
"""simple docstring"""







from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
							from transformers import (
							    TFBertModel,
							    TFCLIPVisionModel,
							    TFDeiTModel,
							    TFRobertaModel,
							    TFVisionTextDualEncoderModel,
							    TFViTModel,
							    VisionTextDualEncoderConfig,
							)

if is_vision_available():
							from PIL import Image

							from transformers import VisionTextDualEncoderProcessor
def    __lowerCAmelCase    (_UpperCamelCase		):
	if isinstance(_UpperCamelCase	,  collections.abc.Iterable		):
		return x
	return (x, x)




@require_tf
class 			A__	:

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			pass

		def 							__lowerCamelCase							(				self			):
			pass

		def 							__lowerCamelCase							(				self			):
			pass

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[str]						      =				VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[str]						      =				TFVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				model(input_ids=_SCREAMING_SNAKE_CASE	,		pixel_values=_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE			)

			self.assertEqual(output['text_embeds'].shape	,		(input_ids.shape[0], config.projection_dim)			)
			self.assertEqual(output['image_embeds'].shape	,		(pixel_values.shape[0], config.projection_dim)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      ,       __lowerCAmelCase      :						Any						      =				self.get_vision_text_model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE	,		text_model=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Optional[Any]						      =				model(input_ids=_SCREAMING_SNAKE_CASE	,		pixel_values=_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE			)

			self.assertEqual(output['text_embeds'].shape	,		(input_ids.shape[0], model.config.projection_dim)			)
			self.assertEqual(output['image_embeds'].shape	,		(pixel_values.shape[0], model.config.projection_dim)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      ,       __lowerCAmelCase      :						str						      =				self.get_vision_text_model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						str						      =				{'vision_model': vision_model, 'text_model': text_model}
			__lowerCAmelCase      :						Dict						      =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[str]						      =				model(input_ids=_SCREAMING_SNAKE_CASE	,		pixel_values=_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE			)

			self.assertEqual(output['text_embeds'].shape	,		(input_ids.shape[0], model.config.projection_dim)			)
			self.assertEqual(output['image_embeds'].shape	,		(pixel_values.shape[0], model.config.projection_dim)			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      ,       __lowerCAmelCase      :						int						      =				self.get_vision_text_model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[Any]						      =				TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE	,		text_model=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						str						      =				model(input_ids=_SCREAMING_SNAKE_CASE	,		pixel_values=_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				output[0].numpy()

			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_pretrained(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						Tuple						      =				TFVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						Union[str, Any]						      =				model(input_ids=_SCREAMING_SNAKE_CASE	,		pixel_values=_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						Optional[int]						      =				after_output[0].numpy()
				__lowerCAmelCase      :						List[str]						      =				np.amax(np.abs(out_a - out_a			)			)
				self.assertLessEqual(_SCREAMING_SNAKE_CASE	,		1E-5			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[int]						      =				self.get_vision_text_model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE	,		text_model=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						int						      =				model(
			    input_ids=_SCREAMING_SNAKE_CASE	,		pixel_values=_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE	,		output_attentions=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				output.vision_model_output.attentions
			self.assertEqual(len(_SCREAMING_SNAKE_CASE			)	,		vision_config.num_hidden_layers			)

			# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
			__lowerCAmelCase      :						Optional[int]						      =				to_atuple(vision_model.config.image_size			)
			__lowerCAmelCase      :						Optional[Any]						      =				to_atuple(vision_model.config.patch_size			)
			__lowerCAmelCase      :						Optional[int]						      =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
			__lowerCAmelCase      :						str						      =				num_patches + 1
			self.assertEqual(vision_attentions[0].shape[-3:]	,		(vision_config.num_attention_heads, seq_len, seq_len)			)

			__lowerCAmelCase      :						Union[str, Any]						      =				output.text_model_output.attentions
			self.assertEqual(len(_SCREAMING_SNAKE_CASE			)	,		text_config.num_hidden_layers			)

			self.assertEqual(
			    text_attentions[0].shape[-3:]	,		(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])	,		)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Optional[Any]						      =				np.abs((a - b)			).max()
			self.assertLessEqual(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		f"Difference between torch and flax is {diff} (>= {tol})."			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				self.prepare_config_and_inputs()
			self.check_vision_text_dual_encoder_model(**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				self.prepare_config_and_inputs()
			self.check_model_from_pretrained_configs(**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.prepare_config_and_inputs()
			self.check_vision_text_dual_encoder_from_pretrained(**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[Any]						      =				self.prepare_config_and_inputs()
			self.check_save_load(**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				self.prepare_config_and_inputs()
			self.check_vision_text_output_attention(**_SCREAMING_SNAKE_CASE			)







		@slow
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      ,       __lowerCAmelCase      :						Any						      =				self.get_pretrained_model_and_inputs()

			__lowerCAmelCase      :						int						      =				model_a(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				outputs[0].numpy()

			with tempfile.TemporaryDirectory() as tmp_dirname:
				model_a.save_pretrained(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						str						      =				TFVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE			)

				__lowerCAmelCase      :						str						      =				model_a(**_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						int						      =				after_outputs[0].numpy()
				__lowerCAmelCase      :						Union[str, Any]						      =				np.amax(np.abs(out_a - out_a			)			)
				self.assertLessEqual(_SCREAMING_SNAKE_CASE	,		1E-5			)




@require_tf
class 			A__	(     _lowerCamelCase	,					unittest.TestCase):

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(
			    'hf-internal-testing/tiny-random-vit'	,		'hf-internal-testing/tiny-random-bert'			)
			__lowerCAmelCase      :						Optional[int]						      =				13
			__lowerCAmelCase      :						Any						      =				floats_tensor(
			    [
			        batch_size,
			        model.vision_model.config.num_channels,
			        model.vision_model.config.image_size,
			        model.vision_model.config.image_size,
			    ]			)
			__lowerCAmelCase      :						List[str]						      =				ids_tensor([batch_size, 4]	,		model.text_model.config.vocab_size			)
			__lowerCAmelCase      :						str						      =				random_attention_mask([batch_size, 4]			)
			__lowerCAmelCase      :						Optional[Any]						      =				{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

			return model, inputs

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						int						      =				TFViTModel(_SCREAMING_SNAKE_CASE	,		name='vision_model'			)
			__lowerCAmelCase      :						Optional[Any]						      =				TFBertModel(_SCREAMING_SNAKE_CASE	,		name='text_model'			)
			return vision_model, text_model







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				TFViTModelTester(self			)
			__lowerCAmelCase      :						List[str]						      =				TFBertModelTester(self			)
			__lowerCAmelCase      :						Dict						      =				vit_model_tester.prepare_config_and_inputs()
			__lowerCAmelCase      :						Union[str, Any]						      =				bert_model_tester.prepare_config_and_inputs()

			__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						List[str]						      =				vision_config_and_inputs

			(
			    (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       
			)      :						int						      =				text_config_and_inputs

			return {
			    "text_config": text_config,
			    "vision_config": vision_config,
			    "pixel_values": pixel_values,
			    "attention_mask": input_mask,
			    "input_ids": input_ids,
			    "text_token_type_ids": token_type_ids,
			    "text_sequence_labels": sequence_labels,
			    "text_token_labels": token_labels,
			    "text_choice_labels": choice_labels,
			}




@require_tf
class 			A__	(     _lowerCamelCase	,					unittest.TestCase):

		def 							__lowerCamelCase							(				self			):
			# DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
			# just reinitialize it.
			__lowerCAmelCase      :						Optional[int]						      =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(
			    'Rocketknight1/tiny-random-deit-tf'	,		'hf-internal-testing/tiny-random-roberta'			)
			__lowerCAmelCase      :						Dict						      =				13
			__lowerCAmelCase      :						str						      =				floats_tensor(
			    [
			        batch_size,
			        model.vision_model.config.num_channels,
			        model.vision_model.config.image_size,
			        model.vision_model.config.image_size,
			    ]			)
			__lowerCAmelCase      :						Union[str, Any]						      =				ids_tensor([batch_size, 4]	,		model.text_model.config.vocab_size			)
			__lowerCAmelCase      :						Any						      =				random_attention_mask([batch_size, 4]			)
			__lowerCAmelCase      :						int						      =				{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

			return model, inputs

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None	,		**_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      ,       __lowerCAmelCase      :						int						      =				self.get_vision_text_model(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Tuple						      =				TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE	,		text_model=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				model(
			    input_ids=_SCREAMING_SNAKE_CASE	,		pixel_values=_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE	,		output_attentions=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Union[str, Any]						      =				output.vision_model_output.attentions
			self.assertEqual(len(_SCREAMING_SNAKE_CASE			)	,		vision_config.num_hidden_layers			)

			# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
			__lowerCAmelCase      :						Optional[Any]						      =				to_atuple(vision_model.config.image_size			)
			__lowerCAmelCase      :						Optional[int]						      =				to_atuple(vision_model.config.patch_size			)
			__lowerCAmelCase      :						List[Any]						      =				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
			__lowerCAmelCase      :						str						      =				num_patches + 2
			self.assertEqual(vision_attentions[0].shape[-3:]	,		(vision_config.num_attention_heads, seq_len, seq_len)			)

			__lowerCAmelCase      :						int						      =				output.text_model_output.attentions
			self.assertEqual(len(_SCREAMING_SNAKE_CASE			)	,		text_config.num_hidden_layers			)

			self.assertEqual(
			    text_attentions[0].shape[-3:]	,		(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])	,		)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Dict						      =				TFDeiTModel(_SCREAMING_SNAKE_CASE	,		name='vision_model'			)
			__lowerCAmelCase      :						Any						      =				TFRobertaModel(_SCREAMING_SNAKE_CASE	,		name='text_model'			)
			return vision_model, text_model







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				TFDeiTModelTester(self			)
			__lowerCAmelCase      :						Dict						      =				TFRobertaModelTester(self			)
			__lowerCAmelCase      :						Tuple						      =				vit_model_tester.prepare_config_and_inputs()
			__lowerCAmelCase      :						List[str]						      =				bert_model_tester.prepare_config_and_inputs()

			__lowerCAmelCase      ,       __lowerCAmelCase      ,       __lowerCAmelCase      :						str						      =				vision_config_and_inputs

			(
			    (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       (
			    __lowerCAmelCase
			)      ,       
			)      :						List[str]						      =				text_config_and_inputs

			return {
			    "text_config": text_config,
			    "vision_config": vision_config,
			    "pixel_values": pixel_values,
			    "attention_mask": input_mask,
			    "input_ids": input_ids,
			    "text_token_type_ids": token_type_ids,
			    "text_sequence_labels": sequence_labels,
			    "text_token_labels": token_labels,
			    "text_choice_labels": choice_labels,
			}




@require_tf
class 			A__	(     _lowerCamelCase	,					unittest.TestCase):

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				TFVisionTextDualEncoderModel.from_vision_text_pretrained(
			    'Rocketknight1/tiny-random-clip-tf'	,		'hf-internal-testing/tiny-random-bert'			)
			__lowerCAmelCase      :						Dict						      =				13
			__lowerCAmelCase      :						Dict						      =				floats_tensor(
			    [
			        batch_size,
			        model.vision_model.config.num_channels,
			        model.vision_model.config.image_size,
			        model.vision_model.config.image_size,
			    ]			)
			__lowerCAmelCase      :						Optional[int]						      =				ids_tensor([batch_size, 4]	,		model.text_model.config.vocab_size			)
			__lowerCAmelCase      :						Optional[int]						      =				random_attention_mask([batch_size, 4]			)
			__lowerCAmelCase      :						str						      =				{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

			return model, inputs

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Optional[int]						      =				TFCLIPVisionModel(_SCREAMING_SNAKE_CASE	,		name='vision_model'			)
			__lowerCAmelCase      :						Any						      =				TFBertModel(_SCREAMING_SNAKE_CASE	,		name='text_model'			)
			return vision_model, text_model







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Any						      =				TFCLIPVisionModelTester(self			)
			__lowerCAmelCase      :						List[Any]						      =				TFBertModelTester(self			)
			__lowerCAmelCase      :						Tuple						      =				clip_model_tester.prepare_config_and_inputs()
			__lowerCAmelCase      :						int						      =				bert_model_tester.prepare_config_and_inputs()

			__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[Any]						      =				vision_config_and_inputs

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

			return {
			    "text_config": text_config,
			    "vision_config": vision_config,
			    "pixel_values": pixel_values,
			    "attention_mask": input_mask,
			    "input_ids": input_ids,
			    "text_token_type_ids": token_type_ids,
			    "text_sequence_labels": sequence_labels,
			    "text_token_labels": token_labels,
			    "text_choice_labels": choice_labels,
			}




@require_vision
@require_tf
class 			A__	(     unittest.TestCase):

		@slow
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				TFVisionTextDualEncoderModel.from_pretrained(
			    'clip-italian/clip-italian'	,		logit_scale_init_value=1.0	,		from_pt=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'			)

			__lowerCAmelCase      :						int						      =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
			__lowerCAmelCase      :						List[str]						      =				processor(
			    text=['una foto di un gatto', 'una foto di un cane']	,		images=_SCREAMING_SNAKE_CASE	,		padding=_SCREAMING_SNAKE_CASE	,		return_tensors='np'			)

			__lowerCAmelCase      :						Tuple						      =				model(**_SCREAMING_SNAKE_CASE			)

			# verify the logits
			self.assertEqual(outputs.logits_per_image.shape	,		(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])			)
			self.assertEqual(
			    outputs.logits_per_text.shape	,		(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])	,		)

			__lowerCAmelCase      :						Union[str, Any]						      =				np.array([[1.228_4727, 0.310_4122]]			)

			self.assertTrue(np.allclose(outputs.logits_per_image.numpy()	,		_SCREAMING_SNAKE_CASE	,		atol=1E-3			)			)

"""simple docstring"""







import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase__        =		getLogger(__name__)


lowerCamelCase__        =		"""cuda""" if torch.cuda.is_available() else """cpu"""
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = 8	,  _UpperCamelCase = DEFAULT_DEVICE	,  _UpperCamelCase=False	,  _UpperCamelCase="summarization"	,  _UpperCamelCase=None	,  **_UpperCamelCase	,  ):
	__lowerCAmelCase      :						str						      =				Path(_UpperCamelCase		).open('w'	,  encoding='utf-8'		)
	__lowerCAmelCase      :						Union[str, Any]						      =				str(_UpperCamelCase		)
	__lowerCAmelCase      :						List[str]						      =				AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase		).to(_UpperCamelCase		)
	if fpaa:
		__lowerCAmelCase      :						Optional[Any]						      =				model.half()

	__lowerCAmelCase      :						List[str]						      =				AutoTokenizer.from_pretrained(_UpperCamelCase		)
	logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"		)  # if this is wrong, check config.model_type.

	__lowerCAmelCase      :						List[Any]						      =				time.time()
	# update config with task specific params
	use_task_specific_params(_UpperCamelCase	,  _UpperCamelCase		)
	if prefix is None:
		__lowerCAmelCase      :						Optional[int]						      =				prefix or getattr(model.config	,  'prefix'	,  ''		) or ''
	for examples_chunk in tqdm(list(chunks(_UpperCamelCase	,  _UpperCamelCase		)		)		):
		__lowerCAmelCase      :						List[str]						      =				[prefix + text for text in examples_chunk]
		__lowerCAmelCase      :						List[str]						      =				tokenizer(_UpperCamelCase	,  return_tensors='pt'	,  truncation=_UpperCamelCase	,  padding='longest'		).to(_UpperCamelCase		)
		__lowerCAmelCase      :						str						      =				model.generate(
		    input_ids=batch.input_ids	,  attention_mask=batch.attention_mask	,  **_UpperCamelCase	,  )
		__lowerCAmelCase      :						str						      =				tokenizer.batch_decode(_UpperCamelCase	,  skip_special_tokens=_UpperCamelCase	,  clean_up_tokenization_spaces=_UpperCamelCase		)
		for hypothesis in dec:
			fout.write(hypothesis + '\n'		)
			fout.flush()
	fout.close()
	__lowerCAmelCase      :						Optional[int]						      =				int(time.time() - start_time		)  # seconds
	__lowerCAmelCase      :						List[Any]						      =				len(_UpperCamelCase		)
	return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs	,  4		)}
def    __lowerCAmelCase    ():
	return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'		)
def    __lowerCAmelCase    (_UpperCamelCase=True		):
	__lowerCAmelCase      :						List[Any]						      =				argparse.ArgumentParser()
	parser.add_argument('model_name'	,  type=_UpperCamelCase	,  help='like facebook/bart-large-cnn,t5-base, etc.'		)
	parser.add_argument('input_path'	,  type=_UpperCamelCase	,  help='like cnn_dm/test.source'		)
	parser.add_argument('save_path'	,  type=_UpperCamelCase	,  help='where to save summaries'		)
	parser.add_argument('--reference_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  help='like cnn_dm/test.target'		)
	parser.add_argument('--score_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default='metrics.json'	,  help='where to save metrics'		)
	parser.add_argument('--device'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='cuda, cuda:1, cpu etc.'		)
	parser.add_argument(
	    '--prefix'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='will be added to the begininng of src examples'		)
	parser.add_argument('--task'	,  type=_UpperCamelCase	,  default='summarization'	,  help='used for task_specific_params + metrics'		)
	parser.add_argument('--bs'	,  type=_UpperCamelCase	,  default=8	,  required=_UpperCamelCase	,  help='batch size'		)
	parser.add_argument(
	    '--n_obs'	,  type=_UpperCamelCase	,  default=-1	,  required=_UpperCamelCase	,  help='How many observations. Defaults to all.'		)
	parser.add_argument('--fp16'	,  action='store_true'		)
	parser.add_argument('--dump-args'	,  action='store_true'	,  help='print the custom hparams with the results'		)
	parser.add_argument(
	    '--info'	,  nargs='?'	,  type=_UpperCamelCase	,  const=datetime_now()	,  help=(
	        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
	        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
	    )	,  )
	# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[int]						      =				parser.parse_known_args()
	__lowerCAmelCase      :						Optional[int]						      =				parse_numeric_n_bool_cl_kwargs(_UpperCamelCase		)
	if parsed_args and verbose:
		print(F"parsed the following generate kwargs: {parsed_args}"		)
	__lowerCAmelCase      :						Dict						      =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path		).readlines()]
	if args.n_obs > 0:
		__lowerCAmelCase      :						int						      =				examples[: args.n_obs]
	Path(args.save_path		).parent.mkdir(exist_ok=_UpperCamelCase		)

	if args.reference_path is None and Path(args.score_path		).exists():
		warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c."		)

	if args.device == "cpu" and args.fpaa:
		# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
		raise ValueError('Can\'t mix --fp16 and --device cpu'		)

	__lowerCAmelCase      :						Optional[Any]						      =				generate_summaries_or_translations(
	    _UpperCamelCase	,  args.save_path	,  args.model_name	,  batch_size=args.bs	,  device=args.device	,  fpaa=args.fpaa	,  task=args.task	,  prefix=args.prefix	,  **_UpperCamelCase	,  )

	if args.reference_path is None:
		return {}

	# Compute scores
	__lowerCAmelCase      :						str						      =				calculate_bleu if 'translation' in args.task else calculate_rouge
	__lowerCAmelCase      :						Dict						      =				[x.rstrip() for x in open(args.save_path		).readlines()]
	__lowerCAmelCase      :						Dict						      =				[x.rstrip() for x in open(args.reference_path		).readlines()][: len(_UpperCamelCase		)]
	__lowerCAmelCase      :						dict						      =				score_fn(_UpperCamelCase	,  _UpperCamelCase		)
	scores.update(_UpperCamelCase		)

	if args.dump_args:
		scores.update(_UpperCamelCase		)
	if args.info:
		__lowerCAmelCase      :						Optional[Any]						      =				args.info

	if verbose:
		print(_UpperCamelCase		)

	if args.score_path is not None:
		json.dump(_UpperCamelCase	,  open(args.score_path	,  'w'		)		)

	return scores


if __name__ == "__main__":
							# Usage for MT:
							# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
							run_generate(verbose=True)