





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_a						=   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_a						=   "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"






class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	tempfile.mkdtemp()
				os.makedirs(os.path.join(self.diffusers_dir ,						'''schedulers/'''							)							)
				lowerCamelCase__              =	self.diffusers_dir
				shutil.copy(
				    os.path.join(__lowerCAmelCase ,						'''src/diffusers/schedulers/scheduling_ddpm.py'''							) ,						os.path.join(self.diffusers_dir ,						'''schedulers/scheduling_ddpm.py'''							) ,						)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	'''src/diffusers'''
				shutil.rmtree(self.diffusers_dir							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase=None							):



				'''simple docstring'''







				lowerCamelCase__              =	comment + F'\nclass {class_name}(nn.Module):\n' + class_code
				if overwrite_result is not None:
					lowerCamelCase__              =	comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
				lowerCamelCase__              =	black.Mode(target_versions={black.TargetVersion.PYaa} ,						line_length=1_1_9							)
				lowerCamelCase__              =	black.format_str(__lowerCAmelCase ,						mode=__lowerCAmelCase							)
				lowerCamelCase__              =	os.path.join(self.diffusers_dir ,						'''new_code.py'''							)
				with open(__lowerCAmelCase ,						'''w''' ,						newline='''\n'''							) as f:
					f.write(__lowerCAmelCase							)
				if overwrite_result is None:
					self.assertTrue(len(check_copies.is_copy_consistent(__lowerCAmelCase							)							) == 0							)
				else:
					check_copies.is_copy_consistent(f.name ,						overwrite=__lowerCAmelCase							)
					with open(__lowerCAmelCase ,						'''r'''							) as f:
						self.assertTrue(f.read() ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''							)
				self.assertEqual(__lowerCAmelCase ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				self.check_copy_consistency(
				    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,						'''DDPMSchedulerOutput''' ,						REFERENCE_CODE + '''\n''' ,						)

				# With no empty line at the end
				self.check_copy_consistency(
				    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,						'''DDPMSchedulerOutput''' ,						__lowerCAmelCase ,						)

				# Copy consistency with rename
				self.check_copy_consistency(
				    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,						'''TestSchedulerOutput''' ,						re.sub('''DDPM''' ,						'''Test''' ,						__lowerCAmelCase							) ,						)

				# Copy consistency with a really long name
				lowerCamelCase__              =	'''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
				self.check_copy_consistency(
				    F'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}' ,						F'{long_class_name}SchedulerOutput' ,						re.sub('''Bert''' ,						__lowerCAmelCase ,						__lowerCAmelCase							) ,						)

				# Copy consistency with overwrite
				self.check_copy_consistency(
				    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,						'''TestSchedulerOutput''' ,						__lowerCAmelCase ,						overwrite_result=re.sub('''DDPM''' ,						'''Test''' ,						__lowerCAmelCase							) ,						)










from __future__ import annotations

import unittest

from transformers import 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 numpy
			import tensorflow as tf

			from transformers import (
			    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
			    BertConfig,
			    DPRConfig,
			    TFDPRContextEncoder,
			    TFDPRQuestionEncoder,
			    TFDPRReader,
			)






class       __A					:

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase=1_3 ,						__lowerCAmelCase=7 ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=9_9 ,						__lowerCAmelCase=3_2 ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=4 ,						__lowerCAmelCase=3_7 ,						__lowerCAmelCase="gelu" ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=0.1 ,						__lowerCAmelCase=5_1_2 ,						__lowerCAmelCase=1_6 ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=0.02 ,						__lowerCAmelCase=3 ,						__lowerCAmelCase=4 ,						__lowerCAmelCase=None ,						__lowerCAmelCase=0 ,						):



				'''simple docstring'''







				lowerCamelCase__              =	parent
				lowerCamelCase__              =	batch_size
				lowerCamelCase__              =	seq_length
				lowerCamelCase__              =	is_training
				lowerCamelCase__              =	use_input_mask
				lowerCamelCase__              =	use_token_type_ids
				lowerCamelCase__              =	use_labels
				lowerCamelCase__              =	vocab_size
				lowerCamelCase__              =	hidden_size
				lowerCamelCase__              =	num_hidden_layers
				lowerCamelCase__              =	num_attention_heads
				lowerCamelCase__              =	intermediate_size
				lowerCamelCase__              =	hidden_act
				lowerCamelCase__              =	hidden_dropout_prob
				lowerCamelCase__              =	attention_probs_dropout_prob
				lowerCamelCase__              =	max_position_embeddings
				lowerCamelCase__              =	type_vocab_size
				lowerCamelCase__              =	type_sequence_label_size
				lowerCamelCase__              =	initializer_range
				lowerCamelCase__              =	num_labels
				lowerCamelCase__              =	num_choices
				lowerCamelCase__              =	scope
				lowerCamelCase__              =	projection_dim





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size							)

				lowerCamelCase__              =	None
				if self.use_input_mask:
					# follow test_modeling_tf_ctrl.py
					lowerCamelCase__              =	random_attention_mask([self.batch_size, self.seq_length]							)

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

				lowerCamelCase__              =	None
				lowerCamelCase__              =	None
				lowerCamelCase__              =	None
				if self.use_labels:
					lowerCamelCase__              =	ids_tensor([self.batch_size] ,						self.type_sequence_label_size							)
					lowerCamelCase__              =	ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels							)
					lowerCamelCase__              =	ids_tensor([self.batch_size] ,						self.num_choices							)

				lowerCamelCase__              =	BertConfig(
				    vocab_size=self.vocab_size ,						hidden_size=self.hidden_size ,						num_hidden_layers=self.num_hidden_layers ,						num_attention_heads=self.num_attention_heads ,						intermediate_size=self.intermediate_size ,						hidden_act=self.hidden_act ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						max_position_embeddings=self.max_position_embeddings ,						type_vocab_size=self.type_vocab_size ,						is_decoder=__lowerCAmelCase ,						initializer_range=self.initializer_range ,						)
				lowerCamelCase__              =	DPRConfig(projection_dim=self.projection_dim ,						**config.to_dict()							)

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	TFDPRContextEncoder(config=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						attention_mask=__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase							)
				self.parent.assertEqual(result.pooler_output.shape ,						(self.batch_size, self.projection_dim or self.hidden_size)							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	TFDPRQuestionEncoder(config=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						attention_mask=__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase							)
				self.parent.assertEqual(result.pooler_output.shape ,						(self.batch_size, self.projection_dim or self.hidden_size)							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	TFDPRReader(config=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						attention_mask=__lowerCAmelCase							)

				self.parent.assertEqual(result.start_logits.shape ,						(self.batch_size, self.seq_length)							)
				self.parent.assertEqual(result.end_logits.shape ,						(self.batch_size, self.seq_length)							)
				self.parent.assertEqual(result.relevance_logits.shape ,						(self.batch_size,)							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.prepare_config_and_inputs()
				(
				    (
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			
				)              =	config_and_inputs
				lowerCamelCase__              =	{'''input_ids''': input_ids}
				return config, inputs_dict






@require_tf
class       __A					(					lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		(
			    (
			        TFDPRContextEncoder,
			        TFDPRQuestionEncoder,
			        TFDPRReader,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCAmelCase_							  =		{"""feature-extraction""": TFDPRQuestionEncoder} if is_tf_available() else {}

			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	TFDPRModelTester(self							)
				lowerCamelCase__              =	ConfigTester(self ,						config_class=__lowerCAmelCase ,						hidden_size=3_7							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_dpr_context_encoder(*__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_dpr_question_encoder(*__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_dpr_reader(*__lowerCAmelCase							)





			@slow
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase__              =	TFDPRContextEncoder.from_pretrained(__lowerCAmelCase							)
					self.assertIsNotNone(__lowerCAmelCase							)

				for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase__              =	TFDPRContextEncoder.from_pretrained(__lowerCAmelCase							)
					self.assertIsNotNone(__lowerCAmelCase							)

				for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase__              =	TFDPRQuestionEncoder.from_pretrained(__lowerCAmelCase							)
					self.assertIsNotNone(__lowerCAmelCase							)

				for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase__              =	TFDPRReader.from_pretrained(__lowerCAmelCase							)
					self.assertIsNotNone(__lowerCAmelCase							)






@require_tf
class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			@slow
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''							)

				lowerCamelCase__              =	tf.constant(
				    [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]							)  # [CLS] hello, is my dog cute? [SEP]
				lowerCamelCase__              =	model(__lowerCAmelCase							)[0]  # embedding shape = (1, 768)
				# compare the actual values for a slice.
				lowerCamelCase__              =	tf.constant(
				    [
				        [
				            0.0323_6253,
				            0.1275_3335,
				            0.1681_8509,
				            0.0027_9786,
				            0.389_6933,
				            0.2426_4945,
				            0.217_8971,
				            -0.0233_5227,
				            -0.0848_1959,
				            -0.1432_4117,
				        ]
				    ]							)
				self.assertTrue(numpy.allclose(output[:, :1_0].numpy() ,						expected_slice.numpy() ,						atol=1E-4							)							)



