncased')
-    inputs_dict = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
-    inputs = (
-        np.array([inputs_dict['input_ids']]),
-        np.random.random((1, 15, 4)).astype("int64"),
-        np.random.random((1, 3, 224, 224)),
-    )
-    return inputs