

"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A		     =    logging.get_logger(__name__)





class 					snake_case			(       __snake_case						):
				SCREAMING_SNAKE_CASE_     :   Dict        =						["""pixel_values"""]

				def __init__(			self							:       int  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Optional[Dict[str, int]] = None  ,						UpperCamelCase__							:       PILImageResampling = PILImageResampling.BILINEAR  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Union[int, float] = 1 / 2_5_5  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						**UpperCamelCase__							:       Optional[Any]  ,						)->     None:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)
						__lowerCAmelCase: Union[str, Any]					   =    size if size is not None else {"height": 2_2_4, "width": 2_2_4}
						__lowerCAmelCase: Optional[Any]					   =    get_size_dict(UpperCamelCase__)
						__lowerCAmelCase: Union[str, Any]					   =    do_resize
						__lowerCAmelCase: List[Any]					   =    do_rescale
						__lowerCAmelCase: Dict					   =    do_normalize
						__lowerCAmelCase: Union[str, Any]					   =    size
						__lowerCAmelCase: Optional[int]					   =    resample
						__lowerCAmelCase: Dict					   =    rescale_factor
						__lowerCAmelCase: int					   =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase: List[Any]					   =    image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							lowercase_  (			self							:       Optional[Any]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       PILImageResampling = PILImageResampling.BILINEAR  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       Any  ,						)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase: Dict					   =    get_size_dict(UpperCamelCase__)
						if "height" not in size or "width" not in size:
								raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
						return resize(
						    UpperCamelCase__  ,						size=(size["height"], size["width"])  ,						resample=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       Optional[int]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       float  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       str)->     np.ndarray:



						'''simple docstring'''


						return rescale(UpperCamelCase__  ,						scale=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       Any  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Union[float, List[float]]  ,						UpperCamelCase__							:       Union[float, List[float]]  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       Union[str, Any]  ,						)->     np.ndarray:



						'''simple docstring'''


						return normalize(UpperCamelCase__  ,						mean=UpperCamelCase__  ,						std=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)




				def 							lowercase_  (			self							:       str  ,						UpperCamelCase__							:       ImageInput  ,						UpperCamelCase__							:       Optional[bool] = None  ,						UpperCamelCase__							:       Dict[str, int] = None  ,						UpperCamelCase__							:       PILImageResampling = None  ,						UpperCamelCase__							:       Optional[bool] = None  ,						UpperCamelCase__							:       Optional[float] = None  ,						UpperCamelCase__							:       Optional[bool] = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[str, TensorType]] = None  ,						UpperCamelCase__							:       Union[str, ChannelDimension] = ChannelDimension.FIRST  ,						**UpperCamelCase__							:       str  ,						)->     List[str]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase: Optional[int]					   =    do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase: List[Any]					   =    do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase: int					   =    resample if resample is not None else self.resample
						__lowerCAmelCase: Any					   =    rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase: Union[str, Any]					   =    image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase: List[Any]					   =    image_std if image_std is not None else self.image_std

						__lowerCAmelCase: Optional[Any]					   =    size if size is not None else self.size
						__lowerCAmelCase: List[Any]					   =    get_size_dict(UpperCamelCase__)

						__lowerCAmelCase: Dict					   =    make_list_of_images(UpperCamelCase__)

						if not valid_images(UpperCamelCase__):
								raise ValueError(
								    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
								    "torch.Tensor, tf.Tensor or jax.ndarray.")

						if do_resize and size is None:
								raise ValueError("Size must be specified if do_resize is True.")

						if do_rescale and rescale_factor is None:
								raise ValueError("Rescale factor must be specified if do_rescale is True.")

						# All transformations expect numpy arrays.
						__lowerCAmelCase: Tuple					   =    [to_numpy_array(UpperCamelCase__) for image in images]

						if do_resize:
								__lowerCAmelCase: Union[str, Any]					   =    [self.resize(image=UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__) for image in images]

						if do_rescale:
								__lowerCAmelCase: Tuple					   =    [self.rescale(image=UpperCamelCase__  ,						scale=UpperCamelCase__) for image in images]

						if do_normalize:
								__lowerCAmelCase: Dict					   =    [self.normalize(image=UpperCamelCase__  ,						mean=UpperCamelCase__  ,						std=UpperCamelCase__) for image in images]

						__lowerCAmelCase: Dict					   =    [to_channel_dimension_format(UpperCamelCase__  ,						UpperCamelCase__) for image in images]

						__lowerCAmelCase: Optional[Any]					   =    {"pixel_values": images}
						return BatchFeature(data=UpperCamelCase__  ,						tensor_type=UpperCamelCase__)





"""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 					snake_case			:

				def __init__(			self							:       str  ,						UpperCamelCase__							:       Optional[int]  ,						)->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: str					   =    parent
						__lowerCAmelCase: Union[str, Any]					   =    1_3
						__lowerCAmelCase: Union[str, Any]					   =    7
						__lowerCAmelCase: List[Any]					   =    True
						__lowerCAmelCase: Any					   =    True
						__lowerCAmelCase: List[Any]					   =    True
						__lowerCAmelCase: str					   =    True
						__lowerCAmelCase: List[Any]					   =    True
						__lowerCAmelCase: Tuple					   =    9_9
						__lowerCAmelCase: Dict					   =    3_2
						__lowerCAmelCase: Optional[Any]					   =    2
						__lowerCAmelCase: Union[str, Any]					   =    4
						__lowerCAmelCase: Union[str, Any]					   =    3_7
						__lowerCAmelCase: List[str]					   =    "gelu"
						__lowerCAmelCase: List[str]					   =    0.1
						__lowerCAmelCase: List[str]					   =    0.1
						__lowerCAmelCase: Union[str, Any]					   =    5_1_2
						__lowerCAmelCase: int					   =    1_6
						__lowerCAmelCase: int					   =    2
						__lowerCAmelCase: str					   =    0.02
						__lowerCAmelCase: Union[str, Any]					   =    3
						__lowerCAmelCase: str					   =    4
						__lowerCAmelCase: List[Any]					   =    None
						__lowerCAmelCase: int					   =    self.vocab_size - 1

				def 							lowercase_  (			self							:       Union[str, Any])->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: Any					   =    ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size)

						__lowerCAmelCase: str					   =    None
						if self.use_input_mask:
								__lowerCAmelCase: List[Any]					   =    random_attention_mask([self.batch_size, self.seq_length])

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

						__lowerCAmelCase: Dict					   =    None
						if self.use_mc_token_ids:
								__lowerCAmelCase: Union[str, Any]					   =    ids_tensor([self.batch_size, self.num_choices]  ,						self.seq_length)

						__lowerCAmelCase: int					   =    None
						__lowerCAmelCase: Optional[Any]					   =    None
						__lowerCAmelCase: int					   =    None
						if self.use_labels:
								__lowerCAmelCase: Tuple					   =    ids_tensor([self.batch_size]  ,						self.type_sequence_label_size)
								__lowerCAmelCase: str					   =    ids_tensor([self.batch_size, self.seq_length]  ,						self.num_labels)
								__lowerCAmelCase: int					   =    ids_tensor([self.batch_size]  ,						self.num_choices)

						__lowerCAmelCase: int					   =    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  ,						)

						__lowerCAmelCase: Union[str, Any]					   =    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 							lowercase_  (			self							:       Optional[int]  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       Dict  ,						*UpperCamelCase__							:       Dict)->     Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase: List[str]					   =    TFOpenAIGPTModel(config=UpperCamelCase__)
						__lowerCAmelCase: Tuple					   =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						__lowerCAmelCase: List[Any]					   =    model(UpperCamelCase__)

						__lowerCAmelCase: List[Any]					   =    [input_ids, input_mask]
						__lowerCAmelCase: Tuple					   =    model(UpperCamelCase__)

						__lowerCAmelCase: List[Any]					   =    model(UpperCamelCase__)

						self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size))

				def 							lowercase_  (			self							:       Dict  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       int  ,						*UpperCamelCase__							:       Optional[Any])->     Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase: Any					   =    TFOpenAIGPTLMHeadModel(config=UpperCamelCase__)
						__lowerCAmelCase: int					   =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						__lowerCAmelCase: Any					   =    model(UpperCamelCase__)
						self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.seq_length, self.vocab_size))

				def 							lowercase_  (			self							:       Tuple  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Tuple  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Optional[Any]  ,						*UpperCamelCase__							:       Optional[Any])->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Union[str, Any]					   =    TFOpenAIGPTDoubleHeadsModel(config=UpperCamelCase__)

						__lowerCAmelCase: List[str]					   =    tf.tile(tf.expand_dims(UpperCamelCase__  ,						1)  ,						(1, self.num_choices, 1))
						__lowerCAmelCase: Union[str, Any]					   =    tf.tile(tf.expand_dims(UpperCamelCase__  ,						1)  ,						(1, self.num_choices, 1))
						__lowerCAmelCase: Optional[Any]					   =    tf.tile(tf.expand_dims(UpperCamelCase__  ,						1)  ,						(1, self.num_choices, 1))

						__lowerCAmelCase: str					   =    {
						    "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,
						}
						__lowerCAmelCase: Optional[int]					   =    model(UpperCamelCase__)
						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 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       Optional[Any]  ,						UpperCamelCase__							:       Any  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Optional[Any]  ,						*UpperCamelCase__							:       List[str])->     Any:



						'''simple docstring'''


						__lowerCAmelCase: str					   =    self.num_labels
						__lowerCAmelCase: Dict					   =    ids_tensor([self.batch_size]  ,						self.type_sequence_label_size)
						__lowerCAmelCase: List[str]					   =    {
						    "input_ids": input_ids,
						    "attention_mask": input_mask,
						    "token_type_ids": token_type_ids,
						    "labels": sequence_labels,
						}
						__lowerCAmelCase: str					   =    TFOpenAIGPTForSequenceClassification(UpperCamelCase__)
						__lowerCAmelCase: Optional[int]					   =    model(UpperCamelCase__)
						self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels))




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



						'''simple docstring'''


						__lowerCAmelCase: List[str]					   =    self.prepare_config_and_inputs()

						(
						    (
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				(
						    __lowerCAmelCase
						)   ,				
						): List[Any]					   =    config_and_inputs

						__lowerCAmelCase: List[Any]					   =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
						return config, inputs_dict





@require_tf
class 					snake_case			(       __snake_case,							__snake_case,							unittest.TestCase						):
				SCREAMING_SNAKE_CASE_     :   Dict        =						(
				    (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
				    if is_tf_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_     :   List[str]        =						(
				    (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
				SCREAMING_SNAKE_CASE_     :   int        =						(
				    {
				        """feature-extraction""": TFOpenAIGPTModel,
				        """text-classification""": TFOpenAIGPTForSequenceClassification,
				        """text-generation""": TFOpenAIGPTLMHeadModel,
				        """zero-shot""": TFOpenAIGPTForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_     :   List[Any]        =						False
				SCREAMING_SNAKE_CASE_     :   Tuple        =						False

				def 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Optional[int]  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Optional[Any])->     Union[str, Any]:



						'''simple docstring'''


						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 							lowercase_  (			self							:       str)->     Tuple:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    TFOpenAIGPTModelTester(self)
						__lowerCAmelCase: List[Any]					   =    ConfigTester(self  ,						config_class=UpperCamelCase__  ,						n_embd=3_7)

				def 							lowercase_  (			self							:       List[Any])->     List[Any]:



						'''simple docstring'''


						self.config_tester.run_common_tests()

				def 							lowercase_  (			self							:       Tuple)->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_model(*UpperCamelCase__)

				def 							lowercase_  (			self							:       Union[str, Any])->     Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_lm_head(*UpperCamelCase__)

				def 							lowercase_  (			self							:       Optional[Any])->     Dict:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_double_head(*UpperCamelCase__)

				def 							lowercase_  (			self							:       List[str])->     Tuple:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*UpperCamelCase__)




				@slow
				def 							lowercase_  (			self							:       str)->     Dict:



						'''simple docstring'''


						for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowerCAmelCase: str					   =    TFOpenAIGPTModel.from_pretrained(UpperCamelCase__)
								self.assertIsNotNone(UpperCamelCase__)





@require_tf
class 					snake_case			(       unittest.TestCase						):

				@slow
				def 							lowercase_  (			self							:       Union[str, Any])->     List[str]:



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
						__lowerCAmelCase: List[str]					   =    tf.convert_to_tensor([[4_8_1, 4_7_3_5, 5_4_4]]  ,						dtype=tf.intaa)  # the president is
						__lowerCAmelCase: Union[str, Any]					   =    [
						    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

						__lowerCAmelCase: str					   =    model.generate(UpperCamelCase__  ,						do_sample=UpperCamelCase__)
						self.assertListEqual(output_ids[0].numpy().tolist()  ,						UpperCamelCase__)


