





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
					import tensorflow as tf

					from transformers.models.xglm.modeling_tf_xglm import (
					    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFXGLMForCausalLM,
					    TFXGLMModel,
					)
@require_tf
class 			UpperCamelCase_		:
					lowerCAmelCase_							=	XGLMConfig
					lowerCAmelCase_							=	{}
					lowerCAmelCase_							=	'''gelu'''




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=14				,						lowerCAmelCase_=7				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						lowerCAmelCase_=99				,						lowerCAmelCase_=32				,						lowerCAmelCase_=2				,						lowerCAmelCase_=4				,						lowerCAmelCase_=37				,						lowerCAmelCase_="gelu"				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=512				,						lowerCAmelCase_=0.02				,						)		->							Optional[Any]:
										_snake_case									=				parent
										_snake_case									=				batch_size
										_snake_case									=				seq_length
										_snake_case									=				is_training
										_snake_case									=				use_input_mask
										_snake_case									=				use_labels
										_snake_case									=				vocab_size
										_snake_case									=				d_model
										_snake_case									=				num_hidden_layers
										_snake_case									=				num_attention_heads
										_snake_case									=				ffn_dim
										_snake_case									=				activation_function
										_snake_case									=				activation_dropout
										_snake_case									=				attention_dropout
										_snake_case									=				max_position_embeddings
										_snake_case									=				initializer_range
										_snake_case									=				None
										_snake_case									=				0
										_snake_case									=				2
										_snake_case									=				1




					def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										return XGLMConfig.from_pretrained('facebook/xglm-564M'  )




					def 	lowerCAmelCase			(		self  )		->							Any:
										_snake_case									=				tf.clip_by_value(
										    ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size  )				,						clip_value_min=0				,						clip_value_max=3  )

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

										_snake_case									=				self.get_config()

										_snake_case									=				floats_tensor([self.num_hidden_layers, self.num_attention_heads]				,						2  )

										return (
										    config,
										    input_ids,
										    input_mask,
										    head_mask,
										)




					def 	lowerCAmelCase			(		self  )		->							str:
										return XGLMConfig(
										    vocab_size=self.vocab_size				,						d_model=self.hidden_size				,						num_layers=self.num_hidden_layers				,						attention_heads=self.num_attention_heads				,						ffn_dim=self.ffn_dim				,						activation_function=self.activation_function				,						activation_dropout=self.activation_dropout				,						attention_dropout=self.attention_dropout				,						max_position_embeddings=self.max_position_embeddings				,						initializer_range=self.initializer_range				,						use_cache=lowerCAmelCase_				,						bos_token_id=self.bos_token_id				,						eos_token_id=self.eos_token_id				,						pad_token_id=self.pad_token_id				,						return_dict=lowerCAmelCase_				,						)






					def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										_snake_case									=				self.prepare_config_and_inputs()

										(
										    (
										    _snake_case     
										)							,     (
										    _snake_case     
										)							,     (
										    _snake_case     
										)							,     (
										    _snake_case     
										)							,          
										)									=				config_and_inputs

										_snake_case									=				{
										    'input_ids': input_ids,
										    'head_mask': head_mask,
										}

										return config, inputs_dict





@require_tf
class 			UpperCamelCase_		(  _lowerCamelCase							,  _lowerCamelCase							,  unittest.TestCase					):
					lowerCAmelCase_							=	(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
					lowerCAmelCase_							=	(TFXGLMForCausalLM,) if is_tf_available() else ()
					lowerCAmelCase_							=	(
					    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
					)
					lowerCAmelCase_							=	False
					lowerCAmelCase_							=	False
					lowerCAmelCase_							=	False




					def 	lowerCAmelCase			(		self  )		->							Dict:
										_snake_case									=				TFXGLMModelTester(self  )
										_snake_case									=				ConfigTester(self				,						config_class=lowerCAmelCase_				,						n_embd=37  )




					def 	lowerCAmelCase			(		self  )		->							Any:
										self.config_tester.run_common_tests()




					@slow
					def 	lowerCAmelCase			(		self  )		->							Tuple:
										for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_snake_case									=				TFXGLMModel.from_pretrained(lowerCAmelCase_  )
															self.assertIsNotNone(lowerCAmelCase_  )






					@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'  )
					def 	lowerCAmelCase			(		self  )		->							str:
										super().test_resize_token_embeddings()





@require_tf
class 			UpperCamelCase_		(  unittest.TestCase					):




					@slow
					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_=True  )		->							Tuple:
										_snake_case									=				TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'  )
										_snake_case									=				tf.convert_to_tensor([[2, 268, 9865]]				,						dtype=tf.intaa  )  # The dog
										# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
										# fmt: off
										_snake_case									=				[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
										# fmt: on
										_snake_case									=				model.generate(lowerCAmelCase_				,						do_sample=lowerCAmelCase_				,						num_beams=1  )
										if verify_outputs:
															self.assertListEqual(output_ids[0].numpy().tolist()				,						lowerCAmelCase_  )




					@slow
					def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										_snake_case									=				XGLMTokenizer.from_pretrained('facebook/xglm-564M'  )
										_snake_case									=				TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'  )

										tf.random.set_seed(0  )
										_snake_case									=				tokenizer('Today is a nice day and'				,						return_tensors='tf'  )
										_snake_case									=				tokenized.input_ids
										# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
										with tf.device(':/CPU:0'  ):
															_snake_case									=				model.generate(lowerCAmelCase_				,						do_sample=lowerCAmelCase_				,						seed=[7, 0]  )
										_snake_case									=				tokenizer.decode(output_ids[0]				,						skip_special_tokens=lowerCAmelCase_  )

										_snake_case									=				(
										    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
										)
										self.assertEqual(lowerCAmelCase_				,						lowerCAmelCase_  )






					@slow
					def 	lowerCAmelCase			(		self  )		->							int:
										_snake_case									=				TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'  )
										_snake_case									=				XGLMTokenizer.from_pretrained('facebook/xglm-564M'  )

										_snake_case									=				'left'

										# use different length sentences to test batching
										_snake_case									=				[
										    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
										    'left-padding, such as in batched generation. The output for the sequence below should be the same '
										    'regardless of whether left padding is applied or not. When',
										    'Hello, my dog is a little',
										]

										_snake_case									=				tokenizer(lowerCAmelCase_				,						return_tensors='tf'				,						padding=lowerCAmelCase_  )
										_snake_case									=				inputs['input_ids']

										_snake_case									=				model.generate(input_ids=lowerCAmelCase_				,						attention_mask=inputs['attention_mask']				,						max_new_tokens=12  )

										_snake_case									=				tokenizer(sentences[0]				,						return_tensors='tf'  ).input_ids
										_snake_case									=				model.generate(input_ids=lowerCAmelCase_				,						max_new_tokens=12  )

										_snake_case									=				tokenizer(sentences[1]				,						return_tensors='tf'  ).input_ids
										_snake_case									=				model.generate(input_ids=lowerCAmelCase_				,						max_new_tokens=12  )

										_snake_case									=				tokenizer.batch_decode(lowerCAmelCase_				,						skip_special_tokens=lowerCAmelCase_  )
										_snake_case									=				tokenizer.decode(output_non_padded[0]				,						skip_special_tokens=lowerCAmelCase_  )
										_snake_case									=				tokenizer.decode(output_padded[0]				,						skip_special_tokens=lowerCAmelCase_  )

										_snake_case									=				[
										    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
										    'left-padding, such as in batched generation. The output for the sequence below should be the same '
										    'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
										    'a single',
										    'Hello, my dog is a little bit of a shy one, but he is very friendly',
										]
										self.assertListEqual(lowerCAmelCase_				,						lowerCAmelCase_  )
										self.assertListEqual(lowerCAmelCase_				,						[non_padded_sentence, padded_sentence]  )










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


UpperCAmelCase_      							=     {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 			UpperCamelCase_		(  nn.Module					):




					def __init__(		self				,						lowerCAmelCase_  )		->							int:
										super().__init__()
										_snake_case									=				torchvision.models.resnetaaa(pretrained=lowerCAmelCase_  )
										_snake_case									=				list(model.children()  )[:-2]
										_snake_case									=				nn.Sequential(*lowerCAmelCase_  )
										_snake_case									=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							str:
										# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
										_snake_case									=				self.pool(self.model(lowerCAmelCase_  )  )
										_snake_case									=				torch.flatten(lowerCAmelCase_				,						start_dim=2  )
										_snake_case									=				out.transpose(1				,						2  ).contiguous()
										return out  # BxNx2048





class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[Any]:
										_snake_case									=				[json.loads(lowerCAmelCase_  ) for l in open(lowerCAmelCase_  )]
										_snake_case									=				os.path.dirname(lowerCAmelCase_  )
										_snake_case									=				tokenizer
										_snake_case									=				labels
										_snake_case									=				len(lowerCAmelCase_  )
										_snake_case									=				max_seq_length

										_snake_case									=				transforms




					def __len__(		self  )		->							Any:
										return len(self.data  )




					def __getitem__(		self				,						lowerCAmelCase_  )		->							Optional[int]:
										_snake_case									=				torch.LongTensor(self.tokenizer.encode(self.data[index]['text']				,						add_special_tokens=lowerCAmelCase_  )  )
										_snake_case							,     _snake_case							,     _snake_case									=				sentence[0], sentence[1:-1], sentence[-1]
										_snake_case									=				sentence[: self.max_seq_length]

										_snake_case									=				torch.zeros(self.n_classes  )
										_snake_case									=				1

										_snake_case									=				Image.open(os.path.join(self.data_dir				,						self.data[index]['img']  )  ).convert('RGB'  )
										_snake_case									=				self.transforms(lowerCAmelCase_  )

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






					def 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				Counter()
										for row in self.data:
															label_freqs.update(row['label']  )
										return label_freqs





def 						lowerCamelCase__     (     UpperCamelCase__			:       str						)			-> Dict:



					'''simple docstring'''




					_snake_case									=				[len(row['sentence']						) for row in batch]
					_snake_case							,     _snake_case									=				len(UpperCamelCase__						), max(UpperCamelCase__						)

					_snake_case									=				torch.zeros(UpperCamelCase__       ,    UpperCamelCase__       ,    dtype=torch.long						)
					_snake_case									=				torch.zeros(UpperCamelCase__       ,    UpperCamelCase__       ,    dtype=torch.long						)

					for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__       ,    UpperCamelCase__						)						):
										_snake_case									=				input_row['sentence']
										_snake_case									=				1

					_snake_case									=				torch.stack([row['image'] for row in batch]						)
					_snake_case									=				torch.stack([row['label'] for row in batch]						)
					_snake_case									=				torch.stack([row['image_start_token'] for row in batch]						)
					_snake_case									=				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 						lowerCamelCase__     (     )			-> str:



					'''simple docstring'''




					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 						lowerCamelCase__     (     )			-> Tuple:



					'''simple docstring'''




					return transforms.Compose(
					    [
					        transforms.Resize(256						),
					        transforms.CenterCrop(224						),
					        transforms.ToTensor(),
					        transforms.Normalize(
					            mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]       ,    std=[0.1222_1994, 0.1214_5835, 0.1438_0469]       ,    ),
					    ]						)



