# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
# coding=utf-8
# Copyright 2022 The OpenBMB Team and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch CPMAnt model. """

import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        CpmAntConfig,
        CpmAntForCausalLM,
        CpmAntModel,
        CpmAntTokenizer,
    )


@require_torch
class CpmAntModelTester:
    def __init__(
        self,
        parent,
        batch_size=2,
        seq_length=8,
        is_training=True,
        use_token_type_ids=False,
        use_input_mask=False,
        use_labels=False,
        use_mc_token_ids=False,
        vocab_size=99,
        hidden_size=32,
        num_hidden_layers=3,
        num_attention_heads=4,
        intermediate_size=37,
        num_buckets=32,
        max_distance=128,
        prompt_length=8,
        prompt_types=8,
        segment_types=8,
        init_std=1.0,
        return_dict=True,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_token_type_ids = use_token_type_ids
        self.use_input_mask = use_input_mask
        self.use_labels = use_labels
        self.use_mc_token_ids = use_mc_token_ids
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.num_buckets = num_buckets
        self.max_distance = max_distance
        self.prompt_length = prompt_length
        self.prompt_types = prompt_types
        self.segment_types = segment_types
        self.init_std = init_std
        self.return_dict = return_dict

    def prepare_config_and_inputs(self):
        input_ids = {}
        input_ids["input_ids"] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).type(torch.int32)
        input_ids["use_cache"] = False

        config = self.get_config()

        return (config, input_ids)

    def get_config(self):
        return CpmAntConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            dim_ff=self.intermediate_size,
            position_bias_num_buckets=self.num_buckets,
            position_bias_max_distance=self.max_distance,
            prompt_types=self.prompt_types,
            prompt_length=self.prompt_length,
            segment_types=self.segment_types,
            use_cache=True,
            init_std=self.init_std,
            return_dict=self.return_dict,
        )

    def create_and_check_cpmant_model(self, config, input_ids, *args):
        model = CpmAntModel(config=config)
        model.to(torch_device)
        model.eval()

        hidden_states = model(**input_ids).last_hidden_state

        self.parent.assertEqual(hidden_states.shape, (self.batch_size, self.seq_length, config.hidden_size))

    def create_and_check_lm_head_model(self, config, input_ids, *args):
        model = CpmAntForCausalLM(config)
        model.to(torch_device)
        input_ids["input_ids"] = input_ids["input_ids"].to(torch_device)
        model.eval()

        model_output = model(**input_ids)
        self.parent.assertEqual(
            model_output.logits.shape,
            (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length),
        )

    def prepare_config_and_inputs_for_common(self):
        config, inputs_dict = self.prepare_config_and_inputs()
        return config, inputs_dict


@require_torch
class CpmAntModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": CpmAntModel, "text-generation": CpmAntForCausalLM} if is_torch_available() else {}
    )

    test_pruning = False
    test_missing_keys = False
    test_mismatched_shapes = False
    test_head_masking = False
    test_resize_embeddings = False

    def setUp(self):
        self.model_tester = CpmAntModelTester(self)
        self.config_tester = ConfigTester(self, config_class=CpmAntConfig)

    def test_config(self):
        self.config_tester.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

    def test_inputs_embeds(self):
        unittest.skip("CPMAnt doesn't support input_embeds.")(self.test_inputs_embeds)

    def test_retain_grad_hidden_states_attentions(self):
        unittest.skip(
            "CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model."
        )(self.test_retain_grad_hidden_states_attentions)

    def test_cpmant_model(self):
        config, inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_cpmant_model(config, inputs)

    def test_cpmant_lm_head_model(self):
        config, inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(config, inputs)


@require_torch
class CpmAntModelIntegrationTest(unittest.TestCase):
    @tooslow
    def test_inference_masked_lm(self):
        texts = "今天天气真好！"
        model_path = "openbmb/cpm-ant-10b"
        model = CpmAntModel.from_pretrained(model_path)
        tokenizer = CpmAntTokenizer.from_pretrained(model_path)
        inputs = tokenizer(texts, return_tensors="pt")
        hidden_states = model(**inputs).last_hidden_state

        expected_slice = torch.tensor(
            [[[6.1708, 5.9244, 1.0835], [6.5207, 6.2893, -11.3324], [-1.0107, -0.0576, -5.9577]]],
        )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3], expected_slice, atol=1e-2))


@require_torch
class CpmAntForCausalLMlIntegrationTest(unittest.TestCase):
    @tooslow
    def test_inference_casual(self):
        texts = "今天天气真好！"
        model_path = "openbmb/cpm-ant-10b"
        model = CpmAntForCausalLM.from_pretrained(model_path)
        tokenizer = CpmAntTokenizer.from_pretrained(model_path)
        inputs = tokenizer(texts, return_tensors="pt")
        hidden_states = model(**inputs).logits

        expected_slice = torch.tensor(
            [[[-6.4267, -6.4083, -6.3958], [-5.8802, -5.9447, -5.7811], [-5.3896, -5.4820, -5.4295]]],
        )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3], expected_slice, atol=1e-2))

    @tooslow
    def test_simple_generation(self):
        model_path = "openbmb/cpm-ant-10b"
        model = CpmAntForCausalLM.from_pretrained(model_path)
        tokenizer = CpmAntTokenizer.from_pretrained(model_path)
        texts = "今天天气不错，"
        expected_output = "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"
        model_inputs = tokenizer(texts, return_tensors="pt")
        token_ids = model.generate(**model_inputs)
        output_texts = tokenizer.batch_decode(token_ids)
        self.assertEqual(expected_output, output_texts)

    @tooslow
    def test_batch_generation(self):
        model_path = "openbmb/cpm-ant-10b"
        model = CpmAntForCausalLM.from_pretrained(model_path)
        tokenizer = CpmAntTokenizer.from_pretrained(model_path)
        texts = ["今天天气不错，", "新年快乐，万事如意！"]
        expected_output = [
            "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的",
            "新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的",
        ]
        model_inputs = tokenizer(texts, return_tensors="pt", padding=True)
        token_ids = model.generate(**model_inputs)
        output_texts = tokenizer.batch_decode(token_ids)
        self.assertEqual(expected_output, output_texts)

# coding=utf-8
# Copyright 2022 The OpenBMB Team and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Testing suite for the PyTorch CPMAnt model. """

import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        CpmAntConfig,
        CpmAntForCausalLM,
        CpmAntModel,
        CpmAntTokenizer,
    )


@require_torch
class CpmAntModelTester:
    def __init__(
        self,
        parent,
        batch_size=2,
        seq_length=8,
        is_training=True,
        use_token_type_ids=False,
        use_input_mask=False,
        use_labels=False,
        use_mc_token_ids=False,
        vocab_size=99,
        hidden_size=32,
        num_hidden_layers=3,
        num_attention_heads=4,
        intermediate_size=37,
        num_buckets=32,
        max_distance=128,
        prompt_length=8,
        prompt_types=8,
        segment_types=8,
        init_std=1.0,
        return_dict=True,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_token_type_ids = use_token_type_ids
        self.use_input_mask = use_input_mask
        self.use_labels = use_labels
        self.use_mc_token_ids = use_mc_token_ids
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.num_buckets = num_buckets
        self.max_distance = max_distance
        self.prompt_length = prompt_length
        self.prompt_types = prompt_types
        self.segment_types = segment_types
        self.init_std = init_std
        self.return_dict = return_dict

    def prepare_config_and_inputs(self):
        input_ids = {}
        input_ids["input_ids"] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).type(torch.int32)
        input_ids["use_cache"] = False

        config = self.get_config()

        return (config, input_ids)

    def get_config(self):
        return CpmAntConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            dim_ff=self.intermediate_size,
            position_bias_num_buckets=self.num_buckets,
            position_bias_max_distance=self.max_distance,
            prompt_types=self.prompt_types,
            prompt_length=self.prompt_length,
            segment_types=self.segment_types,
            use_cache=True,
            init_std=self.init_std,
            return_dict=self.return_dict,
        )

    def create_and_check_cpmant_model(self, config, input_ids, *args):
        model = CpmAntModel(config=config)
        model.to(torch_device)
        model.eval()

        hidden_states = model(**input_ids).last_hidden_state

        self.parent.assertEqual(hidden_states.shape, (self.batch_size, self.seq_length, config.hidden_size))

    def create_and_check_lm_head_model(self, config, input_ids, *args):
        model = CpmAntForCausalLM(config)
        model.to(torch_device)
        input_ids["input_ids"] = input_ids["input_ids"].to(torch_device)
        model.eval()

        model_output = model(**input_ids)
        self.parent.assertEqual(
            model_output.logits.shape,
            (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length),
        )

    def prepare_config_and_inputs_for_common(self):
        config, inputs_dict = self.prepare_config_and_inputs()
        return config, inputs_dict


@require_torch
class CpmAntModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    all_model_classes = (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"feature-extraction": CpmAntModel, "text-generation": CpmAntForCausalLM} if is_torch_available() else {}
    )

    test_pruning = False
    test_missing_keys = False
    test_mismatched_shapes = False
    test_head_masking = False
    test_resize_embeddings = False

    def setUp(self):
        self.model_tester = CpmAntModelTester(self)
        self.config_tester = ConfigTester(self, config_class=CpmAntConfig)

    def test_config(self):
        self.config_tester.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

    def test_inputs_embeds(self):
        unittest.skip("CPMAnt doesn't support input_embeds.")(self.test_inputs_embeds)

    def test_retain_grad_hidden_states_attentions(self):
        unittest.skip(
            "CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model."
        )(self.test_retain_grad_hidden_states_attentions)

    def test_cpmant_model(self):
        config, inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_cpmant_model(config, inputs)

    def test_cpmant_lm_head_model(self):
        config, inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(config, inputs)


@require_torch
class CpmAntModelIntegrationTest(unittest.TestCase):
    @tooslow
    def test_inference_masked_lm(self):
        texts = "今天天气真好！"
        model_path = "openbmb/cpm-ant-10b"
        model = CpmAntModel.from_pretrained(model_path)
        tokenizer = CpmAntTokenizer.from_pretrained(model_path)
        inputs = tokenizer(texts, return_tensors="pt")
        hidden_states = model(**inputs).last_hidden_state

        expected_slice = torch.tensor(
            [[[6.1708, 5.9244, 1.0835], [6.5207, 6.2893, -11.3324], [-1.0107, -0.0576, -5.9577]]],
        )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3], expected_slice, atol=1e-2))


@require_torch
class CpmAntForCausalLMlIntegrationTest(unittest.TestCase):
    @tooslow
    def test_inference_casual(self):
        texts = "今天天气真好！"
        model_path = "openbmb/cpm-ant-10b"
        model = CpmAntForCausalLM.from_pretrained(model_path)
        tokenizer = CpmAntTokenizer.from_pretrained(model_path)
        inputs = tokenizer(texts, return_tensors="pt")
        hidden_states = model(**inputs).logits

        expected_slice = torch.tensor(
            [[[-6.4267, -6.4083, -6.3958], [-5.8802, -5.9447, -5.7811], [-5.3896, -5.4820, -5.4295]]],
        )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3], expected_slice, atol=1e-2))

    @tooslow
    def test_simple_generation(self):
        model_path = "openbmb/cpm-ant-10b"
        model = CpmAntForCausalLM.from_pretrained(model_path)
        tokenizer = CpmAntTokenizer.from_pretrained(model_path)
        texts = "今天天气不错，"
        expected_output = "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"
        model_inputs = tokenizer(texts, return_tensors="pt")
        token_ids = model.generate(**model_inputs)
        output_texts = tokenizer.batch_decode(token_ids)
        self.assertEqual(expected_output, output_texts)

    @tooslow
    def test_batch_generation(self):
        model_path = "openbmb/cpm-ant-10b"
        model = CpmAntForCausalLM.from_pretrained(model_path)
        tokenizer = CpmAntTokenizer.from_pretrained(model_path)
        texts = ["今天天气不错，", "新年快乐，万事如意！"]
        expected_output = [
            "今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的",
            "新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的",
        ]
        model_inputs = tokenizer(texts, return_tensors="pt", padding=True)
        token_ids = model.generate(**model_inputs)
        output_texts = tokenizer.batch_decode(token_ids)
        self.assertEqual(expected_output, output_texts)
