





import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class 			__A(    unittest.TestCase			):

     @slow
     @unittest.skip('''Model is not available.'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    '''nvidia/megatron-gpt2-345m/'''
            if "MYDIR" in os.environ:
                   _UpperCamelCase														=    os.path.join(os.environ['''MYDIR'''],							A   )
            _UpperCamelCase														=    GPTaLMHeadModel.from_pretrained(A   )
            model.to(A   )
            model.half()

            _UpperCamelCase														=    torch.tensor(
                [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]],							device=A,							dtype=torch.long,							)

            with torch.no_grad():
                   _UpperCamelCase														=    model(A   ).logits

            _UpperCamelCase														=    torch.Size((1, 9, 5_0257)   )
            self.assertEqual(output.shape,							A   )

            _UpperCamelCase														=    torch.tensor(
                [
                    4.9_414,
                    -0.2_920,
                    -1.2_148,
                    -4.0_273,
                    -0.5_161,
                    -5.2_109,
                    -1.2_412,
                    -1.8_301,
                    -1.7_734,
                    -4.7_148,
                    -0.2_317,
                    -1.0_811,
                    -2.1_777,
                    0.4_141,
                    -3.7_969,
                    -4.0_586,
                    -2.5_332,
                    -3.3_809,
                    4.3_867,
                ],							device=A,							dtype=torch.half,							)

            for i in range(19   ):
                   _UpperCamelCase							,		_UpperCamelCase														=    8 * i // 17, 2792 * i  # along the diagonal
                   _UpperCamelCase							,		_UpperCamelCase														=    output[0, r, c], expected_diag[i]
                   _UpperCamelCase														=    F'''row={r} col={c} computed={computed} expected={expected}'''
                   self.assertAlmostEqual(A,							A,							delta=1E-4,							msg=A   )








'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=self.device   )

								# set step values
								self.scheduler.set_timesteps(UpperCamelCase   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE					=			self.unet(UpperCamelCase       ,    UpperCamelCase   ).sample

														# 2. compute previous image: x_t -> x_t-1
														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase   ).prev_sample

								_SCREAMING_SNAKE_CASE					=			(image / 2 + 0.5).clamp(0       ,    1   )
								_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )