






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 		_snake_case  :
			_lowercase		:    Tuple			     =							XGLMConfig
			_lowercase		:    Any			     =							{}
			_lowercase		:    int			     =							'''gelu'''






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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						return XGLMConfig.from_pretrained('facebook/xglm-564M')






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_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)

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

						SCREAMING_SNAKE_CASE						= self.get_config()

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

						return (
						    config,
						    input_ids,
						    input_mask,
						    head_mask,
						)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						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=a		,					bos_token_id=self.bos_token_id		,					eos_token_id=self.eos_token_id		,					pad_token_id=self.pad_token_id		,					return_dict=a		,					)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.prepare_config_and_inputs()

						(
						    (
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						
						)						= config_and_inputs

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

						return config, inputs_dict



@require_tf
class 		_snake_case  (  A__   ,    A__   ,    unittest.TestCase					):
			_lowercase		:    int			     =							(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			_lowercase		:    str			     =							(TFXGLMForCausalLM,) if is_tf_available() else ()
			_lowercase		:    Dict			     =							(
			    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
			)
			_lowercase		:    Union[str, Any]			     =							False
			_lowercase		:    int			     =							False
			_lowercase		:    Tuple			     =							False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE						= TFXGLMModelTester(self)
						SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=a		,					n_embd=37)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						self.config_tester.run_common_tests()






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE						= TFXGLMModel.from_pretrained(a)
									self.assertIsNotNone(a)







			@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						super().test_resize_token_embeddings()



@require_tf
class 		_snake_case  (  unittest.TestCase					):






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self		,					a=True)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M')
						SCREAMING_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
						SCREAMING_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
						SCREAMING_SNAKE_CASE						= model.generate(a		,					do_sample=a		,					num_beams=1)
						if verify_outputs:
									self.assertListEqual(output_ids[0].numpy().tolist()		,					a)






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

						tf.random.set_seed(0)
						SCREAMING_SNAKE_CASE						= tokenizer('Today is a nice day and'		,					return_tensors='tf')
						SCREAMING_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'):
									SCREAMING_SNAKE_CASE						= model.generate(a		,					do_sample=a		,					seed=[7, 0])
						SCREAMING_SNAKE_CASE						= tokenizer.decode(output_ids[0]		,					skip_special_tokens=a)

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







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M')
						SCREAMING_SNAKE_CASE						= XGLMTokenizer.from_pretrained('facebook/xglm-564M')

						SCREAMING_SNAKE_CASE						= 'left'

						# use different length sentences to test batching
						SCREAMING_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',
						]

						SCREAMING_SNAKE_CASE						= tokenizer(a		,					return_tensors='tf'		,					padding=a)
						SCREAMING_SNAKE_CASE						= inputs['input_ids']

						SCREAMING_SNAKE_CASE						= model.generate(input_ids=a		,					attention_mask=inputs['attention_mask']		,					max_new_tokens=12)

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

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

						SCREAMING_SNAKE_CASE						= tokenizer.batch_decode(a		,					skip_special_tokens=a)
						SCREAMING_SNAKE_CASE						= tokenizer.decode(output_non_padded[0]		,					skip_special_tokens=a)
						SCREAMING_SNAKE_CASE						= tokenizer.decode(output_padded[0]		,					skip_special_tokens=a)

						SCREAMING_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(a		,					a)
						self.assertListEqual(a		,					[non_padded_sentence, padded_sentence])












from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from ..image_utils import load_image


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

a_		:						str             =    logging.get_logger(__name__)


a_		:						List[Any]             =    Dict[str, Any]
a_		:						Optional[int]             =    List[Prediction]

@add_end_docstrings(A__					)
class 		_snake_case  (  A__					):






			def __init__(       self		,					*a		,					**a)      ->       int:
						super().__init__(*a		,					**a)

						if self.framework == "tf":
									raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')

						requires_backends(self		,					'vision')
						self.check_model_type(
						    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))






			def    SCREAMING_SNAKE_CASE__    (       self		,					**a)      ->       int:
						SCREAMING_SNAKE_CASE						= {}
						if "threshold" in kwargs:
									SCREAMING_SNAKE_CASE						= kwargs['threshold']
						return {}, {}, postprocess_kwargs






			def __call__(       self		,					*a		,					**a)      ->       Union[Predictions, List[Prediction]]:
						return super().__call__(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= load_image(a)
						SCREAMING_SNAKE_CASE						= torch.IntTensor([[image.height, image.width]])
						SCREAMING_SNAKE_CASE						= self.image_processor(images=[image]		,					return_tensors='pt')
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= self.tokenizer(text=inputs['words']		,					boxes=inputs['boxes']		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= target_size
						return inputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= model_inputs.pop('target_size')
						SCREAMING_SNAKE_CASE						= self.model(**a)
						SCREAMING_SNAKE_CASE						= outputs.__class__({'target_size': target_size, **outputs})
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= model_inputs['bbox']
						return model_outputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=0.9)      ->       Dict:
						SCREAMING_SNAKE_CASE						= model_outputs['target_size']
						if self.tokenizer is not None:
									# This is a LayoutLMForTokenClassification variant.
									# The OCR got the boxes and the model classified the words.
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= target_size[0].tolist()

									def unnormalize(a):
												return self._get_bounding_box(
												    torch.Tensor(
												        [
												            (width * bbox[0] / 1000),
												            (height * bbox[1] / 1000),
												            (width * bbox[2] / 1000),
												            (height * bbox[3] / 1000),
												        ]))

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
									SCREAMING_SNAKE_CASE						= [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
									SCREAMING_SNAKE_CASE						= [unnormalize(a) for bbox in model_outputs['bbox'].squeeze(0)]
									SCREAMING_SNAKE_CASE						= ['score', 'label', 'box']
									SCREAMING_SNAKE_CASE						= [dict(zip(a		,					a)) for vals in zip(scores.tolist()		,					a		,					a) if vals[0] > threshold]
						else:
									# This is a regular ForObjectDetectionModel
									SCREAMING_SNAKE_CASE						= self.image_processor.post_process_object_detection(a		,					a		,					a)
									SCREAMING_SNAKE_CASE						= raw_annotations[0]
									SCREAMING_SNAKE_CASE						= raw_annotation['scores']
									SCREAMING_SNAKE_CASE						= raw_annotation['labels']
									SCREAMING_SNAKE_CASE						= raw_annotation['boxes']

									SCREAMING_SNAKE_CASE						= scores.tolist()
									SCREAMING_SNAKE_CASE						= [self.model.config.idalabel[label.item()] for label in labels]
									SCREAMING_SNAKE_CASE						= [self._get_bounding_box(a) for box in boxes]

									# {"scores": [...], ...} --> [{"score":x, ...}, ...]
									SCREAMING_SNAKE_CASE						= ['score', 'label', 'box']
									SCREAMING_SNAKE_CASE						= [
									    dict(zip(a		,					a))
									    for vals in zip(raw_annotation['scores']		,					raw_annotation['labels']		,					raw_annotation['boxes'])
									]

						return annotation







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict[str, int]:
						if self.framework != "pt":
									raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= box.int().tolist()
						SCREAMING_SNAKE_CASE						= {
						    'xmin': xmin,
						    'ymin': ymin,
						    'xmax': xmax,
						    'ymax': ymax,
						}
						return bbox




