



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 		_lowercase		:

   def __init__(   self      :							Dict				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							str=2				, __lowerCAmelCase      :							List[str]=8				, __lowerCAmelCase      :							Union[str, Any]=True				, __lowerCAmelCase      :							str=False				, __lowerCAmelCase      :							int=False				, __lowerCAmelCase      :							Optional[int]=False				, __lowerCAmelCase      :							Any=False				, __lowerCAmelCase      :							int=99				, __lowerCAmelCase      :							List[str]=32				, __lowerCAmelCase      :							List[str]=3				, __lowerCAmelCase      :							Tuple=4				, __lowerCAmelCase      :							str=37				, __lowerCAmelCase      :							Any=32				, __lowerCAmelCase      :							Any=128				, __lowerCAmelCase      :							Optional[Any]=8				, __lowerCAmelCase      :							int=8				, __lowerCAmelCase      :							Optional[int]=8				, __lowerCAmelCase      :							Optional[Any]=1.0				, __lowerCAmelCase      :							Tuple=True				, )							->					List[Any]:


    """simple docstring"""






    a							=		parent
    a							=		batch_size
    a							=		seq_length
    a							=		is_training
    a							=		use_token_type_ids
    a							=		use_input_mask
    a							=		use_labels
    a							=		use_mc_token_ids
    a							=		vocab_size
    a							=		hidden_size
    a							=		num_hidden_layers
    a							=		num_attention_heads
    a							=		intermediate_size
    a							=		num_buckets
    a							=		max_distance
    a							=		prompt_length
    a							=		prompt_types
    a							=		segment_types
    a							=		init_std
    a							=		return_dict

   def 		A				(   self      :							List[Any]				)							->					List[str]:


    """simple docstring"""






    a							=		{}
    a							=		ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size				).type(torch.intaa				)
    a							=		False

    a							=		self.get_config()

    return (config, input_ids)

   def 		A				(   self      :							List[Any]				)							->					str:


    """simple docstring"""






    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=snake_case_				, init_std=self.init_std				, return_dict=self.return_dict				, )

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							List[Any]				, *__lowerCAmelCase      :							Any				)							->					str:


    """simple docstring"""






    a							=		CpmAntModel(config=snake_case_				)
    model.to(snake_case_				)
    model.eval()

    a							=		model(**snake_case_				).last_hidden_state

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

   def 		A				(   self      :							Dict				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							Union[str, Any]				, *__lowerCAmelCase      :							Optional[Any]				)							->					Tuple:


    """simple docstring"""






    a							=		CpmAntForCausalLM(snake_case_				)
    model.to(snake_case_				)
    a							=		input_ids['input_ids'].to(snake_case_				)
    model.eval()

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




   def 		A				(   self      :							Optional[int]				)							->					int:


    """simple docstring"""






    a							=		self.prepare_config_and_inputs()
    return config, inputs_dict



@require_torch
class 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
   _UpperCAmelCase  						=      (
       {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
   )

   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False

   def 		A				(   self      :							Dict				)							->					int:


    """simple docstring"""






    a							=		CpmAntModelTester(self				)
    a							=		ConfigTester(self				, config_class=snake_case_				)

   def 		A				(   self      :							Any				)							->					int:


    """simple docstring"""






    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 		A				(   self      :							Optional[Any]				)							->					str:


    """simple docstring"""






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

   def 		A				(   self      :							List[str]				)							->					Optional[Any]:


    """simple docstring"""






    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 		A				(   self      :							Any				)							->					Optional[int]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_cpmant_model(snake_case_				, snake_case_				)




   def 		A				(   self      :							Dict				)							->					Tuple:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_lm_head_model(snake_case_				, snake_case_				)



@require_torch
class 		_lowercase		(							unittest.TestCase						):

   @tooslow
   def 		A				(   self      :							int				)							->					str:


    """simple docstring"""






    a							=		'今天天气真好！'
    a							=		'openbmb/cpm-ant-10b'
    a							=		CpmAntModel.from_pretrained(snake_case_				)
    a							=		CpmAntTokenizer.from_pretrained(snake_case_				)
    a							=		tokenizer(snake_case_				, return_tensors="pt"				)
    a							=		model(**snake_case_				).last_hidden_state

    a							=		torch.tensor(
        [[[6.1_7_0_8, 5.9_2_4_4, 1.0_8_3_5], [6.5_2_0_7, 6.2_8_9_3, -1_1.3_3_2_4], [-1.0_1_0_7, -0.0_5_7_6, -5.9_5_7_7]]]				, )
    self.assertTrue(torch.allclose(hidden_states[:, :3, :3]				, snake_case_				, atol=1E-2				)				)



@require_torch
class 		_lowercase		(							unittest.TestCase						):

   @tooslow
   def 		A				(   self      :							Optional[Any]				)							->					Dict:


    """simple docstring"""






    a							=		'今天天气真好！'
    a							=		'openbmb/cpm-ant-10b'
    a							=		CpmAntForCausalLM.from_pretrained(snake_case_				)
    a							=		CpmAntTokenizer.from_pretrained(snake_case_				)
    a							=		tokenizer(snake_case_				, return_tensors="pt"				)
    a							=		model(**snake_case_				).logits

    a							=		torch.tensor(
        [[[-6.4_2_6_7, -6.4_0_8_3, -6.3_9_5_8], [-5.8_8_0_2, -5.9_4_4_7, -5.7_8_1_1], [-5.3_8_9_6, -5.4_8_2_0, -5.4_2_9_5]]]				, )
    self.assertTrue(torch.allclose(hidden_states[:, :3, :3]				, snake_case_				, atol=1E-2				)				)

   @tooslow
   def 		A				(   self      :							Any				)							->					Tuple:


    """simple docstring"""






    a							=		'openbmb/cpm-ant-10b'
    a							=		CpmAntForCausalLM.from_pretrained(snake_case_				)
    a							=		CpmAntTokenizer.from_pretrained(snake_case_				)
    a							=		'今天天气不错，'
    a							=		'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'
    a							=		tokenizer(snake_case_				, return_tensors="pt"				)
    a							=		model.generate(**snake_case_				)
    a							=		tokenizer.batch_decode(snake_case_				)
    self.assertEqual(snake_case_				, snake_case_				)




   @tooslow
   def 		A				(   self      :							List[Any]				)							->					int:


    """simple docstring"""






    a							=		'openbmb/cpm-ant-10b'
    a							=		CpmAntForCausalLM.from_pretrained(snake_case_				)
    a							=		CpmAntTokenizer.from_pretrained(snake_case_				)
    a							=		['今天天气不错，', '新年快乐，万事如意！']
    a							=		[
        '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',
    ]
    a							=		tokenizer(snake_case_				, return_tensors="pt"				, padding=snake_case_				)
    a							=		model.generate(**snake_case_				)
    a							=		tokenizer.batch_decode(snake_case_				)
    self.assertEqual(snake_case_				, snake_case_				)








"""simple docstring"""




import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					Union[str, Any]             =  CONFIG_NAME

      @register_to_config
      def __init__(	self   , snake_case_ = True   , snake_case_ = 8   , snake_case_ = "lanczos"   , snake_case_ = True   , snake_case_ = False   , ):

        """simple docstring"""

        super().__init__()

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			int									=    images[None, ...]
        A_    :			Union[str, Any]									=    (images * 2_5_5).round().astype('uint8'    )
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          A_    :			List[str]									=    [Image.fromarray(image.squeeze()   , mode='L'    ) for image in images]
        else:
          A_    :			Tuple									=    [Image.fromarray(snake_case_    ) for image in images]

        return pil_images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if not isinstance(snake_case_   , snake_case_    ):
          A_    :			int									=    [images]
        A_    :			int									=    [np.array(snake_case_    ).astype(np.floataa    ) / 2_55.0 for image in images]
        A_    :			Tuple									=    np.stack(snake_case_   , axis=0    )

        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			Any									=    images[..., None]

        A_    :			Dict									=    torch.from_numpy(images.transpose(0   , 3   , 1   , 2    )    )
        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    images.cpu().permute(0   , 2   , 3   , 1    ).float().numpy()
        return images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        return 2.0 * images - 1.0

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        return (images / 2 + 0.5).clamp(0   , 1    )

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        A_    :			List[Any]									=    image.convert('RGB'    )
        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , ):

        """simple docstring"""

        if height is None:
          A_    :			int									=    image.height
        if width is None:
          A_    :			List[Any]									=    image.width

        A_		,			A_    :			Dict									=    (
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        A_    :			Any									=    image.resize((width, height)   , resample=PIL_INTERPOLATION[self.config.resample]    )
        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None   , snake_case_ = None   , ):

        """simple docstring"""

        A_    :			Dict									=    (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(snake_case_   , snake_case_    ):
          A_    :			Dict									=    [image]
        elif not (isinstance(snake_case_   , snake_case_    ) and all(isinstance(snake_case_   , snake_case_    ) for i in image    )):
          raise ValueError(
              F"""Input is in incorrect format: {[type(snake_case_    ) for i in image]}. Currently, we only support {", ".join(snake_case_    )}"""    )

        if isinstance(image[0]   , PIL.Image.Image    ):
          if self.config.do_convert_rgb:
            A_    :			Dict									=    [self.convert_to_rgb(snake_case_    ) for i in image]
          if self.config.do_resize:
            A_    :			Any									=    [self.resize(snake_case_   , snake_case_   , snake_case_    ) for i in image]
          A_    :			Optional[int]									=    self.pil_to_numpy(snake_case_    )  # to np
          A_    :			Dict									=    self.numpy_to_pt(snake_case_    )  # to pt

        elif isinstance(image[0]   , np.ndarray    ):
          A_    :			Optional[Any]									=    np.concatenate(snake_case_   , axis=0    ) if image[0].ndim == 4 else np.stack(snake_case_   , axis=0    )
          A_    :			Optional[Any]									=    self.numpy_to_pt(snake_case_    )
          A_		,			A_		,			A_		,			A_    :			Tuple									=    image.shape
          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                F"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
                F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""    )

        elif isinstance(image[0]   , torch.Tensor    ):
          A_    :			int									=    torch.cat(snake_case_   , axis=0    ) if image[0].ndim == 4 else torch.stack(snake_case_   , axis=0    )
          A_		,			A_		,			A_		,			A_    :			str									=    image.shape

          # don't need any preprocess if the image is latents
          if channel == 4:
            return image

          if self.config.do_resize and (
              height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
          ):
            raise ValueError(
                F"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
                F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""    )

        # expected range [0,1], normalize to [-1,1]
        A_    :			Optional[Any]									=    self.config.do_normalize
        if image.min() < 0:
          warnings.warn(
              'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
              F"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""   , snake_case_   , )
          A_    :			Optional[int]									=    False

        if do_normalize:
          A_    :			Any									=    self.normalize(snake_case_    )

        return image

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "pil"   , snake_case_ = None   , ):

        """simple docstring"""

        if not isinstance(snake_case_   , torch.Tensor    ):
          raise ValueError(
              F"""Input for postprocessing is in incorrect format: {type(snake_case_    )}. We only support pytorch tensor"""    )
        if output_type not in ["latent", "pt", "np", "pil"]:
          A_    :			Optional[Any]									=    (
              F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
              '`pil`, `np`, `pt`, `latent`'
          )
          deprecate('Unsupported output_type'   , '1.0.0'   , snake_case_   , standard_warn=snake_case_    )
          A_    :			Dict									=    'np'

        if output_type == "latent":
          return image

        if do_denormalize is None:
          A_    :			str									=    [self.config.do_normalize] * image.shape[0]

        A_    :			List[Any]									=    torch.stack(
            [self.denormalize(image[i]    ) if do_denormalize[i] else image[i] for i in range(image.shape[0]    )]    )

        if output_type == "pt":
          return image

        A_    :			Optional[Any]									=    self.pt_to_numpy(snake_case_    )

        if output_type == "np":
          return image

        if output_type == "pil":
          return self.numpy_to_pil(snake_case_    )


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					str             =  CONFIG_NAME

      @register_to_config
      def __init__(	self   , snake_case_ = True   , snake_case_ = 8   , snake_case_ = "lanczos"   , snake_case_ = True   , ):

        """simple docstring"""

        super().__init__()

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			int									=    images[None, ...]
        A_    :			Optional[int]									=    (images * 2_5_5).round().astype('uint8'    )
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          A_    :			Optional[int]									=    [Image.fromarray(image.squeeze()   , mode='L'    ) for image in images]
        else:
          A_    :			Optional[Any]									=    [Image.fromarray(image[:, :, :3]    ) for image in images]

        return pil_images

      @staticmethod
      def    lowerCamelCase_     (	snake_case_    ):

        """simple docstring"""

        return image[:, :, 1] * 2**8 + image[:, :, 2]

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        if images.ndim == 3:
          A_    :			Union[str, Any]									=    images[None, ...]
        A_    :			Tuple									=    images[:, :, :, 3:]
        if images.shape[-1] == 6:
          A_    :			Dict									=    (images_depth * 2_5_5).round().astype('uint8'    )
          A_    :			List[Any]									=    [
              Image.fromarray(self.rgblike_to_depthmap(snake_case_    )   , mode='I;16'    ) for image_depth in images_depth
          ]
        elif images.shape[-1] == 4:
          A_    :			Optional[int]									=    (images_depth * 6_55_35.0).astype(np.uintaa    )
          A_    :			Union[str, Any]									=    [Image.fromarray(snake_case_   , mode='I;16'    ) for image_depth in images_depth]
        else:
          raise Exception('Not supported'    )

        return pil_images

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = "pil"   , snake_case_ = None   , ):

        """simple docstring"""

        if not isinstance(snake_case_   , torch.Tensor    ):
          raise ValueError(
              F"""Input for postprocessing is in incorrect format: {type(snake_case_    )}. We only support pytorch tensor"""    )
        if output_type not in ["latent", "pt", "np", "pil"]:
          A_    :			Optional[int]									=    (
              F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
              '`pil`, `np`, `pt`, `latent`'
          )
          deprecate('Unsupported output_type'   , '1.0.0'   , snake_case_   , standard_warn=snake_case_    )
          A_    :			Any									=    'np'

        if do_denormalize is None:
          A_    :			int									=    [self.config.do_normalize] * image.shape[0]

        A_    :			List[str]									=    torch.stack(
            [self.denormalize(image[i]    ) if do_denormalize[i] else image[i] for i in range(image.shape[0]    )]    )

        A_    :			Optional[Any]									=    self.pt_to_numpy(snake_case_    )

        if output_type == "np":
          if image.shape[-1] == 6:
            A_    :			Dict									=    np.stack([self.rgblike_to_depthmap(im[:, :, 3:]    ) for im in image]   , axis=0    )
          else:
            A_    :			str									=    image[:, :, :, 3:]
          return image[:, :, :, :3], image_depth

        if output_type == "pil":
          return self.numpy_to_pil(snake_case_    ), self.numpy_to_depth(snake_case_    )
        else:
          raise Exception(F"""This type {output_type} is not supported"""    )