"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class   UpperCAmelCase_	(				lowercase__   ):
    def __init__(	self			:       Tuple   ,						_lowercase			:       Dict   ,						_lowercase			:       List[Any]		)						->    Union[str, Any]:
       super().__init__()
       self.register_modules(unet=_lowercase   ,						scheduler=_lowercase		)




    @torch.no_grad()
    def __call__(	self			:       str   ,						_lowercase			:       int = 1   ,						_lowercase			:       Optional[torch.Generator] = None   ,						_lowercase			:       int = 5_0   ,						_lowercase			:       Optional[str] = "pil"   ,						_lowercase			:       bool = True   ,						**_lowercase			:       Optional[Any]   ,						)						->    Union[ImagePipelineOutput, Tuple]:
       _lowercase							     =       torch.randn(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,						generator=_lowercase   ,						)
       _lowercase							     =       image.to(self.device		)

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

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

          # 2. predict previous mean of image x_t-1 and add variance depending on eta
          # eta corresponds to η in paper and should be between [0, 1]
          # do x_t -> x_t-1
          _lowercase							     =       self.scheduler.step(_lowercase   ,						_lowercase   ,						_lowercase		).prev_sample

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

       if not return_dict:
          return (image,), "This is a local test"

       return ImagePipelineOutput(images=_lowercase		), "This is a local test"
"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.openai.modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTModel,
  )
class   UpperCAmelCase_	:
    def __init__(	self			:       Tuple   ,						_lowercase			:       List[str]   ,						)						->    Tuple:
       _lowercase							     =       parent
       _lowercase							     =       1_3
       _lowercase							     =       7
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       9_9
       _lowercase							     =       3_2
       _lowercase							     =       2
       _lowercase							     =       4
       _lowercase							     =       3_7
       _lowercase							     =       "gelu"
       _lowercase							     =       0.1
       _lowercase							     =       0.1
       _lowercase							     =       5_1_2
       _lowercase							     =       1_6
       _lowercase							     =       2
       _lowercase							     =       0.02
       _lowercase							     =       3
       _lowercase							     =       4
       _lowercase							     =       None
       _lowercase							     =       self.vocab_size - 1
    def 		_lowerCamelCase      (	self			:       int		)						->    List[Any]:
       _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size		)

       _lowercase							     =       None
       if self.use_input_mask:
          _lowercase							     =       random_attention_mask([self.batch_size, self.seq_length]		)

       _lowercase							     =       None
       if self.use_token_type_ids:
          _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size		)

       _lowercase							     =       None
       if self.use_mc_token_ids:
          _lowercase							     =       ids_tensor([self.batch_size, self.num_choices]   ,						self.seq_length		)

       _lowercase							     =       None
       _lowercase							     =       None
       _lowercase							     =       None
       if self.use_labels:
          _lowercase							     =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
          _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.num_labels		)
          _lowercase							     =       ids_tensor([self.batch_size]   ,						self.num_choices		)

       _lowercase							     =       OpenAIGPTConfig(
           vocab_size=self.vocab_size   ,						n_embd=self.hidden_size   ,						n_layer=self.num_hidden_layers   ,						n_head=self.num_attention_heads   ,						n_positions=self.max_position_embeddings   ,						pad_token_id=self.pad_token_id   ,						)

       _lowercase							     =       ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,						2		)

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
           token_type_ids,
           mc_token_ids,
           sequence_labels,
           token_labels,
           choice_labels,
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       str   ,						_lowercase			:       Dict   ,						_lowercase			:       List[Any]   ,						_lowercase			:       int   ,						*_lowercase			:       List[str]		)						->    Optional[int]:
       _lowercase							     =       TFOpenAIGPTModel(config=_lowercase		)
       _lowercase							     =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       _lowercase							     =       model(_lowercase		)

       _lowercase							     =       [input_ids, input_mask]
       _lowercase							     =       model(_lowercase		)

       _lowercase							     =       model(_lowercase		)

       self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       str   ,						_lowercase			:       Dict   ,						_lowercase			:       List[str]   ,						*_lowercase			:       Optional[Any]		)						->    Dict:
       _lowercase							     =       TFOpenAIGPTLMHeadModel(config=_lowercase		)
       _lowercase							     =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       int   ,						_lowercase			:       str   ,						_lowercase			:       int   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       List[Any]   ,						*_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       TFOpenAIGPTDoubleHeadsModel(config=_lowercase		)

       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)
       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)
       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)

       _lowercase							     =       {
           "input_ids": multiple_choice_inputs_ids,
           "mc_token_ids": mc_token_ids,
           "attention_mask": multiple_choice_input_mask,
           "token_type_ids": multiple_choice_token_type_ids,
       }
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(
           result.logits.shape   ,						(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)		)
       self.parent.assertEqual(result.mc_logits.shape   ,						(self.batch_size, self.num_choices)		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[Any]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Any   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       List[Any]   ,						*_lowercase			:       str		)						->    str:
       _lowercase							     =       self.num_labels
       _lowercase							     =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
       _lowercase							     =       {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
           "labels": sequence_labels,
       }
       _lowercase							     =       TFOpenAIGPTForSequenceClassification(_lowercase		)
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)		)




    def 		_lowerCamelCase      (	self			:       str		)						->    Tuple:
       _lowercase							     =       self.prepare_config_and_inputs()

       (
           (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  
       )							     =       config_and_inputs

       _lowercase							     =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict
@require_tf
class   UpperCAmelCase_	(				lowercase__,    lowercase__,    unittest.TestCase   ):
    snake_case_											=      (
        (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
        if is_tf_available()
        else ()
    )
    snake_case_											=      (
        (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
    snake_case_											=      (
        {
            """feature-extraction""": TFOpenAIGPTModel,
            """text-classification""": TFOpenAIGPTForSequenceClassification,
            """text-generation""": TFOpenAIGPTLMHeadModel,
            """zero-shot""": TFOpenAIGPTForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    snake_case_											=      False
    snake_case_											=      False
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       List[str]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Union[str, Any]   ,						_lowercase			:       Any		)						->    List[str]:
       if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
          # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
          # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
          # tiny config could not be created.
          return True

       return False
    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    str:
       _lowercase							     =       TFOpenAIGPTModelTester(self		)
       _lowercase							     =       ConfigTester(self   ,						config_class=_lowercase   ,						n_embd=3_7		)
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    Optional[int]:
       self.config_tester.run_common_tests()
    def 		_lowerCamelCase      (	self			:       Any		)						->    List[str]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_model(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    List[Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_lm_head(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    List[Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_double_head(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       int		)						->    Union[str, Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowercase		)




    @slow
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Dict:
       for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowercase							     =       TFOpenAIGPTModel.from_pretrained(_lowercase		)
          self.assertIsNotNone(_lowercase		)
@require_tf
class   UpperCAmelCase_	(				unittest.TestCase   ):
    @slow
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    Union[str, Any]:
       _lowercase							     =       TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt"		)
       _lowercase							     =       tf.convert_to_tensor([[4_8_1, 4_7_3_5, 5_4_4]]   ,						dtype=tf.intaa		)  # the president is
       _lowercase							     =       [
           4_8_1,
           4_7_3_5,
           5_4_4,
           2_4_6,
           9_6_3,
           8_7_0,
           7_6_2,
           2_3_9,
           2_4_4,
           4_0_4_7_7,
           2_4_4,
           2_4_9,
           7_1_9,
           8_8_1,
           4_8_7,
           5_4_4,
           2_4_0,
           2_4_4,
           6_0_3,
           4_8_1,
       ]  # the president is a very good man. " \n " i\'m sure he is, " said the

       _lowercase							     =       model.generate(_lowercase   ,						do_sample=_lowercase		)
       self.assertListEqual(output_ids[0].numpy().tolist()   ,						_lowercase		)