
from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin

@dataclass
class        UpperCamelCase__(    snake_case_	):

	"""simple docstring"""

	_A  =		4_2


class        UpperCamelCase__(    snake_case_						,						snake_case_	):

	"""simple docstring"""

	@register_to_config
	def __init__(      self				:			Tuple							,			snake_case__				:			Any = 16							,			snake_case__				:			List[Any] = 88							,			snake_case__				:			Union[str, Any] = None							,			snake_case__				:			Optional[int] = None							,			snake_case__				:			List[Any] = 1							,			snake_case__				:			Union[str, Any] = 0.0							,			snake_case__				:			Dict = 32							,			snake_case__				:			Union[str, Any] = None							,			snake_case__				:			List[Any] = False							,			snake_case__				:			Union[str, Any] = None							,			snake_case__				:			Any = None							,			snake_case__				:			List[Any] = None							,			snake_case__				:			Optional[Any] = "geglu"							,			snake_case__				:			List[str] = None							,			snake_case__				:			Any = False							,			snake_case__				:			Dict = False							,			snake_case__				:			List[str] = False							,			snake_case__				:			List[str] = "layer_norm"							,			snake_case__				:			List[str] = True							,			):
			"""simple docstring"""





			super().__init__()
			A				=use_linear_projection
			A				=num_attention_heads
			A				=attention_head_dim
			A				=num_attention_heads * attention_head_dim

			# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
			# Define whether input is continuous or discrete depending on configuration
			A				=(in_channels is not None) and (patch_size is None)
			A				=num_vector_embeds is not None
			A				=in_channels is not None and patch_size is not None

			if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
					A				=(
					    f'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
					    ''' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'''
					    ''' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'''
					    ''' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'''
					    ''' would be very nice if you could open a Pull request for the `transformer/config.json` file'''
					)
					deprecate("norm_type!=num_embeds_ada_norm"							,			"1.0.0"							,			UpperCAmelCase__							,			standard_warn=UpperCAmelCase__ )
					A				='''ada_norm'''

			if self.is_input_continuous and self.is_input_vectorized:
					raise ValueError(
					    f'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
					    " sure that either `in_channels` or `num_vector_embeds` is None." )
			elif self.is_input_vectorized and self.is_input_patches:
					raise ValueError(
					    f'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
					    " sure that either `num_vector_embeds` or `num_patches` is None." )
			elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
					raise ValueError(
					    f'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
					    f''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.''' )

			# 2. Define input layers
			if self.is_input_continuous:
					A				=in_channels

					A				=torch.nn.GroupNorm(num_groups=UpperCAmelCase__							,			num_channels=UpperCAmelCase__							,			eps=1E-6							,			affine=UpperCAmelCase__ )
					if use_linear_projection:
							A				=nn.Linear(UpperCAmelCase__							,			UpperCAmelCase__ )
					else:
							A				=nn.Convad(UpperCAmelCase__							,			UpperCAmelCase__							,			kernel_size=1							,			stride=1							,			padding=0 )
			elif self.is_input_vectorized:
					assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
					assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

					A				=sample_size
					A				=sample_size
					A				=num_vector_embeds
					A				=self.height * self.width

					A				=ImagePositionalEmbeddings(
					    num_embed=UpperCAmelCase__							,			embed_dim=UpperCAmelCase__							,			height=self.height							,			width=self.width )
			elif self.is_input_patches:
					assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

					A				=sample_size
					A				=sample_size

					A				=patch_size
					A				=PatchEmbed(
					    height=UpperCAmelCase__							,			width=UpperCAmelCase__							,			patch_size=UpperCAmelCase__							,			in_channels=UpperCAmelCase__							,			embed_dim=UpperCAmelCase__							,			)

			# 3. Define transformers blocks
			A				=nn.ModuleList(
			    [
			        BasicTransformerBlock(
			            UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__							,			dropout=UpperCAmelCase__							,			cross_attention_dim=UpperCAmelCase__							,			activation_fn=UpperCAmelCase__							,			num_embeds_ada_norm=UpperCAmelCase__							,			attention_bias=UpperCAmelCase__							,			only_cross_attention=UpperCAmelCase__							,			upcast_attention=UpperCAmelCase__							,			norm_type=UpperCAmelCase__							,			norm_elementwise_affine=UpperCAmelCase__							,			)
			        for d in range(UpperCAmelCase__ )
			    ] )

			# 4. Define output layers
			A				=in_channels if out_channels is None else out_channels
			if self.is_input_continuous:
					# TODO: should use out_channels for continuous projections
					if use_linear_projection:
							A				=nn.Linear(UpperCAmelCase__							,			UpperCAmelCase__ )
					else:
							A				=nn.Convad(UpperCAmelCase__							,			UpperCAmelCase__							,			kernel_size=1							,			stride=1							,			padding=0 )
			elif self.is_input_vectorized:
					A				=nn.LayerNorm(UpperCAmelCase__ )
					A				=nn.Linear(UpperCAmelCase__							,			self.num_vector_embeds - 1 )
			elif self.is_input_patches:
					A				=nn.LayerNorm(UpperCAmelCase__							,			elementwise_affine=UpperCAmelCase__							,			eps=1E-6 )
					A				=nn.Linear(UpperCAmelCase__							,			2 * inner_dim )
					A				=nn.Linear(UpperCAmelCase__							,			patch_size * patch_size * self.out_channels )







	def        _a					(      self				:			List[Any]							,			snake_case__				:			Tuple							,			snake_case__				:			Optional[int] = None							,			snake_case__				:			Union[str, Any] = None							,			snake_case__				:			str = None							,			snake_case__				:			Tuple = None							,			snake_case__				:			Optional[Any] = None							,			snake_case__				:			int = None							,			snake_case__				:			Optional[Any] = True							,			):
			"""simple docstring"""





			if attention_mask is not None and attention_mask.ndim == 2:
					# assume that mask is expressed as:
					#   (1 = keep,      0 = discard)
					# convert mask into a bias that can be added to attention scores:
					#       (keep = +0,     discard = -10000.0)
					A				=(1 - attention_mask.to(hidden_states.dtype )) * -1_00_00.0
					A				=attention_mask.unsqueeze(1 )

			# convert encoder_attention_mask to a bias the same way we do for attention_mask
			if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
					A				=(1 - encoder_attention_mask.to(hidden_states.dtype )) * -1_00_00.0
					A				=encoder_attention_mask.unsqueeze(1 )

			# 1. Input
			if self.is_input_continuous:
					A				=hidden_states.shape
					A				=hidden_states

					A				=self.norm(UpperCAmelCase__ )
					if not self.use_linear_projection:
							A				=self.proj_in(UpperCAmelCase__ )
							A				=hidden_states.shape[1]
							A				=hidden_states.permute(0							,			2							,			3							,			1 ).reshape(UpperCAmelCase__							,			height * width							,			UpperCAmelCase__ )
					else:
							A				=hidden_states.shape[1]
							A				=hidden_states.permute(0							,			2							,			3							,			1 ).reshape(UpperCAmelCase__							,			height * width							,			UpperCAmelCase__ )
							A				=self.proj_in(UpperCAmelCase__ )
			elif self.is_input_vectorized:
					A				=self.latent_image_embedding(UpperCAmelCase__ )
			elif self.is_input_patches:
					A				=self.pos_embed(UpperCAmelCase__ )

			# 2. Blocks
			for block in self.transformer_blocks:
					A				=block(
					    UpperCAmelCase__							,			attention_mask=UpperCAmelCase__							,			encoder_hidden_states=UpperCAmelCase__							,			encoder_attention_mask=UpperCAmelCase__							,			timestep=UpperCAmelCase__							,			cross_attention_kwargs=UpperCAmelCase__							,			class_labels=UpperCAmelCase__							,			)

			# 3. Output
			if self.is_input_continuous:
					if not self.use_linear_projection:
							A				=hidden_states.reshape(UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__ ).permute(0							,			3							,			1							,			2 ).contiguous()
							A				=self.proj_out(UpperCAmelCase__ )
					else:
							A				=self.proj_out(UpperCAmelCase__ )
							A				=hidden_states.reshape(UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__							,			UpperCAmelCase__ ).permute(0							,			3							,			1							,			2 ).contiguous()

					A				=hidden_states + residual
			elif self.is_input_vectorized:
					A				=self.norm_out(UpperCAmelCase__ )
					A				=self.out(UpperCAmelCase__ )
					# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
					A				=logits.permute(0							,			2							,			1 )

					# log(p(x_0))
					A				=F.log_softmax(logits.double()							,			dim=1 ).float()
			elif self.is_input_patches:
					# TODO: cleanup!
					A				=self.transformer_blocks[0].norma.emb(
					    UpperCAmelCase__							,			UpperCAmelCase__							,			hidden_dtype=hidden_states.dtype )
					A				=self.proj_out_a(F.silu(UpperCAmelCase__ ) ).chunk(2							,			dim=1 )
					A				=self.norm_out(UpperCAmelCase__ ) * (1 + scale[:, None]) + shift[:, None]
					A				=self.proj_out_a(UpperCAmelCase__ )

					# unpatchify
					A				=int(hidden_states.shape[1] ** 0.5 )
					A				=hidden_states.reshape(
					    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) )
					A				=torch.einsum("nhwpqc->nchpwq"							,			UpperCAmelCase__ )
					A				=hidden_states.reshape(
					    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) )

			if not return_dict:
					return (output,)

			return TransformeraDModelOutput(sample=UpperCAmelCase__ )











'''simple docstring'''






import argparse
import json
import re
import string
import sys
from collections import Counter





def      lowercase  (  lowerCAmelCase :						int):



				"""simple docstring"""


				def remove_articles(lowerCAmelCase :						List[Any]):
								return re.sub(R'''\b(a|an|the)\b'''   , ''' '''   , lowerCAmelCase)

				def white_space_fix(lowerCAmelCase :						Tuple):
								return " ".join(text.split())

				def remove_punc(lowerCAmelCase :						str):
								_A :				List[Any]				=  set(string.punctuation)
								return "".join(ch for ch in text if ch not in exclude)

				def lower(lowerCAmelCase :						Any):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase))))





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Dict):



				"""simple docstring"""


				_A :				str				=  normalize_answer(lowerCAmelCase).split()
				_A :				Dict				=  normalize_answer(lowerCAmelCase).split()
				_A :				str				=  Counter(lowerCAmelCase) & Counter(lowerCAmelCase)
				_A :				List[Any]				=  sum(common.values())
				if num_same == 0:
								return 0
				_A :				Tuple				=  1.0 * num_same / len(lowerCAmelCase)
				_A :				List[Any]				=  1.0 * num_same / len(lowerCAmelCase)
				_A :				int				=  (2 * precision * recall) / (precision + recall)
				return fa





def      lowercase  (  lowerCAmelCase :						Any   , lowerCAmelCase :						str):



				"""simple docstring"""


				return normalize_answer(lowerCAmelCase) == normalize_answer(lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				_A :				Optional[int]				=  []
				for ground_truth in ground_truths:
								_A :				Union[str, Any]				=  metric_fn(lowerCAmelCase   , lowerCAmelCase)
								scores_for_ground_truths.append(lowerCAmelCase)
				return max(lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						str):



				"""simple docstring"""


				_A :				Union[str, Any]				=  0
				_A :				List[Any]				=  []
				for passage in dataset:
								for qa in passage["qas"]:
												total += 1
												if qa["id"] not in predictions:
																_A :				str				=  f"""Unanswered question {qa['id']} will receive score 0."""
																print(lowerCAmelCase   , file=sys.stderr)
																continue

												_A :				Optional[int]				=  [x['''text'''] for x in qa['''answers''']]
												_A :				Union[str, Any]				=  predictions[qa['''id''']]

												_A :				Optional[Any]				=  metric_max_over_ground_truths(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)
												if int(_exact_match) == 1:
																correct_ids.append(qa['''id'''])
												exact_match += _exact_match

												fa += metric_max_over_ground_truths(lowerCAmelCase   , lowerCAmelCase   , lowerCAmelCase)

				_A :				Dict				=  exact_match / total
				_A :				Dict				=  fa / total

				return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
	__UpperCamelCase      :   List[str]								=     '''1.0'''
	__UpperCamelCase      :   Union[str, Any]								=     argparse.ArgumentParser('''Official evaluation script for ReCoRD v1.0.''')
	parser.add_argument('''data_file''', help='''The dataset file in JSON format.''')
	parser.add_argument('''pred_file''', help='''The model prediction file in JSON format.''')
	parser.add_argument('''--output_correct_ids''', action='''store_true''', help='''Output the correctly answered query IDs.''')
	__UpperCamelCase      :   Optional[int]								=     parser.parse_args()

	with open(args.data_file) as data_file:
		__UpperCamelCase      :   Optional[int]								=     json.load(data_file)
		if dataset_json["version"] != expected_version:
			print(
			    f'Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}',
			    file=sys.stderr,
			)
		__UpperCamelCase      :   Optional[Any]								=     dataset_json['''data''']

	with open(args.pred_file) as pred_file:
		__UpperCamelCase      :   int								=     json.load(pred_file)

	__UpperCamelCase							,  __UpperCamelCase      :   List[str]								=     evaluate(dataset, predictions)

	if args.output_correct_ids:
		print(f'Output {len(correct_ids)} correctly answered question IDs.')
		with open('''correct_ids.json''', '''w''') as f:
			json.dump(correct_ids, f)
