


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_keras_nlp_available():
	from transformers.models.gpta import TFGPTaTokenizer


A			:						Optional[int]        =  ['gpt2']
A			:						Any        =  'gpt2'

if is_tf_available():



	class 				A       (			tf.Module				):
						'''simple docstring'''






						def __init__(self       :		List[str] ,   _UpperCAmelCase       :		List[Any]						)		->  Any:




								"""simple docstring"""


								super().__init__()
								lowercase__	     =						tokenizer
								lowercase__	     =						AutoConfig.from_pretrained(_UpperCAmelCase						)
								lowercase__	     =						TFGPTaLMHeadModel.from_config(_UpperCAmelCase						)


						@tf.function(input_signature=(tf.TensorSpec((None,) ,   tf.string ,   name="""text"""						),)						)
						def     lowerCamelCase__	(self       :		Dict ,   _UpperCAmelCase       :		Tuple						)		->  str:




								"""simple docstring"""


								lowercase__	     =						self.tokenizer(_UpperCAmelCase						)
								lowercase__	     =						tokenized["""input_ids"""].to_tensor()

								lowercase__	     =						tf.cast(input_ids_dense > 0 ,   tf.intaa						)
								# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

								lowercase__	     =						self.model(input_ids=_UpperCAmelCase ,   attention_mask=_UpperCAmelCase						)["""logits"""]

								return outputs



@require_tf
@require_keras_nlp
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






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




							"""simple docstring"""


							super().setUp()

							lowercase__	     =						[GPTaTokenizer.from_pretrained(_UpperCAmelCase						) for checkpoint in (TOKENIZER_CHECKPOINTS)]
							lowercase__	     =						[TFGPTaTokenizer.from_pretrained(_UpperCAmelCase						) for checkpoint in TOKENIZER_CHECKPOINTS]
							assert len(self.tokenizers						) == len(self.tf_tokenizers						)

							lowercase__	     =						[
							    """This is a straightforward English test sentence.""",
							    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
							    """Now we're going to add some Chinese: 一 二 三 一二三""",
							    """And some much more rare Chinese: 齉 堃 齉堃""",
							    """Je vais aussi écrire en français pour tester les accents""",
							    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
							]
							lowercase__	     =						list(zip(self.test_sentences ,   self.test_sentences[::-1]						)						)

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




							"""simple docstring"""


							for tokenizer, tf_tokenizer in zip(self.tokenizers ,   self.tf_tokenizers						):
									for test_inputs in self.test_sentences:
											lowercase__	     =						tokenizer([test_inputs] ,   return_tensors="""tf"""						)
											lowercase__	     =						tf_tokenizer([test_inputs]						)

											for key in python_outputs.keys():
													# convert them to numpy to avoid messing with ragged tensors
													lowercase__	     =						python_outputs[key].numpy()
													lowercase__	     =						tf_outputs[key].numpy()

													self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape						)						)
													self.assertTrue(tf.reduce_all(tf.cast(_UpperCAmelCase ,   tf.intaa						) == tf_outputs_values						)						)

					@slow
					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Union[str, Any]:




							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
									lowercase__	     =						tf.function(_UpperCAmelCase						)
									for test_inputs in self.test_sentences:
											lowercase__	     =						tf.constant(_UpperCAmelCase						)
											lowercase__	     =						compiled_tokenizer(_UpperCAmelCase						)
											lowercase__	     =						tf_tokenizer(_UpperCAmelCase						)

											for key in eager_outputs.keys():
													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)

					@slow
					def     lowerCamelCase__	(self       :		Tuple						)		->  int:




							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
									lowercase__	     =						ModelToSave(tokenizer=_UpperCAmelCase						)
									lowercase__	     =						tf.convert_to_tensor([self.test_sentences[0]]						)
									lowercase__	     =						model.serving(_UpperCAmelCase						)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
											lowercase__	     =						Path(_UpperCAmelCase						) / """saved.model"""
											tf.saved_model.save(_UpperCAmelCase ,   _UpperCAmelCase ,   signatures={"""serving_default""": model.serving}						)
											lowercase__	     =						tf.saved_model.load(_UpperCAmelCase						)
									lowercase__	     =						loaded_model.signatures["""serving_default"""](_UpperCAmelCase						)["""output_0"""]
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output						)						)

					@slow
					def     lowerCamelCase__	(self       :		int						)		->  Optional[Any]:




							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
									lowercase__	     =						tf.convert_to_tensor([self.test_sentences[0]]						)
									lowercase__	     =						tf_tokenizer(_UpperCAmelCase						)  # Build model with some sample inputs

									lowercase__	     =						tf_tokenizer.get_config()
									lowercase__	     =						TFGPTaTokenizer.from_config(_UpperCAmelCase						)
									lowercase__	     =						model_from_config(_UpperCAmelCase						)

									for key in from_config_output.keys():
											self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]						)						)


					@slow
					def     lowerCamelCase__	(self       :		List[str]						)		->  str:




							"""simple docstring"""


							for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									lowercase__	     =						12_3123

									for max_length in [3, 5, 1024]:
											lowercase__	     =						tf.convert_to_tensor([self.test_sentences[0]]						)
											lowercase__	     =						tf_tokenizer(_UpperCAmelCase ,   max_length=_UpperCAmelCase						)

											lowercase__	     =						out["""input_ids"""].numpy().shape[1]

											assert out_length == max_length







import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


a__		     =     {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class    SCREAMING_SNAKE_CASE_				(       nn.Module   ):



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						List[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						super().__init__()
						__UpperCamelCase					:	Tuple             =      torchvision.models.resnetaaa(pretrained=lowerCAmelCase	)
						__UpperCamelCase					:	Union[str, Any]             =      list(model.children()	)[:-2]
						__UpperCamelCase					:	List[str]             =      nn.Sequential(*lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]	)






	def 					lowerCamelCase__     (					self		:						List[str]					,     lowerCAmelCase		:						Optional[Any]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.pool(self.model(lowerCAmelCase	)	)
						__UpperCamelCase					:	Dict             =      torch.flatten(lowerCAmelCase					,     start_dim=2	)
						__UpperCamelCase					:	Optional[int]             =      out.transpose(1					,     2	).contiguous()
						return out  # BxNx2048






class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	def __init__(					self		:						Optional[Any]					,     lowerCAmelCase		:						str					,     lowerCAmelCase		:						Dict					,     lowerCAmelCase		:						List[str]					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Optional[int]	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      [json.loads(lowerCAmelCase	) for l in open(lowerCAmelCase	)]
						__UpperCamelCase					:	Optional[Any]             =      os.path.dirname(lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      tokenizer
						__UpperCamelCase					:	Dict             =      labels
						__UpperCamelCase					:	int             =      len(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      max_seq_length

						__UpperCamelCase					:	int             =      transforms






	def __len__(					self		:						Union[str, Any]	)	->       List[Any]:







						"""simple docstring"""







						return len(self.data	)






	def __getitem__(					self		:						Optional[Any]					,     lowerCAmelCase		:						List[Any]	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					,     add_special_tokens=lowerCAmelCase	)	)
						__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      sentence[0], sentence[1:-1], sentence[-1]
						__UpperCamelCase					:	Optional[int]             =      sentence[: self.max_seq_length]

						__UpperCamelCase					:	List[str]             =      torch.zeros(self.n_classes	)
						__UpperCamelCase					:	List[str]             =      1

						__UpperCamelCase					:	List[Any]             =      Image.open(os.path.join(self.data_dir					,     self.data[index]["""img"""]	)	).convert("""RGB"""	)
						__UpperCamelCase					:	Dict             =      self.transforms(lowerCAmelCase	)

						return {
						    "image_start_token": start_token,
						    "image_end_token": end_token,
						    "sentence": sentence,
						    "image": image,
						    "label": label,
						}






	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Optional[int]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      Counter()
						for row in self.data:
											label_freqs.update(row["""label"""]	)
						return label_freqs






def 				A__ (snake_case   :		int				)     ->						Optional[int]:
					__UpperCamelCase					:	str             =      [len(row["""sentence"""]				) for row in batch]
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      len(snake_case				), max(snake_case				)

					__UpperCamelCase					:	List[str]             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)
					__UpperCamelCase					:	Tuple             =      torch.zeros(snake_case		,	snake_case		,	dtype=torch.long				)

					for i_batch, (input_row, length) in enumerate(zip(snake_case		,	snake_case				)				):
										__UpperCamelCase					:	Optional[Any]             =      input_row["""sentence"""]
										__UpperCamelCase					:	Any             =      1

					__UpperCamelCase					:	List[Any]             =      torch.stack([row["""image"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""label"""] for row in batch]				)
					__UpperCamelCase					:	Tuple             =      torch.stack([row["""image_start_token"""] for row in batch]				)
					__UpperCamelCase					:	List[str]             =      torch.stack([row["""image_end_token"""] for row in batch]				)

					return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def 				A__ ()     ->						Optional[Any]:
					return [
					    "Crime",
					    "Drama",
					    "Thriller",
					    "Action",
					    "Comedy",
					    "Romance",
					    "Documentary",
					    "Short",
					    "Mystery",
					    "History",
					    "Family",
					    "Adventure",
					    "Fantasy",
					    "Sci-Fi",
					    "Western",
					    "Horror",
					    "Sport",
					    "War",
					    "Music",
					    "Musical",
					    "Animation",
					    "Biography",
					    "Film-Noir",
					]







def 				A__ ()     ->						Union[str, Any]:
					return transforms.Compose(
					    [
					        transforms.Resize(2_56				),
					        transforms.CenterCrop(2_24				),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.46777044, 0.44531429, 0.40661017]		,	std=[0.12221994, 0.12145835, 0.14380469]		,	),
					    ]				)






