






"""simple docstring"""
import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import RegNetForImageClassification, RegNetModel
							from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor



class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=3  ,  lowerCAmelCase_=32  ,  lowerCAmelCase_=3  ,  lowerCAmelCase_=10  ,  lowerCAmelCase_=[10, 20, 30, 40]  ,  lowerCAmelCase_=[1, 1, 2, 1]  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_="relu"  ,  lowerCAmelCase_=3  ,  lowerCAmelCase_=None  ,  ):
		A_		:    Tuple						     = parent
		A_		:    str						     = batch_size
		A_		:    str						     = image_size
		A_		:    str						     = num_channels
		A_		:    List[Any]						     = embeddings_size
		A_		:    int						     = hidden_sizes
		A_		:    List[Any]						     = depths
		A_		:    Tuple						     = is_training
		A_		:    Any						     = use_labels
		A_		:    List[str]						     = hidden_act
		A_		:    int						     = num_labels
		A_		:    Union[str, Any]						     = scope
		A_		:    Any						     = len(lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    List[Any]						     = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

		A_		:    Dict						     = None
		if self.use_labels:
			A_		:    int						     = ids_tensor([self.batch_size]  ,  self.num_labels				)

		A_		:    Tuple						     = self.get_config()

		return config, pixel_values, labels





	def  lowerCamelCase(self				):
		return RegNetConfig(
		    num_channels=self.num_channels  ,  embeddings_size=self.embeddings_size  ,  hidden_sizes=self.hidden_sizes  ,  depths=self.depths  ,  hidden_act=self.hidden_act  ,  num_labels=self.num_labels  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Union[str, Any]						     = RegNetModel(config=lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    int						     = model(lowerCAmelCase_				)
		# expected last hidden states: B, C, H // 32, W // 32
		self.parent.assertEqual(
		    result.last_hidden_state.shape  ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)  ,  )





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Any						     = self.num_labels
		A_		:    Optional[Any]						     = RegNetForImageClassification(lowerCAmelCase_				)
		model.to(lowerCAmelCase_				)
		model.eval()
		A_		:    Any						     = model(lowerCAmelCase_  ,  labels=lowerCAmelCase_				)
		self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)				)





	def  lowerCamelCase(self				):
		A_		:    Tuple						     = self.prepare_config_and_inputs()
		A_     ,  A_     ,  A_		:    Union[str, Any]						     = config_and_inputs
		A_		:    Any						     = {"""pixel_values""": pixel_values}
		return config, inputs_dict






@require_torch
class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					str 			=					(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
	_A							:					List[Any] 			=					(
	    {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_A							:					Tuple 			=					False
	_A							:					Dict 			=					False
	_A							:					Any 			=					False
	_A							:					List[Any] 			=					False





	def  lowerCamelCase(self				):
		A_		:    Any						     = RegNetModelTester(self				)
		A_		:    List[str]						     = ConfigTester(self  ,  config_class=lowerCAmelCase_  ,  has_text_modality=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		self.create_and_test_config_common_properties()
		self.config_tester.create_and_test_config_to_json_string()
		self.config_tester.create_and_test_config_to_json_file()
		self.config_tester.create_and_test_config_from_and_save_pretrained()
		self.config_tester.create_and_test_config_with_num_labels()
		self.config_tester.check_config_can_be_init_without_params()
		self.config_tester.check_config_arguments_init()





	def  lowerCamelCase(self				):
		return





	@unittest.skip(reason="""RegNet does not use inputs_embeds"""				)
	def  lowerCamelCase(self				):
		pass





	@unittest.skip(reason="""RegNet does not support input and output embeddings"""				)
	def  lowerCamelCase(self				):
		pass





	def  lowerCamelCase(self				):
		A_     ,  A_		:    Tuple						     = self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			A_		:    Optional[int]						     = model_class(lowerCAmelCase_				)
			A_		:    Union[str, Any]						     = inspect.signature(model.forward				)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			A_		:    List[Any]						     = [*signature.parameters.keys()]

			A_		:    Any						     = ["""pixel_values"""]
			self.assertListEqual(arg_names[:1]  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    str						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_     ,  A_		:    str						     = self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			A_		:    Optional[int]						     = model_class(config=lowerCAmelCase_				)
			for name, module in model.named_modules():
				if isinstance(lowerCAmelCase_  ,  (nn.BatchNormad, nn.GroupNorm)				):
					self.assertTrue(
					    torch.all(module.weight == 1				)  ,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""  ,  )
					self.assertTrue(
					    torch.all(module.bias == 0				)  ,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""  ,  )





	def  lowerCamelCase(self				):
		def check_hidden_states_output(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
			A_		:    Optional[int]						     = model_class(lowerCAmelCase_				)
			model.to(lowerCAmelCase_				)
			model.eval()

			with torch.no_grad():
				A_		:    Any						     = model(**self._prepare_for_class(lowerCAmelCase_  ,  lowerCAmelCase_				)				)

			A_		:    List[Any]						     = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

			A_		:    Optional[int]						     = self.model_tester.num_stages
			self.assertEqual(len(lowerCAmelCase_				)  ,  expected_num_stages + 1				)

			# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
			self.assertListEqual(
			    list(hidden_states[0].shape[-2:]				)  ,  [self.model_tester.image_size // 2, self.model_tester.image_size // 2]  ,  )

		A_     ,  A_		:    Tuple						     = self.model_tester.prepare_config_and_inputs_for_common()
		A_		:    List[Any]						     = ["""basic""", """bottleneck"""]
		for model_class in self.all_model_classes:
			for layer_type in layers_type:
				A_		:    str						     = layer_type
				A_		:    str						     = True
				check_hidden_states_output(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)

				# check that output_hidden_states also work using config
				del inputs_dict["output_hidden_states"]
				A_		:    Optional[int]						     = True

				check_hidden_states_output(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    str						     = self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_				)





	@slow
	def  lowerCamelCase(self				):
		for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			A_		:    List[str]						     = RegNetModel.from_pretrained(lowerCAmelCase_				)
			self.assertIsNotNone(lowerCAmelCase_				)






def       __UpperCamelCase						(		):
	A_		:    Any						     = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image




@require_torch
@require_vision
class   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	@cached_property
	def  lowerCamelCase(self				):
		return (
		    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
		    if is_vision_available()
		    else None
		)





	@slow
	def  lowerCamelCase(self				):
		A_		:    int						     = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(lowerCAmelCase_				)

		A_		:    Dict						     = self.default_image_processor
		A_		:    Optional[Any]						     = prepare_img()
		A_		:    Tuple						     = image_processor(images=lowerCAmelCase_  ,  return_tensors="""pt"""				).to(lowerCAmelCase_				)

		# forward pass
		with torch.no_grad():
			A_		:    Tuple						     = model(**lowerCAmelCase_				)

		# verify the logits
		A_		:    Optional[Any]						     = torch.Size((1, 1000)				)
		self.assertEqual(outputs.logits.shape  ,  lowerCAmelCase_				)

		A_		:    Dict						     = torch.tensor([-0.4180, -1.5051, -3.4836]				).to(lowerCAmelCase_				)

		self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,  lowerCAmelCase_  ,  atol=1e-4				)				)









"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__=True , snake_case__="pt"       ):
	A_		:    Dict						     = {"""add_prefix_space""": True} if isinstance(snake_case__ , snake_case__       ) and not line.startswith(""" """       ) else {}
	A_		:    int						     = padding_side
	return tokenizer(
	    [line] , max_length=snake_case__ , padding="""max_length""" if pad_to_max_length else None , truncation=snake_case__ , return_tensors=snake_case__ , add_special_tokens=snake_case__ , **snake_case__ , )






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__=None , ):
	A_		:    int						     = input_ids.ne(snake_case__       ).any(dim=0       )
	if attention_mask is None:
		return input_ids[:, keep_column_mask]
	else:
		return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_="train"  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=""  ,  ):
		super().__init__()
		A_		:    str						     = Path(lowerCAmelCase_				).joinpath(type_path + """.source"""				)
		A_		:    Tuple						     = Path(lowerCAmelCase_				).joinpath(type_path + """.target"""				)
		A_		:    Optional[Any]						     = self.get_char_lens(self.src_file				)
		A_		:    Optional[Any]						     = max_source_length
		A_		:    Tuple						     = max_target_length
		assert min(self.src_lens				) > 0, f"""found empty line in {self.src_file}"""
		A_		:    Tuple						     = tokenizer
		A_		:    Optional[int]						     = prefix
		if n_obs is not None:
			A_		:    Union[str, Any]						     = self.src_lens[:n_obs]
		A_		:    Optional[int]						     = src_lang
		A_		:    Union[str, Any]						     = tgt_lang





	def __len__(self				):
		return len(self.src_lens				)





	def __getitem__(self  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = index + 1  # linecache starts at 1
		A_		:    int						     = self.prefix + linecache.getline(str(self.src_file				)  ,  lowerCAmelCase_				).rstrip("""\n"""				)
		A_		:    Any						     = linecache.getline(str(self.tgt_file				)  ,  lowerCAmelCase_				).rstrip("""\n"""				)
		assert source_line, f"""empty source line for index {index}"""
		assert tgt_line, f"""empty tgt line for index {index}"""

		# Need to add eos token manually for T5
		if isinstance(self.tokenizer  ,  lowerCAmelCase_				):
			source_line += self.tokenizer.eos_token
			tgt_line += self.tokenizer.eos_token

		# Pad source and target to the right
		A_		:    Optional[Any]						     = (
		    self.tokenizer.question_encoder if isinstance(self.tokenizer  ,  lowerCAmelCase_				) else self.tokenizer
		)
		A_		:    Optional[int]						     = self.tokenizer.generator if isinstance(self.tokenizer  ,  lowerCAmelCase_				) else self.tokenizer

		A_		:    str						     = encode_line(lowerCAmelCase_  ,  lowerCAmelCase_  ,  self.max_source_length  ,  """right"""				)
		A_		:    Optional[Any]						     = encode_line(lowerCAmelCase_  ,  lowerCAmelCase_  ,  self.max_target_length  ,  """right"""				)

		A_		:    int						     = source_inputs["""input_ids"""].squeeze()
		A_		:    int						     = target_inputs["""input_ids"""].squeeze()
		A_		:    Tuple						     = source_inputs["""attention_mask"""].squeeze()
		return {
		    "input_ids": source_ids,
		    "attention_mask": src_mask,
		    "decoder_input_ids": target_ids,
		}





	@staticmethod
	def  lowerCamelCase(lowerCAmelCase_				):
		return [len(lowerCAmelCase_				) for x in Path(lowerCAmelCase_				).open().readlines()]





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[str]						     = torch.stack([x["""input_ids"""] for x in batch]				)
		A_		:    Optional[int]						     = torch.stack([x["""attention_mask"""] for x in batch]				)
		A_		:    Any						     = torch.stack([x["""decoder_input_ids"""] for x in batch]				)
		A_		:    Optional[Any]						     = (
		    self.tokenizer.generator.pad_token_id
		    if isinstance(self.tokenizer  ,  lowerCAmelCase_				)
		    else self.tokenizer.pad_token_id
		)
		A_		:    int						     = (
		    self.tokenizer.question_encoder.pad_token_id
		    if isinstance(self.tokenizer  ,  lowerCAmelCase_				)
		    else self.tokenizer.pad_token_id
		)
		A_		:    List[str]						     = trim_batch(lowerCAmelCase_  ,  lowerCAmelCase_				)
		A_     ,  A_		:    Dict						     = trim_batch(lowerCAmelCase_  ,  lowerCAmelCase_  ,  attention_mask=lowerCAmelCase_				)
		A_		:    Optional[Any]						     = {
		    """input_ids""": source_ids,
		    """attention_mask""": source_mask,
		    """decoder_input_ids""": y,
		}
		return batch






_lowerCAmelCase       =				getLogger(__name__)






def       __UpperCamelCase						(		snake_case__       ):
	return list(itertools.chain.from_iterable(snake_case__       )       )






def       __UpperCamelCase						(		snake_case__       ):
	A_		:    List[str]						     = get_git_info()
	save_json(snake_case__ , os.path.join(snake_case__ , """git_log.json"""       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__=4 , **snake_case__       ):
	with open(snake_case__ , """w"""       ) as f:
		json.dump(snake_case__ , snake_case__ , indent=snake_case__ , **snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):
	with open(snake_case__       ) as f:
		return json.load(snake_case__       )






def       __UpperCamelCase						(		):
	A_		:    Optional[int]						     = git.Repo(search_parent_directories=snake_case__       )
	A_		:    Union[str, Any]						     = {
	    """repo_id""": str(snake_case__       ),
	    """repo_sha""": str(repo.head.object.hexsha       ),
	    """repo_branch""": str(repo.active_branch       ),
	    """hostname""": str(socket.gethostname()       ),
	}
	return repo_infos






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	return list(map(snake_case__ , snake_case__       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	with open(snake_case__ , """wb"""       ) as f:
		return pickle.dump(snake_case__ , snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):

	def remove_articles(snake_case__       ):
		return re.sub(R"""\b(a|an|the)\b""" , """ """ , snake_case__       )

	def white_space_fix(snake_case__       ):
		return " ".join(text.split()       )

	def remove_punc(snake_case__       ):
		A_		:    Optional[Any]						     = set(string.punctuation       )
		return "".join(ch for ch in text if ch not in exclude       )

	def lower(snake_case__       ):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(snake_case__       )       )       )       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    Tuple						     = normalize_answer(snake_case__       ).split()
	A_		:    Dict						     = normalize_answer(snake_case__       ).split()
	A_		:    int						     = Counter(snake_case__       ) & Counter(snake_case__       )
	A_		:    Dict						     = sum(common.values()       )
	if num_same == 0:
		return 0
	A_		:    str						     = 1.0 * num_same / len(snake_case__       )
	A_		:    Any						     = 1.0 * num_same / len(snake_case__       )
	A_		:    Union[str, Any]						     = (2 * precision * recall) / (precision + recall)
	return fa






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	return normalize_answer(snake_case__       ) == normalize_answer(snake_case__       )






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	assert len(snake_case__       ) == len(snake_case__       )
	A_		:    Optional[Any]						     = 0
	for hypo, pred in zip(snake_case__ , snake_case__       ):
		em += exact_match_score(snake_case__ , snake_case__       )
	if len(snake_case__       ) > 0:
		em /= len(snake_case__       )
	return {"em": em}






def       __UpperCamelCase						(		snake_case__       ):
	return model_prefix.startswith("""rag"""       )






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__       ):
	A_		:    Any						     = {p: p for p in extra_params}
	# T5 models don't have `dropout` param, they have `dropout_rate` instead
	A_		:    List[Any]						     = """dropout_rate"""
	for p in extra_params:
		if getattr(snake_case__ , snake_case__ , snake_case__       ):
			if not hasattr(snake_case__ , snake_case__       ) and not hasattr(snake_case__ , equivalent_param[p]       ):
				logger.info("""config doesn't have a `{}` attribute""".format(snake_case__       )       )
				delattr(snake_case__ , snake_case__       )
				continue
			A_		:    Dict						     = p if hasattr(snake_case__ , snake_case__       ) else equivalent_param[p]
			setattr(snake_case__ , snake_case__ , getattr(snake_case__ , snake_case__       )       )
			delattr(snake_case__ , snake_case__       )
	return hparams, config

